diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 451317c8..1ecbe2da 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -11,6 +11,7 @@ repos:
     rev: v6.0.0
     hooks:
       - id: check-added-large-files
+        # No longer bundling RULER corpus (moved to local: bring-your-own model).
       - id: check-toml
       - id: check-yaml
         args:
diff --git a/examples/README.md b/examples/README.md
index d9abaf71..7979fe0b 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -11,6 +11,7 @@ matches what you're trying to do, copy it, edit the marked fields, and run
 | [quickstart.yaml](quickstart.yaml) | Single task + single model + 5 samples — smoke test your install |
 | [leaderboard-math-sft.yaml](leaderboard-math-sft.yaml) | Math SFT leaderboard — multiple math tasks against one or more models |
 | [infer-recipe-override.yaml](infer-recipe-override.yaml) | Pin a specific inference recipe or override engine args |
+| [ruler-qwen3-8b-nonthinking.yaml](ruler-qwen3-8b-nonthinking.yaml) | RULER long-context sweep — 13 subtasks × multiple lengths (4k/8k/16k/32k); see the `-withyarn-64k`/`-128k` and `-thinking` variants alongside it |
 
 ## Hardware-indexed (reference configs)
 
diff --git a/examples/ruler-qwen3-8b-nonthinking.yaml b/examples/ruler-qwen3-8b-nonthinking.yaml
new file mode 100644
index 00000000..d3584e1e
--- /dev/null
+++ b/examples/ruler-qwen3-8b-nonthinking.yaml
@@ -0,0 +1,192 @@
+# ==============================================================================
+# RULER Long-Context Evaluation — Qwen3-8B, Non-Thinking Baseline
+# ==============================================================================
+# Scenario: RULER across a 4K–128K context sweep with thinking disabled. Six
+#   tasks (one per length) served by two configs (native / YaRN).
+# Task: RULER (0-shot, generative).
+#
+# Configuration highlights:
+#   - enable_thinking: false (no reasoning chain)
+#   - Assistant-prefill mode: continue_final_message=true + add_generation_prompt=false
+#   - Datasets: ruler_4k / 8k / 16k / 32k / 64k / 128k (subtask: all, 500 samples each)
+#   - Models:
+#       qwen3-8b       — native 32768 context, serves 4K / 8K / 16K / 32K
+#       qwen3-8b-yarn  — YaRN-scaled to 131072, serves 64K / 128K
+#     (YaRN is enabled only for 64K/128K, which exceed the native 32768 window)
+#   - Temperature / top_p / top_k: 0.7 / 0.8 / 20 ; presence_penalty: 1.5
+#   - concurrency_limit: 64 (native) / 32 (YaRN)
+#   - GPU: H200-141G (native) / L40 (YaRN)
+#
+# Setup steps:
+#   1. Prepare data: sieval dataset download ruler
+#      (or point $SIEVAL_DATA_DIR at an existing RULER staging dir)
+#   2. Point checkpoints / tokenizers at your local model (fields marked # EDIT ME)
+#   3. Run evaluation:
+#      sieval run  ruler-qwen3-8b-nonthinking.yaml   (launch + eval)
+#      sieval eval ruler-qwen3-8b-nonthinking.yaml   (model already served)
+#
+# Editable fields (marked # EDIT ME inline):
+#   models.*.infer.checkpoint            — local model path
+#   datasets.ruler_*.args.tokenizer_path — tokenizer path (same as model)
+#
+# Output:
+#   result_dir: ./outputs/ruler_qwen3_8b_nonthinking
+#   Per-cell scores plus per-length and overall RULER means (see report()).
+#
+# Context fit: thinking is disabled, so there is no think budget — each prompt
+#   packs to max_seq_length reserving only the answer, and every length fits its
+#   serving context_length (≤32K on native 32768, 64K/128K on YaRN 131072). If you
+#   change a dataset's max_seq_length or a model's context_length, keep
+#   max_seq_length + answer ≤ context_length.
+# ==============================================================================
+
+result_dir: ./outputs/ruler_qwen3_8b_nonthinking
+models:
+  qwen3-8b:
+    args:
+      concurrency_limit: 64
+      temperature: 0.7
+      top_p: 0.8
+      presence_penalty: 1.5
+      extra_body:
+        enable_thinking: false
+        top_k: 20
+        continue_final_message: true
+        add_generation_prompt: false
+    infer:
+      backend: sglang
+      recipe: qwen3-8b
+      checkpoint: /path/to/qwen3-8b   # EDIT ME
+      overrides:
+        context_length: 32768
+    infer_meta:
+      gpu: H200-141G
+      image: lmsysorg/sglang:latest
+
+  qwen3-8b-yarn:
+    args:
+      concurrency_limit: 32
+      temperature: 0.7
+      top_p: 0.8
+      presence_penalty: 1.5
+      extra_body:
+        enable_thinking: false
+        top_k: 20
+        continue_final_message: true
+        add_generation_prompt: false
+    infer:
+      backend: sglang
+      recipe: qwen3-8b
+      checkpoint: /path/to/qwen3-8b   # EDIT ME
+      overrides:
+        context_length: 131072
+        disable_cuda_graph: true
+        json_model_override_args: '{"rope_scaling": {"rope_type": "yarn", "factor":
+          4, "original_max_position_embeddings": 32768}}'
+    infer_meta:
+      gpu: L40
+      image: lmsysorg/sglang:latest
+
+datasets:
+  ruler_4k:
+    class: RulerDataset
+    path: ${SIEVAL_DATA_DIR}
+    args:
+      subtask: all
+      max_seq_length: 4096
+      num_samples: 500
+      tokenizer_type: hf
+      tokenizer_path: /path/to/qwen3-8b   # EDIT ME
+      enable_thinking: false
+      model_name: qwen3
+
+  ruler_8k:
+    class: RulerDataset
+    path: ${SIEVAL_DATA_DIR}
+    args:
+      subtask: all
+      max_seq_length: 8192
+      num_samples: 500
+      tokenizer_type: hf
+      tokenizer_path: /path/to/qwen3-8b   # EDIT ME
+      enable_thinking: false
+      model_name: qwen3
+
+  ruler_16k:
+    class: RulerDataset
+    path: ${SIEVAL_DATA_DIR}
+    args:
+      subtask: all
+      max_seq_length: 16384
+      num_samples: 500
+      tokenizer_type: hf
+      tokenizer_path: /path/to/qwen3-8b   # EDIT ME
+      enable_thinking: false
+      model_name: qwen3
+
+  ruler_32k:
+    class: RulerDataset
+    path: ${SIEVAL_DATA_DIR}
+    args:
+      subtask: all
+      max_seq_length: 32768
+      num_samples: 500
+      tokenizer_type: hf
+      tokenizer_path: /path/to/qwen3-8b   # EDIT ME
+      enable_thinking: false
+      model_name: qwen3
+
+  ruler_64k:
+    class: RulerDataset
+    path: ${SIEVAL_DATA_DIR}
+    args:
+      subtask: all
+      max_seq_length: 65536
+      num_samples: 500
+      tokenizer_type: hf
+      tokenizer_path: /path/to/qwen3-8b   # EDIT ME
+      enable_thinking: false
+      model_name: qwen3
+
+  ruler_128k:
+    class: RulerDataset
+    path: ${SIEVAL_DATA_DIR}
+    args:
+      subtask: all
+      max_seq_length: 131072
+      num_samples: 500
+      tokenizer_type: hf
+      tokenizer_path: /path/to/qwen3-8b   # EDIT ME
+      enable_thinking: false
+      model_name: qwen3
+
+tasks:
+  ruler_4k:
+    class: RulerZeroShotGenTask
+    dataset: ruler_4k
+    model: qwen3-8b
+
+  ruler_8k:
+    class: RulerZeroShotGenTask
+    dataset: ruler_8k
+    model: qwen3-8b
+
+  ruler_16k:
+    class: RulerZeroShotGenTask
+    dataset: ruler_16k
+    model: qwen3-8b
+
+  ruler_32k:
+    class: RulerZeroShotGenTask
+    dataset: ruler_32k
+    model: qwen3-8b
+
+  ruler_64k:
+    class: RulerZeroShotGenTask
+    dataset: ruler_64k
+    model: qwen3-8b-yarn
+
+  ruler_128k:
+    class: RulerZeroShotGenTask
+    dataset: ruler_128k
+    model: qwen3-8b-yarn
diff --git a/examples/ruler-qwen3-8b-thinking.yaml b/examples/ruler-qwen3-8b-thinking.yaml
new file mode 100644
index 00000000..fce3bdc3
--- /dev/null
+++ b/examples/ruler-qwen3-8b-thinking.yaml
@@ -0,0 +1,207 @@
+# ==============================================================================
+# RULER Long-Context Evaluation — Qwen3-8B, Thinking Enabled
+# ==============================================================================
+# Scenario: RULER across a 4K–128K context sweep with Qwen3 internal thinking
+#   enabled. Six tasks (one per length) served by two configs (native / YaRN).
+# Task: RULER (0-shot, generative + thinking).
+#
+# Configuration highlights:
+#   - enable_thinking: true; thinking_budget: 8192 (Qwen3ThinkingBudgetLogitProcessor)
+#   - Datasets: ruler_4k / 8k / 16k / 32k / 64k / 128k (subtask: all, 500 samples each)
+#   - Models:
+#       qwen3-8b-think       — native 32768 context, serves 4K / 8K
+#       qwen3-8b-think-yarn  — YaRN-scaled to 131072, serves 16K / 32K / 64K / 128K
+#     (16K/32K fit the native window but run under YaRN to match the Qwen3
+#      technical report's RULER setup; see the note above `tasks:`)
+#   - Temperature / top_p / top_k: 0.6 / 0.95 / 20 ; concurrency_limit: 32
+#   - GPU: H200-141G (both configs)
+#
+# Setup steps:
+#   1. Prepare data: sieval dataset download ruler
+#      (enable_thinking and think_budget are baked into each dataset's args below)
+#   2. Point checkpoints / tokenizers at your local model (fields marked # EDIT ME)
+#   3. Run evaluation: sieval run ruler-qwen3-8b-thinking.yaml
+#
+# Editable fields (marked # EDIT ME inline):
+#   models.*.infer.checkpoint            — local model path
+#   datasets.ruler_*.args.tokenizer_path — tokenizer path (same as model)
+#   custom_logit_processor is framework-generated; do not edit by hand
+#
+# Output:
+#   result_dir: ./outputs/ruler_qwen3_8b_thinking
+#   Per-cell scores plus per-length and overall RULER means (see report()).
+#
+# Note: thinking requires reasoning_parser: qwen3 and enable_custom_logit_processor: true.
+#
+# Budget coupling (keep these in sync when editing):
+#   Each dataset sets think_budget: 8192 to match the serving thinking_budget: 8192.
+#   For contexts < 128K the think budget is NOT reserved while packing the prompt
+#   (it is added back at inference), so the prompt fills max_seq_length reserving
+#   only the answer; it still fits because the serving context_length leaves
+#   headroom — input (≤ max_seq_length) + 8192 think + answer. For 128K the think
+#   budget IS reserved while packing (max_seq_length == context_length == 131072),
+#   so no headroom is needed. If you change a dataset's max_seq_length or a model's
+#   context_length, re-check: max_seq_length + think_budget + answer ≤ context_length.
+# ==============================================================================
+
+result_dir: ./outputs/ruler_qwen3_8b_thinking
+models:
+  qwen3-8b-think:
+    args:
+      concurrency_limit: 32
+      temperature: 0.6
+      top_p: 0.95
+      extra_body:
+        enable_thinking: true
+        top_k: 20
+        custom_logit_processor: '{"callable": "80049554000000000000008c2a73676c616e672e7372742e73616d706c696e672e637573746f6d5f6c6f6769745f70726f636573736f72948c215177656e335468696e6b696e674275646765744c6f67697450726f636573736f729493942e"}'
+        custom_params:
+          thinking_budget: 8192
+    infer:
+      backend: sglang
+      recipe: qwen3-8b
+      checkpoint: /path/to/qwen3-8b   # EDIT ME
+      overrides:
+        context_length: 32768
+        reasoning_parser: qwen3
+        enable_custom_logit_processor: true
+    infer_meta:
+      gpu: H200-141G
+      image: lmsysorg/sglang:latest
+
+  qwen3-8b-think-yarn:
+    args:
+      concurrency_limit: 32
+      temperature: 0.6
+      top_p: 0.95
+      extra_body:
+        enable_thinking: true
+        top_k: 20
+        custom_logit_processor: '{"callable": "80049554000000000000008c2a73676c616e672e7372742e73616d706c696e672e637573746f6d5f6c6f6769745f70726f636573736f72948c215177656e335468696e6b696e674275646765744c6f67697450726f636573736f729493942e"}'
+        custom_params:
+          thinking_budget: 8192
+    infer:
+      backend: sglang
+      recipe: qwen3-8b
+      checkpoint: /path/to/qwen3-8b   # EDIT ME
+      overrides: { context_length: 131072, reasoning_parser: qwen3, enable_custom_logit_processor: true, json_model_override_args: "{\"rope_scaling\": {\"rope_type\": \"yarn\", \"factor\": 4, \"original_max_position_embeddings\": 32768}}" }
+    infer_meta:
+      gpu: H200-141G
+      image: lmsysorg/sglang:latest
+
+datasets:
+  ruler_4k:
+    class: RulerDataset
+    path: ${SIEVAL_DATA_DIR}
+    args:
+      subtask: all
+      max_seq_length: 4096
+      num_samples: 500
+      tokenizer_type: hf
+      tokenizer_path: /path/to/qwen3-8b   # EDIT ME
+      enable_thinking: true
+      model_name: qwen3
+      think_budget: 8192
+
+  ruler_8k:
+    class: RulerDataset
+    path: ${SIEVAL_DATA_DIR}
+    args:
+      subtask: all
+      max_seq_length: 8192
+      num_samples: 500
+      tokenizer_type: hf
+      tokenizer_path: /path/to/qwen3-8b   # EDIT ME
+      enable_thinking: true
+      model_name: qwen3
+      think_budget: 8192
+
+  ruler_16k:
+    class: RulerDataset
+    path: ${SIEVAL_DATA_DIR}
+    args:
+      subtask: all
+      max_seq_length: 16384
+      num_samples: 500
+      tokenizer_type: hf
+      tokenizer_path: /path/to/qwen3-8b   # EDIT ME
+      enable_thinking: true
+      model_name: qwen3
+      think_budget: 8192
+
+  ruler_32k:
+    class: RulerDataset
+    path: ${SIEVAL_DATA_DIR}
+    args:
+      subtask: all
+      max_seq_length: 32768
+      num_samples: 500
+      tokenizer_type: hf
+      tokenizer_path: /path/to/qwen3-8b   # EDIT ME
+      enable_thinking: true
+      model_name: qwen3
+      think_budget: 8192
+
+  ruler_64k:
+    class: RulerDataset
+    path: ${SIEVAL_DATA_DIR}
+    args:
+      subtask: all
+      max_seq_length: 65536
+      num_samples: 500
+      tokenizer_type: hf
+      tokenizer_path: /path/to/qwen3-8b   # EDIT ME
+      enable_thinking: true
+      model_name: qwen3
+      think_budget: 8192
+
+  ruler_128k:
+    class: RulerDataset
+    path: ${SIEVAL_DATA_DIR}
+    args:
+      subtask: all
+      max_seq_length: 131072
+      num_samples: 500
+      tokenizer_type: hf
+      tokenizer_path: /path/to/qwen3-8b   # EDIT ME
+      enable_thinking: true
+      model_name: qwen3
+      think_budget: 8192
+
+# Model assignment by context length:
+#   - 4K/8K  → qwen3-8b-think (native 32768 context, no YaRN)
+#   - 16K+   → qwen3-8b-think-yarn (YaRN-scaled to 131072)
+# 16K/32K fit the native window, so YaRN is not strictly required there; they are
+# evaluated under YaRN anyway to match the RULER setup in the Qwen3 technical
+# report (Qwen3 enables YaRN for long-context eval), so these numbers line up with
+# that table. 64K/128K exceed the native window and require YaRN.
+tasks:
+  ruler_4k:
+    class: RulerZeroShotGenTask
+    dataset: ruler_4k
+    model: qwen3-8b-think
+
+  ruler_8k:
+    class: RulerZeroShotGenTask
+    dataset: ruler_8k
+    model: qwen3-8b-think
+
+  ruler_16k:
+    class: RulerZeroShotGenTask
+    dataset: ruler_16k
+    model: qwen3-8b-think-yarn
+
+  ruler_32k:
+    class: RulerZeroShotGenTask
+    dataset: ruler_32k
+    model: qwen3-8b-think-yarn
+
+  ruler_64k:
+    class: RulerZeroShotGenTask
+    dataset: ruler_64k
+    model: qwen3-8b-think-yarn
+
+  ruler_128k:
+    class: RulerZeroShotGenTask
+    dataset: ruler_128k
+    model: qwen3-8b-think-yarn
diff --git a/pdm.lock b/pdm.lock
index 4002a168..7b7710af 100644
--- a/pdm.lock
+++ b/pdm.lock
@@ -2,40 +2,40 @@
 # It is not intended for manual editing.
 
 [metadata]
-groups = ["default", "dev", "drop", "ifbench", "ifeval", "math", "t-eval", "test"]
+groups = ["default", "dev", "drop", "ifbench", "ifeval", "math", "ruler", "ruler-gen", "t-eval", "test"]
 strategy = ["inherit_metadata"]
 lock_version = "4.5.0"
-content_hash = "sha256:aadbe110ed98781ee5c51ae38bd40780304ed26da3c371a0fd3f5ff1fd1256ef"
+content_hash = "sha256:d4cf55b4348dae0179fd21c8b097035c624a3aa97587e4edac4ed11f10c9047b"
 
 [[metadata.targets]]
 requires_python = ">=3.12,<3.15"
 
 [[package]]
 name = "absl-py"
-version = "2.3.1"
-requires_python = ">=3.8"
+version = "2.5.0"
+requires_python = ">=3.10"
 summary = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py."
 groups = ["ifeval"]
 files = [
-    {file = "absl_py-2.3.1-py3-none-any.whl", hash = "sha256:eeecf07f0c2a93ace0772c92e596ace6d3d3996c042b2128459aaae2a76de11d"},
-    {file = "absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9"},
+    {file = "absl_py-2.5.0-py3-none-any.whl", hash = "sha256:0f17b89f2a4eaaedc4f28c622998aa690564b3012a396a4ffad0821007fe03ba"},
+    {file = "absl_py-2.5.0.tar.gz", hash = "sha256:0c996f25c0490700fadabe6351630f6111534fa0ae252cc6d2014ea3b141135f"},
 ]
 
 [[package]]
 name = "aiohappyeyeballs"
-version = "2.6.1"
-requires_python = ">=3.9"
+version = "2.7.1"
+requires_python = ">=3.10"
 summary = "Happy Eyeballs for asyncio"
 groups = ["default"]
 files = [
-    {file = "aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8"},
-    {file = "aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558"},
+    {file = "aiohappyeyeballs-2.7.1-py3-none-any.whl", hash = "sha256:9243213661e29250eb41368e5daa826fc017156c3b8a11440826b2e3ed376472"},
+    {file = "aiohappyeyeballs-2.7.1.tar.gz", hash = "sha256:065665c041c42a5938ed220bdcd7230f22527fbec085e1853d2402c8a3615d9d"},
 ]
 
 [[package]]
 name = "aiohttp"
-version = "3.13.2"
-requires_python = ">=3.9"
+version = "3.14.1"
+requires_python = ">=3.10"
 summary = "Async http client/server framework (asyncio)"
 groups = ["default"]
 dependencies = [
@@ -46,78 +46,93 @@ dependencies = [
     "frozenlist>=1.1.1",
     "multidict<7.0,>=4.5",
     "propcache>=0.2.0",
+    "typing-extensions>=4.4; python_version < \"3.13\"",
     "yarl<2.0,>=1.17.0",
 ]
 files = [
-    {file = "aiohttp-3.13.2-cp312-cp312-macosx_10_13_universal2.whl", hash = "sha256:b1e56bab2e12b2b9ed300218c351ee2a3d8c8fdab5b1ec6193e11a817767e47b"},
-    {file = "aiohttp-3.13.2-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:364e25edaabd3d37b1db1f0cbcee8c73c9a3727bfa262b83e5e4cf3489a2a9dc"},
-    {file = "aiohttp-3.13.2-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:c5c94825f744694c4b8db20b71dba9a257cd2ba8e010a803042123f3a25d50d7"},
-    {file = "aiohttp-3.13.2-cp312-cp312-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:ba2715d842ffa787be87cbfce150d5e88c87a98e0b62e0f5aa489169a393dbbb"},
-    {file = "aiohttp-3.13.2-cp312-cp312-manylinux2014_armv7l.manylinux_2_17_armv7l.manylinux_2_31_armv7l.whl", hash = "sha256:585542825c4bc662221fb257889e011a5aa00f1ae4d75d1d246a5225289183e3"},
-    {file = "aiohttp-3.13.2-cp312-cp312-manylinux2014_ppc64le.manylinux_2_17_ppc64le.manylinux_2_28_ppc64le.whl", hash = "sha256:39d02cb6025fe1aabca329c5632f48c9532a3dabccd859e7e2f110668972331f"},
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-    {file = "aiohttp-3.13.2-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:4356474ad6333e41ccefd39eae869ba15a6c5299c9c01dfdcfdd5c107be4363e"},
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 requires_python = ">=3.8"
 summary = "Document parameters, class attributes, return types, and variables inline, with Annotated."
-groups = ["default"]
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     "huggingface-hub<2.0,>=0.25.0",
-    "multiprocess<0.70.19",
+    "multiprocess<0.70.20",
     "numpy>=1.17",
     "packaging",
     "pandas",
@@ -507,29 +631,40 @@ dependencies = [
     "xxhash",
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+]
+
+[[package]]
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+version = "0.7.1"
+requires_python = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+summary = "XML bomb protection for Python stdlib modules"
+groups = ["ifbench", "ifeval", "ruler"]
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-version = "0.4.0"
-requires_python = ">=3.8"
+version = "0.4.1"
+requires_python = ">=3.9"
 summary = "serialize all of Python"
 groups = ["default"]
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-version = "0.4.0"
+version = "0.4.3"
 summary = "Distribution utilities"
 groups = ["dev"]
 files = [
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@@ -556,13 +691,13 @@ files = [
 
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+version = "3.30.0"
 requires_python = ">=3.10"
 summary = "A platform independent file lock."
-groups = ["default", "dev", "t-eval"]
+groups = ["default", "dev", "ruler", "t-eval"]
 files = [
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 ]
 
 [[package]]
@@ -658,29 +793,29 @@ files = [
 
 [[package]]
 name = "fsspec"
-version = "2025.10.0"
-requires_python = ">=3.9"
+version = "2026.4.0"
+requires_python = ">=3.10"
 summary = "File-system specification"
-groups = ["default", "t-eval"]
+groups = ["default", "ruler", "t-eval"]
 files = [
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 ]
 
 [[package]]
 name = "fsspec"
-version = "2025.10.0"
+version = "2026.4.0"
 extras = ["http"]
-requires_python = ">=3.9"
+requires_python = ">=3.10"
 summary = "File-system specification"
 groups = ["default"]
 dependencies = [
     "aiohttp!=4.0.0a0,!=4.0.0a1",
-    "fsspec==2025.10.0",
+    "fsspec==2026.4.0",
 ]
 files = [
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 [[package]]
@@ -688,7 +823,7 @@ name = "h11"
 version = "0.16.0"
 requires_python = ">=3.8"
 summary = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
-groups = ["default"]
+groups = ["default", "ruler", "t-eval"]
 files = [
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@@ -696,34 +831,48 @@ files = [
 
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 requires_python = ">=3.8"
 summary = "Fast transfer of large files with the Hugging Face Hub."
-groups = ["default", "t-eval"]
-marker = "platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"arm64\" or platform_machine == \"aarch64\""
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+]
+
+[[package]]
+name = "html2text"
+version = "2025.4.15"
+requires_python = ">=3.9"
+summary = "Turn HTML into equivalent Markdown-structured text."
+groups = ["ruler-gen"]
+files = [
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 [[package]]
@@ -731,7 +880,7 @@ name = "httpcore"
 version = "1.0.9"
 requires_python = ">=3.8"
 summary = "A minimal low-level HTTP client."
-groups = ["default"]
+groups = ["default", "ruler", "t-eval"]
 dependencies = [
     "certifi",
     "h11>=0.16",
@@ -746,7 +895,7 @@ name = "httpx"
 version = "0.28.1"
 requires_python = ">=3.8"
 summary = "The next generation HTTP client."
-groups = ["default"]
+groups = ["default", "ruler", "t-eval"]
 dependencies = [
     "anyio",
     "certifi",
@@ -760,56 +909,57 @@ files = [
 
 [[package]]
 name = "huggingface-hub"
-version = "0.36.2"
-requires_python = ">=3.8.0"
+version = "1.23.0"
+requires_python = ">=3.10.0"
 summary = "Client library to download and publish models, datasets and other repos on the huggingface.co hub"
-groups = ["default", "t-eval"]
+groups = ["default", "ruler", "t-eval"]
 dependencies = [
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+    "filelock>=3.10.0",
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-    "hf-xet<2.0.0,>=1.1.3; platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"arm64\" or platform_machine == \"aarch64\"",
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+    "httpx<1,>=0.23.0",
     "packaging>=20.9",
     "pyyaml>=5.1",
-    "requests",
     "tqdm>=4.42.1",
-    "typing-extensions>=3.7.4.3",
+    "typing-extensions>=4.1.0",
 ]
 files = [
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+version = "3.6.0"
 requires_python = ">=3.10"
 summary = "mutation testing for Python 3"
 groups = ["test"]
@@ -1393,44 +1554,57 @@ dependencies = [
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-version = "9.0.2"
+version = "9.1.1"
 requires_python = ">=3.10"
 summary = "pytest: simple powerful testing with Python"
 groups = ["test"]
@@ -2262,13 +2387,13 @@ dependencies = [
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-version = "7.0.0"
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 requires_python = ">=3.9"
 summary = "Pytest plugin for measuring coverage."
 groups = ["test"]
@@ -2278,8 +2403,8 @@ dependencies = [
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+[[package]]
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+requires_python = ">=3.8"
+summary = "Python interpreter discovery"
+groups = ["dev"]
+dependencies = [
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+    "platformdirs<5,>=4.3.6",
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+
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 name = "pytz"
-version = "2025.2"
+version = "2026.2"
 summary = "World timezone definitions, modern and historical"
 groups = ["default"]
 files = [
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 [[package]]
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 version = "6.0.3"
 requires_python = ">=3.8"
 summary = "YAML parser and emitter for Python"
-groups = ["default", "dev", "t-eval", "test"]
+groups = ["default", "dev", "ruler", "t-eval", "test"]
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 requires_python = ">=3.7"
 summary = "Tool to Detect Surrounding Shell"
-groups = ["default"]
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+groups = ["ruler-gen"]
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 version = "0.7.2"
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+version = "8.2.8"
 requires_python = "<4.0,>=3.9"
 summary = "Modern Text User Interface framework"
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 requires_python = ">=3.9"
 summary = ""
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 dependencies = [
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 [[package]]
 name = "yarl"
-version = "1.22.0"
-requires_python = ">=3.9"
+version = "1.24.2"
+requires_python = ">=3.10"
 summary = "Yet another URL library"
 groups = ["default"]
 dependencies = [
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     "propcache>=0.2.1",
 ]
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+    {file = "yarl-1.24.2-cp314-cp314t-musllinux_1_2_aarch64.whl", hash = "sha256:86746bef442aa479107fe28132e1277237f9c24c2f00b0b0cf22b3ee0904f2bb"},
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+    {file = "yarl-1.24.2-cp314-cp314t-win_arm64.whl", hash = "sha256:e434a45ce2e7a947f951fc5a8944c8cc080b7e59f9c50ae80fd39107cf88126d"},
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+    {file = "yarl-1.24.2.tar.gz", hash = "sha256:9ac374123c6fd7abf64d1fec93962b0bd4ee2c19751755a762a72dd96c0378f8"},
 ]
diff --git a/pyproject.toml b/pyproject.toml
index ad53d496..37eea8ec 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -51,6 +51,26 @@ math = [
   "math-verify[antlr4-11-0]==0.8.0",
   "regex>=2024.0.0",
 ]
+# ruler: benchmark eval + loader deps (what `sieval` needs to run RULER).
+ruler = [
+  "tiktoken>=0.8.0",
+  "wonderwords>=2.2.0,<3",
+  "numpy<=2.2",
+  "scipy>=1.16.3",
+  "nltk>=3.9.2",
+  # tokenizer_type: hf (both example configs) → transformers.AutoTokenizer in community/ruler/scripts/tokenizer.py
+  "transformers>=4.44.0",
+]
+# ruler-gen: deps for the BYO corpus regeneration script only
+# (scripts/gen_paul_graham_essays.py). Not needed to run the benchmark, so kept
+# out of `ruler`; install with `pdm install -G ruler-gen` before regenerating.
+ruler-gen = [
+  # Upper-bounded: html2text renamed/removed converter attrs across majors
+  # (e.g. escape_all → escape_snob), so cap to avoid silent API drift.
+  "html2text>=2020.1.16,<2026",
+  "beautifulsoup4>=4.12.0",
+  "certifi>=2024.7.4",
+]
 t-eval = ["numpy<=2.2", "sentence-transformers>=5.1.2"]
 
 [project.scripts]
@@ -124,7 +144,7 @@ select = [
 ] # community/ follows upstream implementations — do not enforce line-length or print restrictions
 "scripts/**/*.py" = [
   "T201",
-] # scripts/ are CLI tools — print() for progress output
+] # scripts/ are CLI tools — print() is their output mechanism
 "tests/**/*.py" = ["T201"] # tests/ may use print() for debugging
 
 [tool.ty.environment]
@@ -151,6 +171,27 @@ include = ["sieval/tasks/t_eval_before_calling_0shot_gen.py"]
 [tool.ty.overrides.rules]
 unresolved-import = "ignore"
 
+# ruler: wonderwords, tiktoken; not in CI light env. Tests import tiktoken behind try/except.
+[[tool.ty.overrides]]
+include = [
+  "sieval/datasets/ruler/_niah.py",
+  "sieval/datasets/ruler/_cwe.py",
+  "tests/unit/datasets/test_ruler.py",
+  "tests/unit/datasets/test_ruler_shared.py",
+]
+
+[tool.ty.overrides.rules]
+unresolved-import = "ignore"
+
+# gen_paul_graham_essays.py: html2text, beautifulsoup4, certifi live in the
+# optional `ruler-gen` group (not installed by default / in CI), so ty can't
+# resolve them here. Install with `pdm install -G ruler-gen` to run the script.
+[[tool.ty.overrides]]
+include = ["scripts/gen_paul_graham_essays.py"]
+
+[tool.ty.overrides.rules]
+unresolved-import = "ignore"
+
 [tool.coverage.run]
 source = ["sieval/core"]
 branch = true
diff --git a/scripts/gen_paul_graham_essays.py b/scripts/gen_paul_graham_essays.py
new file mode 100644
index 00000000..ab2c4c77
--- /dev/null
+++ b/scripts/gen_paul_graham_essays.py
@@ -0,0 +1,182 @@
+#!/usr/bin/env python
+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License
+"""Regenerate RULER's Paul Graham Essays haystack corpus (bring-your-own).
+
+Adapted from NVIDIA RULER's ``scripts/data/synthetic/json/
+download_paulgraham_essay.py`` (Apache-2.0). Fetches ~218 essays (paulgraham.com
+HTML + gkamradt's needle-haystack repo text), concatenates them into a single
+``{"text": ...}`` document, and writes it *directly* into the data dir at
+``<data-dir>/ruler/PaulGrahamEssays.json.gz`` — exactly where the RULER loader
+reads it.
+
+The essay text is not redistributable (Apache-2.0 covers NVIDIA's scraper, not
+Paul Graham's essays), so it is never bundled in the package or committed. The
+``local:`` source scheme treats it as bring-your-own: run this script to produce
+the file. The URL list is pinned to a RULER commit SHA (not ``main``) so re-runs
+are reproducible against a fixed essay set. This is a regeneration tool; its
+html2text/beautifulsoup4/tqdm deps are intentionally NOT part of sieval's runtime
+dependency graph (this file lives under ``scripts/`` and is never imported by
+``sieval/``).
+
+Usage:
+    pdm run python scripts/gen_paul_graham_essays.py --data-dir "$SIEVAL_DATA_DIR"
+    # or, with SIEVAL_DATA_DIR exported:
+    pdm run python scripts/gen_paul_graham_essays.py
+
+AI-Generated Code - Claude Opus 4.8 (1M context) (Anthropic)
+"""
+
+import argparse
+import gzip
+import json
+import os
+import ssl
+import time
+import urllib.request
+from pathlib import Path
+
+import certifi
+import html2text
+from bs4 import BeautifulSoup
+from tqdm import tqdm
+
+# Per-request timeout (seconds) and retry budget — paulgraham.com occasionally
+# stalls; without a timeout a single hung socket wedges the whole run.
+_TIMEOUT = 30.0
+# paulgraham.com intermittently serves an incomplete TLS chain (missing
+# intermediate), so a given essay randomly hits CERTIFICATE_VERIFY_FAILED on
+# some attempts and succeeds on others. Generous retries make a full 218/218
+# fetch reliable; the run aborts rather than write a partial corpus.
+_RETRIES = 6
+
+# Verify TLS against certifi's CA bundle explicitly. Some interpreters (system
+# python, conda) point ssl at a missing/empty cert store, so the default context
+# fails with CERTIFICATE_VERIFY_FAILED — pinning certifi makes the script work
+# regardless of which python runs it.
+_SSL_CTX = ssl.create_default_context(cafile=certifi.where())
+
+# Pinned RULER commit that owns the URL list (2024-04-29) — keeps the essay set
+# fixed across regenerations.
+_RULER_SHA = "041a952ca058bc90f75f25bb92f32aa4144202ba"
+_URL_LIST = (
+    f"https://raw.githubusercontent.com/NVIDIA/RULER/{_RULER_SHA}/"
+    "scripts/data/synthetic/json/PaulGrahamEssays_URLs.txt"
+)
+
+# The corpus is staged under ``<data-dir>/ruler/`` (the RULER dataset name), the
+# same layout the ``local:`` handler reports and the loader reads via `gzip.open`.
+_CORPUS_RELPATH = Path("ruler") / "PaulGrahamEssays.json.gz"
+
+
+def _resolve_out_path(data_dir: str | None) -> Path:
+    data_dir = data_dir or os.environ.get("SIEVAL_DATA_DIR")
+    if not data_dir:
+        raise SystemExit(
+            "no data dir: pass --data-dir <path> or export SIEVAL_DATA_DIR; "
+            "the corpus is written to <data-dir>/ruler/PaulGrahamEssays.json.gz"
+        )
+    return Path(data_dir).expanduser() / _CORPUS_RELPATH
+
+
+def _html_to_text(content: str, converter: html2text.HTML2Text) -> str:
+    soup = BeautifulSoup(content, "html.parser")
+    specific_tag = soup.find("font")
+    return converter.handle(str(specific_tag))
+
+
+def _fetch(url: str) -> bytes:
+    """GET *url* with a per-request timeout and bounded retries."""
+    last_exc: Exception | None = None
+    for attempt in range(_RETRIES):
+        try:
+            with urllib.request.urlopen(
+                url, timeout=_TIMEOUT, context=_SSL_CTX
+            ) as resp:
+                return resp.read()
+        except Exception as e:  # noqa: BLE001 — retry any transport error
+            last_exc = e
+            time.sleep(2**attempt)
+    assert last_exc is not None
+    raise last_exc
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(description="Regenerate RULER's PG corpus.")
+    parser.add_argument(
+        "--data-dir",
+        default=None,
+        help="Data dir root (defaults to $SIEVAL_DATA_DIR); the corpus is written "
+        "to <data-dir>/ruler/PaulGrahamEssays.json.gz",
+    )
+    args = parser.parse_args()
+    out_path = _resolve_out_path(args.data_dir)
+
+    converter = html2text.HTML2Text()
+    converter.ignore_images = True
+    converter.ignore_tables = True
+    # escape_snob escapes all markdown-special chars (html2text renamed the old
+    # `escape_all`); links stay inline by default, so no reference_links toggle.
+    converter.escape_snob = True
+    converter.mark_code = False
+
+    urls = [line.strip() for line in _fetch(_URL_LIST).decode("utf-8").splitlines()]
+    urls = [u for u in urls if u]
+
+    essays: list[str] = []
+    failed: list[str] = []
+    for url in tqdm(urls, desc="essays"):
+        try:
+            raw = _fetch(url)
+            if ".html" in url:
+                # Mirror RULER's exact per-essay decode: `unicode_escape`
+                # here is faithful to the original download_paulgraham_essay.py,
+                # not an oversight. (Full-corpus bytes still differ from upstream:
+                # this script concatenates essays in the pinned URL-list order,
+                # while upstream groups them repo-then-html by folder — glob order
+                # within each group. Score-neutral, enumerated in
+                # RulerZeroShotGenTask.reference_impl.notes.)
+                parsed = _html_to_text(raw.decode("unicode_escape", "utf-8"), converter)
+            else:
+                parsed = raw.decode("utf-8")
+        except Exception as e:  # noqa: BLE001 — best-effort, record and skip
+            print(f"Fail download {url} ({e})")
+            failed.append(url)
+            continue
+        essays.append(parsed)
+
+    # A partial corpus would silently change the haystack bytes (and break
+    # reproducibility against RULER), so refuse to write an incomplete file.
+    if failed:
+        raise SystemExit(
+            f"{len(failed)}/{len(urls)} essays failed to download; "
+            f"refusing to write a partial corpus. Failed: {failed}"
+        )
+
+    text = "".join(essays)
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+    # mtime=0 so the gzip header is byte-stable across regenerations — the same
+    # essay set always yields identical bytes regardless of when it was run.
+    with gzip.GzipFile(out_path, "wb", mtime=0) as gz:
+        gz.write(json.dumps({"text": text}, ensure_ascii=False).encode("utf-8"))
+
+    size = out_path.stat().st_size
+    print(
+        f"Wrote {len(essays)}/{len(urls)} essays "
+        f"({size / 1_000_000:.1f} MB) -> {out_path}"
+    )
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/sync_meta_index.py b/scripts/sync_meta_index.py
index 46227ce6..ccb1d847 100644
--- a/scripts/sync_meta_index.py
+++ b/scripts/sync_meta_index.py
@@ -69,7 +69,7 @@ def main() -> int:
     os.replace(tmp, INDEX_PATH)
     payload = json.loads(rendered)
     relative = INDEX_PATH.relative_to(ROOT)
-    print(  # noqa: T201
+    print(
         f"Wrote {len(payload['datasets'])} dataset and "
         f"{len(payload['tasks'])} task entries to {relative}"
     )
diff --git a/scripts/sync_package_stubs.py b/scripts/sync_package_stubs.py
index 93930cf7..7fbda977 100644
--- a/scripts/sync_package_stubs.py
+++ b/scripts/sync_package_stubs.py
@@ -110,11 +110,15 @@ def discover_subpackage_tasks(subpkg_dir: Path) -> dict[str, str]:
     return _discover_task_classes(_iter_module_paths(subpkg_dir))
 
 
-def discover_datasets(package_dir: Path) -> dict[str, str]:
+def _discover_dataset_classes(module_paths: list[Path]) -> dict[str, str]:
+    """Scan *module_paths* for public Dataset/DatasetSample/CSVSample class definitions.
+
+    Returns ``{ClassName: module_stem}`` mapping.
+    """
     export_to_module: dict[str, str] = {}
     suffixes = ("Dataset", "DatasetSample", "CSVSample")
 
-    for module_path in _iter_module_paths(package_dir):
+    for module_path in module_paths:
         module_name = module_path.stem
         module_ast = ast.parse(
             module_path.read_text(encoding="utf-8"),
@@ -143,13 +147,26 @@ def discover_datasets(package_dir: Path) -> dict[str, str]:
             if export_name is None:
                 continue
 
-            previous_module = export_to_module.get(export_name)
-            if previous_module and previous_module != module_name:
-                raise RuntimeError(
-                    f"Duplicate dataset export '{export_name}' found in "
-                    f"'{previous_module}' and '{module_name}'."
-                )
-            export_to_module[export_name] = module_name
+            _register_export(export_to_module, export_name, module_name, "dataset")
+
+    return export_to_module
+
+
+def discover_datasets(package_dir: Path) -> dict[str, str]:
+    """Discover all dataset exports from flat modules and subpackages."""
+    export_to_module: dict[str, str] = {}
+
+    # 1) Flat .py modules
+    for name, mod in _discover_dataset_classes(_iter_module_paths(package_dir)).items():
+        _register_export(export_to_module, name, mod, "dataset")
+
+    # 2) Subpackage dataset modules — scan .py files inside each subpackage
+    for subpkg_dir in _iter_subpackage_dirs(package_dir):
+        subpkg_name = subpkg_dir.name
+        for name, _mod in _discover_dataset_classes(
+            _iter_module_paths(subpkg_dir)
+        ).items():
+            _register_export(export_to_module, name, subpkg_name, "dataset")
 
     return export_to_module
 
diff --git a/sieval/cli/dataset/commands.py b/sieval/cli/dataset/commands.py
index 5df28e18..5b6bfeee 100644
--- a/sieval/cli/dataset/commands.py
+++ b/sieval/cli/dataset/commands.py
@@ -18,6 +18,7 @@
 from sieval.core.utils.logging import configure_logging
 from sieval.core.utils.paths import resolve_data_dir
 from sieval.datasets.downloaders import resolve as resolve_handler
+from sieval.datasets.downloaders.local import LocalSourceUnavailable
 from sieval.datasets.downloaders.resolver import extras_unsatisfied
 from sieval.datasets.downloaders.verify import verify_checksums
 from sieval.meta import load_index
@@ -180,13 +181,19 @@ def download_cmd(
 
 
 def _download_one(m: DatasetMeta, dest_root: Path, force: bool) -> None:
+    missing_local_sources: list[str] = []
     for src in m.source:
         h = resolve_handler(src)
         if h.is_downloaded(src, dest_root, m.name) and not force:
             typer.echo(f"[{m.name}] already present: {src}")
             continue
         typer.echo(f"[{m.name}] fetching {src}")
-        h.download(src, dest_root, m.name, force=force)
+        try:
+            h.download(src, dest_root, m.name, force=force)
+        except LocalSourceUnavailable as e:
+            # BYO corpus: surface the instructions and track for a summary.
+            typer.secho(f"[{m.name}] {e}", fg=typer.colors.YELLOW)
+            missing_local_sources.append(src)
 
     mismatches = verify_checksums(m, dest_root)
     if mismatches:
@@ -202,6 +209,15 @@ def _download_one(m: DatasetMeta, dest_root: Path, force: bool) -> None:
             f"`sieval dataset download {m.name}` to refetch"
         )
 
+    # BYO corpus: exit non-zero if any local: sources were unavailable.
+    if missing_local_sources:
+        raise RuntimeError(
+            f"{m.name!r} requires {len(missing_local_sources)} bring-your-own "
+            f"corpus file(s). Run `pdm run python "
+            f"scripts/gen_paul_graham_essays.py --data-dir $SIEVAL_DATA_DIR` "
+            f"to produce it, then re-run this download."
+        )
+
     # Post-download hint; print-only, never installs.
     if m.deps_group:
         unmet = extras_unsatisfied(m.deps_group)
diff --git a/sieval/community/ruler/__init__.py b/sieval/community/ruler/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/sieval/community/ruler/datasets/__init__.py b/sieval/community/ruler/datasets/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/sieval/community/ruler/datasets/constants.py b/sieval/community/ruler/datasets/constants.py
new file mode 100644
index 00000000..ac881274
--- /dev/null
+++ b/sieval/community/ruler/datasets/constants.py
@@ -0,0 +1,56 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# adapted from https://github.com/NVIDIA/RULER/blob/ab17b7853df4e0a30b78cd5d2b463ac7dff6ee13/scripts/data/synthetic/constants.py
+
+"""
+Add a new task (required arguments):
+
+TASK_NAME: {
+    'tokens_to_generate': how many tokens we want to generate.
+    'template': the template with at least {context} and {query}.
+}
+"""
+
+TASKS = {
+    'niah': {
+        'tokens_to_generate': 128,
+        'template': """Some special magic {type_needle_v} are hidden within the following text. Make sure to memorize it. I will quiz you about the {type_needle_v} afterwards.\n{context}\nWhat are all the special magic {type_needle_v} for {query} mentioned in the provided text?""",
+        'answer_prefix': """ The special magic {type_needle_v} for {query} mentioned in the provided text are""",
+    },
+    
+    'variable_tracking': {
+        'tokens_to_generate': 30,
+        'template': """Memorize and track the chain(s) of variable assignment hidden in the following text.\n\n{context}\nQuestion: Find all variables that are assigned the value {query} in the text above.""",
+        'answer_prefix': """ Answer: According to the chain(s) of variable assignment in the text above, {num_v} variables are assigned the value {query}, they are: """
+    },
+    
+    'common_words_extraction': {
+        'tokens_to_generate': 120,
+        'template': """Below is a numbered list of words. In these words, some appear more often than others. Memorize the ones that appear most often.\n{context}\nQuestion: What are the 10 most common words in the above list?""",
+        'answer_prefix': """ Answer: The top 10 words that appear most often in the list are:"""
+    },
+    
+    'freq_words_extraction' : {
+        'tokens_to_generate': 50,
+        'template': """Read the following coded text and track the frequency of each coded word. Find the three most frequently appeared coded words. {context}\nQuestion: Do not provide any explanation. Please ignore the dots '....'. What are the three most frequently appeared words in the above coded text?""",
+        'answer_prefix': """ Answer: According to the coded text above, the three most frequently appeared words are:""",
+    },
+
+    'qa': {
+        'tokens_to_generate': 32, 
+        'template': """Answer the question based on the given documents. Only give me the answer and do not output any other words.\n\nThe following are given documents.\n\n{context}\n\nAnswer the question based on the given documents. Only give me the answer and do not output any other words.\n\nQuestion: {query}""",
+        'answer_prefix': """ Answer:""",
+    },
+}
\ No newline at end of file
diff --git a/sieval/community/ruler/datasets/template.py b/sieval/community/ruler/datasets/template.py
new file mode 100644
index 00000000..4208e923
--- /dev/null
+++ b/sieval/community/ruler/datasets/template.py
@@ -0,0 +1,40 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# adapted from https://github.com/NVIDIA/RULER/blob/ab17b7853df4e0a30b78cd5d2b463ac7dff6ee13/scripts/data/template.py
+
+
+Templates = {
+    'base': "{task_template}",
+
+    'meta-chat': "[INST] {task_template} [/INST]",
+
+    'vicuna-chat': "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {task_template} ASSISTANT:",
+
+    'lwm-chat': "You are a helpful assistant. USER: {task_template} ASSISTANT: ",
+
+    'command-r-chat': "<BOS_TOKEN><|START_OF_TURN_TOKEN|><|USER_TOKEN|>{task_template}<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>",
+
+    'chatglm-chat': "[gMASK]sop<|user|> \n {task_template}<|assistant|> \n ",
+    
+    'RWKV': "User: hi\n\nAssistant: Hi. I am your assistant and I will provide expert full response in full details. Please feel free to ask any question and I will always answer it\n\nUser: {task_template}\n\nAssistant:",
+
+    'Phi3': "<|user|>\n{task_template}<|end|>\n<|assistant|>\n",
+
+    'meta-llama3': "<|begin_of_text|><|start_header_id|>user<|end_header_id|>\n\n{task_template}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n",
+    
+    'jamba': "<|startoftext|><|bom|><|system|> <|eom|><|bom|><|user|> {task_template}<|eom|><|bom|><|assistant|>",
+    
+    'nemotron5-instruct': "<SPECIAL_10>System\n\n<SPECIAL_11>User\n{task_template}\n<SPECIAL_11>Assistant\n",
+}
\ No newline at end of file
diff --git a/sieval/community/ruler/eval/__init__.py b/sieval/community/ruler/eval/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/sieval/community/ruler/eval/constants.py b/sieval/community/ruler/eval/constants.py
new file mode 100644
index 00000000..8fb7714d
--- /dev/null
+++ b/sieval/community/ruler/eval/constants.py
@@ -0,0 +1,42 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License
+
+# adapted from https://github.com/NVIDIA/RULER/blob/ab17b7853df4e0a30b78cd5d2b463ac7dff6ee13/scripts/eval/synthetic/constants.py
+
+def string_match_part(preds, refs):
+    score = sum([max([1.0 if r.lower() in pred.lower() else 0.0 for r in ref]) for pred, ref in zip(preds, refs)]) / len(preds) * 100
+    return round(score, 2)
+
+def string_match_all(preds, refs):
+    score = sum([sum([1.0 if r.lower() in pred.lower() else 0.0 for r in ref]) / len(ref) for pred, ref in zip(preds, refs)]) / len(preds) * 100
+    return round(score, 2)
+    
+
+TASKS = {
+    'niah': {
+        'metric_fn': string_match_all,
+    },
+    'variable_tracking': {
+        'metric_fn': string_match_all,
+    },
+    'common_words_extraction': {
+        'metric_fn': string_match_all,
+    },
+    'freq_words_extraction': {
+        'metric_fn': string_match_all
+    },
+    'qa': {
+        'metric_fn': string_match_part,
+    },
+}
diff --git a/sieval/community/ruler/scripts/__init__.py b/sieval/community/ruler/scripts/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/sieval/community/ruler/scripts/tokenizer.py b/sieval/community/ruler/scripts/tokenizer.py
new file mode 100644
index 00000000..7b711bbf
--- /dev/null
+++ b/sieval/community/ruler/scripts/tokenizer.py
@@ -0,0 +1,60 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# adapted from https://github.com/NVIDIA/RULER/blob/ab17b7853df4e0a30b78cd5d2b463ac7dff6ee13/scripts/data/tokenizer.py
+
+import os
+from typing import List
+
+def select_tokenizer(tokenizer_type, tokenizer_path):
+    if tokenizer_type == 'hf':
+        return HFTokenizer(model_path=tokenizer_path)
+    elif tokenizer_type == 'openai':
+        return OpenAITokenizer(model_path=tokenizer_path)
+    else:
+        raise ValueError(f"Unknown tokenizer_type {tokenizer_type}")
+
+
+class HFTokenizer:
+    """
+    Tokenizer from HF models
+    """
+    def __init__(self, model_path) -> None:
+        from transformers import AutoTokenizer
+        self.tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+    
+    def text_to_tokens(self, text: str) -> List[str]:
+        tokens = self.tokenizer.tokenize(text)
+        return tokens
+
+    def tokens_to_text(self, tokens: List[int]) -> str:
+        text = self.tokenizer.convert_tokens_to_string(tokens)
+        return text
+
+
+class OpenAITokenizer:
+    """
+    Tokenizer from tiktoken
+    """
+    def __init__(self, model_path="cl100k_base") -> None:
+        import tiktoken
+        self.tokenizer = tiktoken.get_encoding(model_path)
+
+    def text_to_tokens(self, text: str) -> List[int]:
+        tokens = self.tokenizer.encode(text)
+        return tokens
+
+    def tokens_to_text(self, tokens: List[int]) -> str:
+        text = self.tokenizer.decode(tokens)
+        return text
diff --git a/sieval/core/datasets/meta.py b/sieval/core/datasets/meta.py
index 5568755c..06c823f0 100644
--- a/sieval/core/datasets/meta.py
+++ b/sieval/core/datasets/meta.py
@@ -179,7 +179,7 @@ def _validate(
                 f"{_VALID_SCHEMES} (e.g. 'hf:org/name')"
             )
 
-    _validate_url_basenames_unique(name, source)
+    _validate_staged_basenames_unique(name, source)
 
 
 def url_path_basename(url: str) -> str:
@@ -189,20 +189,27 @@ def url_path_basename(url: str) -> str:
     return urlparse(url).path.rsplit("/", 1)[-1]
 
 
-def _validate_url_basenames_unique(name: str, source: tuple[str, ...]) -> None:
-    """Reject duplicate basenames among url: sources in one dataset — two URLs
-    sharing a basename would overwrite each other at ``<dest>/<name>/<basename>``."""
+_STAGED_SCHEMES = ("url:", "local:")
+
+
+def _validate_staged_basenames_unique(name: str, source: tuple[str, ...]) -> None:
+    """Reject duplicate basenames among sources that stage to a flat file in one
+    dataset — both url: and local: land at ``<dest>/<name>/<basename>``, so two
+    sources (url/url, local/local, or url/local) sharing a basename would
+    silently overwrite each other."""
     basenames = [
-        url_path_basename(src[len("url:") :])
+        url_path_basename(src[len(scheme) :])
         for src in source
-        if src.startswith("url:")
+        for scheme in _STAGED_SCHEMES
+        if src.startswith(scheme)
     ]
     counter = Counter(basenames)
     duplicates = {b for b, count in counter.items() if count > 1}
     if duplicates:
         raise ValueError(
-            f"url: sources in dataset {name!r} have colliding basenames: "
-            f"{sorted(duplicates)}; each URL must produce a unique on-disk filename"
+            f"url:/local: sources in dataset {name!r} have colliding basenames: "
+            f"{sorted(duplicates)}; each staged source must produce a unique "
+            f"on-disk filename"
         )
 
 
diff --git a/sieval/datasets/__init__.py b/sieval/datasets/__init__.py
index 167e5c0c..eda191ad 100644
--- a/sieval/datasets/__init__.py
+++ b/sieval/datasets/__init__.py
@@ -24,6 +24,17 @@ def _iter_module_paths() -> list[Path]:
     )
 
 
+def _iter_subpackage_dirs() -> list[Path]:
+    """Return sorted subdirectories that contain ``__init__.py``."""
+    return sorted(
+        path
+        for path in _PACKAGE_DIR.iterdir()
+        if path.is_dir()
+        and not path.name.startswith("_")
+        and (path / "__init__.py").exists()
+    )
+
+
 def _is_export_name(name: str) -> bool:
     return not name.startswith("_") and name.endswith(_DATASET_EXPORT_SUFFIXES)
 
@@ -42,6 +53,8 @@ def _is_typeddict_call(node: ast.AST) -> bool:
 
 def _discover_dataset_exports() -> dict[str, str]:
     export_to_module: dict[str, str] = {}
+
+    # 1) Scan flat modules
     for module_path in _iter_module_paths():
         module_name = module_path.stem
         module_ast = ast.parse(
@@ -74,6 +87,46 @@ def _discover_dataset_exports() -> dict[str, str]:
                 )
             export_to_module[export_name] = module_name
 
+    # 2) Scan subpackage modules
+    for subpkg_dir in _iter_subpackage_dirs():
+        subpkg_name = subpkg_dir.name
+        for module_path in sorted(
+            p
+            for p in subpkg_dir.iterdir()
+            if p.suffix == ".py"
+            and p.name != "__init__.py"
+            and not p.name.startswith("_")
+        ):
+            module_ast = ast.parse(
+                module_path.read_text(encoding="utf-8"),
+                filename=str(module_path),
+            )
+
+            for node in module_ast.body:
+                export_name: str | None = None
+
+                if isinstance(node, ast.ClassDef) and _is_export_name(node.name):
+                    export_name = node.name
+                elif (
+                    isinstance(node, ast.Assign)
+                    and len(node.targets) == 1
+                    and isinstance(node.targets[0], ast.Name)
+                    and _is_export_name(node.targets[0].id)
+                    and _is_typeddict_call(node.value)
+                ):
+                    export_name = node.targets[0].id
+
+                if export_name is None:
+                    continue
+
+                previous_module = export_to_module.get(export_name)
+                if previous_module and previous_module != subpkg_name:
+                    raise RuntimeError(
+                        f"Duplicate dataset export '{export_name}' found in "
+                        f"'{previous_module}' and '{subpkg_name}'."
+                    )
+                export_to_module[export_name] = subpkg_name
+
     return export_to_module
 
 
diff --git a/sieval/datasets/__init__.pyi b/sieval/datasets/__init__.pyi
index 1973cbc5..3cc9a325 100644
--- a/sieval/datasets/__init__.pyi
+++ b/sieval/datasets/__init__.pyi
@@ -105,6 +105,10 @@ from .openbookqa import (
     OpenBookQADataset,
     OpenBookQADatasetSample,
 )
+from .ruler import (
+    RulerDataset,
+    RulerDatasetSample,
+)
 from .simpleqa_verified import (
     SimpleQAVerifiedDataset,
     SimpleQAVerifiedDatasetSample,
@@ -171,6 +175,8 @@ __all__ = [
     "MMMLUDatasetSample",
     "OpenBookQADataset",
     "OpenBookQADatasetSample",
+    "RulerDataset",
+    "RulerDatasetSample",
     "SimpleQAVerifiedDataset",
     "SimpleQAVerifiedDatasetSample",
     "TEvalBeforeCallingDataset",
diff --git a/sieval/datasets/downloaders/base.py b/sieval/datasets/downloaders/base.py
index d51e424b..51834957 100644
--- a/sieval/datasets/downloaders/base.py
+++ b/sieval/datasets/downloaders/base.py
@@ -1,4 +1,4 @@
-"""SourceHandler Protocol + scheme registry. v0.1 ships ``hf:`` and ``url:``.
+"""SourceHandler Protocol + scheme registry. v0.2 ships ``hf:``, ``url:``, ``local:``.
 
 AI-Generated Code - Claude Haiku 4.5 (Anthropic)
 """
@@ -40,9 +40,10 @@ def _ensure_builtin_handlers() -> None:
     if _builtin_registered:
         return
     from sieval.datasets.downloaders.hf import HFHandler
+    from sieval.datasets.downloaders.local import LocalHandler
     from sieval.datasets.downloaders.url import URLHandler
 
-    for handler in (HFHandler(), URLHandler()):
+    for handler in (HFHandler(), URLHandler(), LocalHandler()):
         if handler.scheme not in _HANDLERS:
             register_handler(handler)
     _builtin_registered = True
diff --git a/sieval/datasets/downloaders/local.py b/sieval/datasets/downloaders/local.py
new file mode 100644
index 00000000..3df42ed1
--- /dev/null
+++ b/sieval/datasets/downloaders/local.py
@@ -0,0 +1,67 @@
+"""local scheme handler: a bring-your-own corpus staged at ``dest_root/<name>/``.
+
+Some datasets depend on a corpus that cannot be redistributed — e.g. RULER's
+Paul Graham essays, where Apache-2.0 covers NVIDIA's scraper but not the essay
+text itself. Such a file is produced out-of-band by a generation script (e.g.
+``scripts/gen_paul_graham_essays.py``) written *directly* into the data dir at
+``{dest_root}/<dataset_name>/``; it is never bundled in the package or committed.
+
+``download`` therefore moves no bytes: if the file is already staged it is a
+no-op, otherwise it raises :class:`LocalSourceUnavailable` with instructions.
+``is_downloaded`` reports actual presence, so ``sieval dataset download`` can
+surface the BYO requirement instead of silently succeeding.
+
+AI-Generated Code - Claude Opus 4.8 (1M context) (Anthropic)
+"""
+
+from pathlib import Path
+
+from sieval.core.datasets.meta import url_path_basename
+
+
+class LocalSourceUnavailable(RuntimeError):
+    """A ``local:`` corpus is required but absent from the data dir (BYO)."""
+
+
+class LocalHandler:
+    scheme = "local"
+
+    def download(
+        self,
+        source: str,
+        dest_root: Path,
+        dataset_name: str,
+        force: bool,
+    ) -> None:
+        relpath = self._strip_scheme(source)
+        target = dest_root / dataset_name / _basename(relpath)
+        # BYO: nothing to fetch. Present → no-op; absent → tell the user how to
+        # produce it (force cannot re-fetch a corpus that was never remote).
+        if target.exists():
+            return
+        raise LocalSourceUnavailable(
+            f"{source} is a bring-your-own corpus and cannot be fetched "
+            f"automatically. Generate or place it at {target} — see the "
+            f"dataset's regeneration script under scripts/."
+        )
+
+    def is_downloaded(
+        self,
+        source: str,
+        dest_root: Path,
+        dataset_name: str,
+    ) -> bool:
+        relpath = self._strip_scheme(source)
+        return (dest_root / dataset_name / _basename(relpath)).exists()
+
+    @staticmethod
+    def _strip_scheme(source: str) -> str:
+        if not source.startswith("local:"):
+            raise ValueError(f"Expected local: scheme, got {source!r}")
+        return source[len("local:") :]
+
+
+def _basename(relpath: str) -> str:
+    """Filename the corpus is staged under; shares the url-handler primitive so
+    the on-disk name matches the ``url:`` convention."""
+    return url_path_basename(relpath) or "download"
diff --git a/sieval/datasets/ruler/__init__.py b/sieval/datasets/ruler/__init__.py
new file mode 100644
index 00000000..6625366c
--- /dev/null
+++ b/sieval/datasets/ruler/__init__.py
@@ -0,0 +1,19 @@
+from ._shared import (
+    RulerTaskSpec,
+    len_tag,
+    ruler_task,
+    thinking_prefill,
+    tokens_to_generate,
+)
+from .ruler import RulerDataset, RulerDatasetSample, _stamp
+
+__all__ = [
+    "RulerDataset",
+    "RulerDatasetSample",
+    "RulerTaskSpec",
+    "len_tag",
+    "_stamp",
+    "ruler_task",
+    "tokens_to_generate",
+    "thinking_prefill",
+]
diff --git a/sieval/datasets/ruler/_cwe.py b/sieval/datasets/ruler/_cwe.py
new file mode 100644
index 00000000..d5755882
--- /dev/null
+++ b/sieval/datasets/ruler/_cwe.py
@@ -0,0 +1,262 @@
+"""Common Words Extraction (CWE) synthesis helpers for RULER."""
+
+import json
+import os
+import random
+
+from sieval.community.ruler.scripts.tokenizer import select_tokenizer
+
+from ._shared import model_template_token, ruler_task, tokens_to_generate
+
+
+def load_cwe(
+    name_or_path: str,
+    *,
+    max_seq_length: int,
+    tokenizer_type: str,
+    tokenizer_path: str,
+    num_samples: int,
+    random_seed: int,
+    remove_newline_tab: bool,
+    enable_thinking: bool,
+    think_budget: int = 0,
+    model_name: str = "qwen3",
+    freq_cw: int,
+    freq_ucw: int,
+    num_cw: int,
+    num_fewshot: int,
+) -> list[dict]:
+    gen_budget = tokens_to_generate(
+        "common_words_extraction",
+        enable_thinking=enable_thinking,
+        think_budget=think_budget,
+        model_name=model_name,
+        context_length=max_seq_length,
+        for_dataset=True,
+    )
+    tokenizer = select_tokenizer(tokenizer_type, tokenizer_path)
+    # Upstream template reserve, computed once (sample-invariant).
+    mtt = model_template_token(
+        tokenizer, model_name=model_name, enable_thinking=enable_thinking
+    )
+
+    random.seed(random_seed)
+
+    words = _word_pool(random_seed)
+
+    randle_words: list[str] = []
+    randle_path = os.path.join(name_or_path, "english_words.json")
+    if os.path.exists(randle_path):
+        with open(randle_path) as f:
+            randle_words = list(json.load(f).values())
+
+    def gen(num_words: int) -> tuple[str, list[str]]:
+        return _generate_input_output(
+            num_words=num_words,
+            words=words,
+            max_seq_length=max_seq_length,
+            freq_cw=freq_cw,
+            freq_ucw=freq_ucw,
+            num_cw=num_cw,
+            random_seed=random_seed,
+            num_fewshot=num_fewshot,
+            randle_words=randle_words,
+        )
+
+    incremental = 10
+    num_words = _binary_search_words(
+        gen=gen,
+        tokenizer=tokenizer,
+        vocab_size=len(words),
+        max_seq_length=max_seq_length,
+        tokens_to_generate=gen_budget,
+        template_token=mtt,
+        incremental=incremental,
+    )
+
+    cwe_answer_prefix = ruler_task("common_words_extraction")["answer_prefix"]
+
+    rows: list[dict] = []
+    for index in range(num_samples):
+        used_words = num_words
+        while True:
+            try:
+                input_text, answer = gen(used_words)
+                length = len(tokenizer.text_to_tokens(input_text)) + mtt + gen_budget
+                assert length <= max_seq_length, "exceeds max_seq_length"
+                break
+            except Exception:
+                if used_words > incremental:
+                    used_words -= incremental
+        if remove_newline_tab:
+            input_text = " ".join(
+                input_text.replace("\n", " ").replace("\t", " ").strip().split()
+            )
+        answer_prefix_index = input_text.rfind(cwe_answer_prefix[:10])
+        answer_prefix = input_text[answer_prefix_index:]
+        input_text = input_text[:answer_prefix_index]
+        rows.append(
+            {
+                "index": index,
+                "input": input_text,
+                "outputs": answer,
+                "length": length,
+                "answer_prefix": answer_prefix,
+            }
+        )
+    return rows
+
+
+def _binary_search_words(
+    *,
+    gen,
+    tokenizer,
+    vocab_size: int,
+    max_seq_length: int,
+    tokens_to_generate: int,
+    template_token: int = 0,
+    incremental: int,
+) -> int:
+    from loguru import logger
+
+    sample_text, _ = gen(min(4096, vocab_size))
+    sample_tokens = len(tokenizer.text_to_tokens(sample_text)) + template_token
+    tokens_per_word = sample_tokens / min(4096, vocab_size)
+    estimated_max_words = int(max_seq_length // tokens_per_word) * 2
+    lower_bound = incremental
+    upper_bound = max(estimated_max_words, incremental * 2)
+    # The wonderwords pool (~8k words) fills prompts to >=98% at <=32k but
+    # underfills at 64k/128k. We keep the cap and scope the `stable` contract to
+    # <=32k (see RulerZeroShotGenTask.reference_impl.notes); 64k/128k are
+    # experimental. Dropping the cap would route long contexts through the
+    # english_words.json fallback, which is not yet validated.
+    if upper_bound > vocab_size:
+        logger.warning(
+            f"RULER CWE: estimated word count {upper_bound} exceeds wonderwords "
+            f"vocab {vocab_size}; capping. Prompts at "
+            f"max_seq_length={max_seq_length} may underfill (expected at >32k)."
+        )
+        upper_bound = vocab_size
+    optimal: int | None = None
+    while lower_bound <= upper_bound:
+        mid = (lower_bound + upper_bound) // 2
+        input_text, _ = gen(mid)
+        total_tokens = (
+            len(tokenizer.text_to_tokens(input_text))
+            + template_token
+            + tokens_to_generate
+        )
+        if total_tokens <= max_seq_length:
+            optimal = mid
+            lower_bound = mid + 1
+        else:
+            upper_bound = mid - 1
+    return optimal if optimal is not None else incremental
+
+
+def _word_pool(random_seed: int) -> list[str]:
+    import wonderwords
+
+    # wonderwords >= 2.2.0: uses private _get_words_from_text_file(name) API to load
+    # word lists from internal data files. This is not stable across versions; pin to
+    # the tested version in pyproject.toml [project.optional-dependencies.ruler].
+    nouns = wonderwords.random_word._get_words_from_text_file("nounlist.txt")
+    adjs = wonderwords.random_word._get_words_from_text_file("adjectivelist.txt")
+    verbs = wonderwords.random_word._get_words_from_text_file("verblist.txt")
+    words = sorted(set(nouns + adjs + verbs))
+    random.Random(random_seed).shuffle(words)
+    return words
+
+
+def _get_example(
+    *,
+    num_words: int,
+    words: list[str],
+    randle_words: list[str],
+    common_repeats: int,
+    uncommon_repeats: int,
+    common_nums: int,
+    random_seed: int,
+) -> tuple[str, list[str]]:
+    if num_words <= len(words):
+        word_list_full = random.sample(words, num_words)
+    else:
+        word_list_full = random.sample(randle_words, num_words)
+    common, uncommon = word_list_full[:common_nums], word_list_full[common_nums:]
+    word_list = common * int(common_repeats) + uncommon * int(uncommon_repeats)
+    random.Random(random_seed).shuffle(word_list)
+    context = " ".join(f"{i + 1}. {word}" for i, word in enumerate(word_list))
+    return context, common
+
+
+def _generate_input_output(
+    *,
+    num_words: int,
+    words: list[str],
+    max_seq_length: int,
+    freq_cw: int,
+    freq_ucw: int,
+    num_cw: int,
+    random_seed: int,
+    num_fewshot: int,
+    randle_words: list[str],
+) -> tuple[str, list[str]]:
+    few_shots = []
+    if max_seq_length < 4096:
+        for _ in range(num_fewshot):
+            context_example, answer_example = _get_example(
+                num_words=20,
+                words=words,
+                randle_words=randle_words,
+                common_repeats=3,
+                uncommon_repeats=1,
+                common_nums=num_cw,
+                random_seed=random_seed,
+            )
+            few_shots.append((context_example, answer_example))
+        context, answer = _get_example(
+            num_words=num_words,
+            words=words,
+            randle_words=randle_words,
+            common_repeats=6,
+            uncommon_repeats=1,
+            common_nums=num_cw,
+            random_seed=random_seed,
+        )
+    else:
+        for _ in range(num_fewshot):
+            context_example, answer_example = _get_example(
+                num_words=40,
+                words=words,
+                randle_words=randle_words,
+                common_repeats=10,
+                uncommon_repeats=3,
+                common_nums=num_cw,
+                random_seed=random_seed,
+            )
+            few_shots.append((context_example, answer_example))
+        context, answer = _get_example(
+            num_words=num_words,
+            words=words,
+            randle_words=randle_words,
+            common_repeats=freq_cw,
+            uncommon_repeats=freq_ucw,
+            common_nums=num_cw,
+            random_seed=random_seed,
+        )
+    _template = (
+        ruler_task("common_words_extraction")["template"]
+        + ruler_task("common_words_extraction")["answer_prefix"]
+    )
+    for n in range(len(few_shots)):
+        shot_answer = " ".join(
+            f"{i + 1}. {word}" for i, word in enumerate(few_shots[n][1])
+        )
+        few_shots[n] = (
+            _template.format(num_cw=num_cw, context=few_shots[n][0], query="")
+            + " "
+            + shot_answer
+        )
+    few_shots_text = "\n".join(few_shots)
+    input_text = _template.format(num_cw=num_cw, context=context, query="")
+    return few_shots_text + "\n" + input_text, answer
diff --git a/sieval/datasets/ruler/_fwe.py b/sieval/datasets/ruler/_fwe.py
new file mode 100644
index 00000000..c8aacafc
--- /dev/null
+++ b/sieval/datasets/ruler/_fwe.py
@@ -0,0 +1,161 @@
+"""Frequent Words Extraction (FWE) synthesis helpers for RULER."""
+
+import random
+import string
+
+import numpy as np
+
+from sieval.community.ruler.scripts.tokenizer import select_tokenizer
+
+from ._shared import model_template_token, ruler_task, tokens_to_generate
+
+
+def load_fwe(
+    _name_or_path: str,
+    *,
+    max_seq_length: int,
+    tokenizer_type: str,
+    tokenizer_path: str,
+    num_samples: int,
+    random_seed: int,
+    remove_newline_tab: bool,
+    enable_thinking: bool,
+    think_budget: int = 0,
+    model_name: str = "qwen3",
+    alpha: float,
+    coded_wordlen: int,
+    vocab_size: int,
+) -> list[dict]:
+    from scipy.special import zeta
+
+    gen_budget = tokens_to_generate(
+        "freq_words_extraction",
+        enable_thinking=enable_thinking,
+        think_budget=think_budget,
+        model_name=model_name,
+        context_length=max_seq_length,
+        for_dataset=True,
+    )
+    tokenizer = select_tokenizer(tokenizer_type, tokenizer_path)
+
+    random.seed(random_seed)
+    np.random.seed(random_seed)
+
+    # Reserve room for the inference-time message-template overhead (role markers,
+    # and the prefilled empty <think></think> block in non-thinking mode), since
+    # the raw coded text is filled to ``input_max_len`` tokens. gen_budget already
+    # covers generation (answer + any think_budget). model_template_token is the
+    # upstream reserve. Without this reservation the wrapped prompt would overflow
+    # max_seq_length — FWE has no shrink-retry loop.
+    template_overhead = model_template_token(
+        tokenizer, model_name=model_name, enable_thinking=enable_thinking
+    )
+    input_max_len = max_seq_length - gen_budget - template_overhead
+    if input_max_len <= 0:
+        raise ValueError(
+            f"RULER FWE: no room for content — max_seq_length={max_seq_length} is "
+            f"fully consumed by generation budget ({gen_budget}, incl. any "
+            f"think_budget) + template overhead ({template_overhead}). Lower "
+            f"think_budget or raise max_seq_length."
+        )
+    if vocab_size == -1:
+        vocab_size = input_max_len // 50
+
+    _, _, num_example_words = _generate_input_output(
+        input_max_len,
+        tokenizer=tokenizer,
+        coded_wordlen=coded_wordlen,
+        vocab_size=vocab_size,
+        incremental=input_max_len // 32,
+        alpha=alpha,
+        random_seed=random_seed,
+        zeta=zeta,
+    )
+
+    fwe_answer_prefix = ruler_task("freq_words_extraction")["answer_prefix"]
+    rows: list[dict] = []
+    for index in range(num_samples):
+        input_text, answer, _ = _generate_input_output(
+            input_max_len,
+            tokenizer=tokenizer,
+            num_words=num_example_words,
+            coded_wordlen=coded_wordlen,
+            vocab_size=vocab_size,
+            incremental=input_max_len // 32,
+            alpha=alpha,
+            random_seed=random_seed,
+            zeta=zeta,
+        )
+        length = (
+            len(tokenizer.text_to_tokens(input_text)) + template_overhead + gen_budget
+        )
+        if remove_newline_tab:
+            input_text = " ".join(
+                input_text.replace("\n", " ").replace("\t", " ").strip().split()
+            )
+        answer_prefix_index = input_text.rfind(fwe_answer_prefix[:10])
+        answer_prefix = input_text[answer_prefix_index:]
+        input_text = input_text[:answer_prefix_index]
+        rows.append(
+            {
+                "index": index,
+                "input": input_text,
+                "outputs": answer,
+                "length": length,
+                "answer_prefix": answer_prefix,
+            }
+        )
+    return rows
+
+
+def _generate_input_output(
+    max_len: int,
+    *,
+    tokenizer,
+    zeta,
+    num_words: int = -1,
+    coded_wordlen: int = 6,
+    vocab_size: int = 2000,
+    incremental: int = 10,
+    alpha: float = 2.0,
+    random_seed: int = 42,
+) -> tuple[str, list[str], int]:
+    vocab = [
+        "".join(random.choices(string.ascii_lowercase, k=coded_wordlen))
+        for _ in range(vocab_size)
+    ]
+    while len(set(vocab)) < vocab_size:
+        vocab.append("".join(random.choices(string.ascii_lowercase, k=coded_wordlen)))
+    vocab = sorted(set(vocab))
+    random.Random(random_seed).shuffle(vocab)
+    vocab[0] = "..."
+
+    def gen_text(n_words: int) -> tuple[str, list[str]]:
+        k = np.arange(1, len(vocab) + 1)
+        sampled_cnt = n_words * (k**-alpha) / zeta(alpha)
+        sampled_words = [
+            [w] * zi for w, zi in zip(vocab, sampled_cnt.astype(int), strict=True)
+        ]
+        flat = [x for wlst in sampled_words for x in wlst]
+        random.Random(random_seed).shuffle(flat)
+        template = (
+            ruler_task("freq_words_extraction")["template"]
+            + ruler_task("freq_words_extraction")["answer_prefix"]
+        )
+        text = template.format(context=" ".join(flat), query="")
+        return text, vocab[1:4]
+
+    if num_words > 0:
+        text, answer = gen_text(num_words)
+        while len(tokenizer.text_to_tokens(text)) > max_len:
+            num_words -= incremental
+            text, answer = gen_text(num_words)
+    else:
+        num_words = max_len // coded_wordlen
+        text, answer = gen_text(num_words)
+        while len(tokenizer.text_to_tokens(text)) < max_len:
+            num_words += incremental
+            text, answer = gen_text(num_words)
+        num_words -= incremental
+    text, answer = gen_text(num_words)
+    return text, answer, num_words
diff --git a/sieval/datasets/ruler/_niah.py b/sieval/datasets/ruler/_niah.py
new file mode 100644
index 00000000..ed32e1bd
--- /dev/null
+++ b/sieval/datasets/ruler/_niah.py
@@ -0,0 +1,363 @@
+"""NIAH (Needle-in-a-Haystack) synthesis helpers for RULER."""
+
+import random
+
+import numpy as np
+
+from sieval.community.ruler.scripts.tokenizer import select_tokenizer
+
+from ._shared import (
+    _NEEDLE,
+    _NIAH_DEPTHS,
+    _build_haystack,
+    _ensure_punkt,
+    model_template_token,
+    ruler_task,
+    tokens_to_generate,
+)
+
+# NIAH subtask → load() kwargs, transcribed from NVIDIA/RULER synthetic.yaml @ab17b78.
+_NIAH_SUBTASK_KWARGS: dict[str, dict] = {
+    "niah_single_1": {
+        "type_haystack": "noise",
+        "type_needle_k": "words",
+        "type_needle_v": "numbers",
+        "num_needle_k": 1,
+        "num_needle_v": 1,
+        "num_needle_q": 1,
+    },
+    "niah_single_2": {
+        "type_haystack": "essay",
+        "type_needle_k": "words",
+        "type_needle_v": "numbers",
+        "num_needle_k": 1,
+        "num_needle_v": 1,
+        "num_needle_q": 1,
+    },
+    "niah_single_3": {
+        "type_haystack": "essay",
+        "type_needle_k": "words",
+        "type_needle_v": "uuids",
+        "num_needle_k": 1,
+        "num_needle_v": 1,
+        "num_needle_q": 1,
+    },
+    "niah_multikey_1": {
+        "type_haystack": "essay",
+        "type_needle_k": "words",
+        "type_needle_v": "numbers",
+        "num_needle_k": 4,
+        "num_needle_v": 1,
+        "num_needle_q": 1,
+    },
+    "niah_multikey_2": {
+        "type_haystack": "needle",
+        "type_needle_k": "words",
+        "type_needle_v": "numbers",
+        "num_needle_k": 1,
+        "num_needle_v": 1,
+        "num_needle_q": 1,
+    },
+    "niah_multikey_3": {
+        "type_haystack": "needle",
+        "type_needle_k": "uuids",
+        "type_needle_v": "uuids",
+        "num_needle_k": 1,
+        "num_needle_v": 1,
+        "num_needle_q": 1,
+    },
+    "niah_multivalue": {
+        "type_haystack": "essay",
+        "type_needle_k": "words",
+        "type_needle_v": "numbers",
+        "num_needle_k": 1,
+        "num_needle_v": 4,
+        "num_needle_q": 1,
+    },
+    "niah_multiquery": {
+        "type_haystack": "essay",
+        "type_needle_k": "words",
+        "type_needle_v": "numbers",
+        "num_needle_k": 1,
+        "num_needle_v": 1,
+        "num_needle_q": 4,
+    },
+}
+
+
+def load_niah(
+    name_or_path: str,
+    *,
+    max_seq_length: int,
+    tokenizer_type: str,
+    tokenizer_path: str,
+    num_samples: int,
+    random_seed: int,
+    remove_newline_tab: bool,
+    enable_thinking: bool,
+    think_budget: int = 0,
+    model_name: str = "qwen3",
+    num_needle_k: int,
+    num_needle_v: int,
+    num_needle_q: int,
+    type_haystack: str,
+    type_needle_k: str,
+    type_needle_v: str,
+) -> list[dict]:
+    gen_budget = tokens_to_generate(
+        "niah",
+        enable_thinking=enable_thinking,
+        think_budget=think_budget,
+        model_name=model_name,
+        context_length=max_seq_length,
+        for_dataset=True,
+    )
+    tokenizer = select_tokenizer(tokenizer_type, tokenizer_path)
+    # Upstream template reserve, computed once (sample-invariant).
+    mtt = model_template_token(
+        tokenizer, model_name=model_name, enable_thinking=enable_thinking
+    )
+
+    random.seed(random_seed)
+    np.random.seed(random_seed)
+
+    num_needle_k = max(num_needle_k, num_needle_q)
+    haystack = _build_haystack(name_or_path, type_haystack)
+    words = _niah_word_pool()
+
+    def gen(num_haystack: int) -> tuple[str, list[str]]:
+        return _generate_input_output(
+            num_haystack=num_haystack,
+            haystack=haystack,
+            words=words,
+            depths=_NIAH_DEPTHS,
+            random_seed=random_seed,
+            num_needle_k=num_needle_k,
+            num_needle_v=num_needle_v,
+            num_needle_q=num_needle_q,
+            type_haystack=type_haystack,
+            type_needle_k=type_needle_k,
+            type_needle_v=type_needle_v,
+        )
+
+    num_haystack = _fit_haystack_size(
+        gen=gen,
+        tokenizer=tokenizer,
+        type_haystack=type_haystack,
+        max_seq_length=max_seq_length,
+        tokens_to_generate=gen_budget,
+        template_token=mtt,
+    )
+
+    incremental = _incremental(type_haystack, max_seq_length)
+    niah_answer_prefix_template = ruler_task("niah")["answer_prefix"]
+
+    rows: list[dict] = []
+    for _ in range(num_samples):
+        used_haystack = num_haystack
+        while True:
+            try:
+                input_text, answer = gen(used_haystack)
+                length = len(tokenizer.text_to_tokens(input_text)) + mtt + gen_budget
+                assert length <= max_seq_length, "exceeds max_seq_length"
+                break
+            except Exception:
+                if used_haystack > incremental:
+                    used_haystack -= incremental
+        if remove_newline_tab:
+            input_text = " ".join(
+                input_text.replace("\n", " ").replace("\t", " ").strip().split()
+            )
+        answer_prefix_index = input_text.rfind(niah_answer_prefix_template[:10])
+        answer_prefix = input_text[answer_prefix_index:]
+        input_text = input_text[:answer_prefix_index]
+        index_in_text = input_text.find(answer[0])
+        token_position_answer = len(
+            tokenizer.text_to_tokens(input_text[:index_in_text])
+        )
+        rows.append(
+            {
+                "index": index_in_text,
+                "input": input_text,
+                "outputs": answer,
+                "length": length,
+                "answer_prefix": answer_prefix,
+                "token_position_answer": token_position_answer,
+            }
+        )
+    return rows
+
+
+def _incremental(type_haystack: str, max_seq_length: int) -> int:
+    if type_haystack == "essay":
+        return 500
+    if max_seq_length < 4096:
+        return 5
+    return 25
+
+
+def _fit_haystack_size(
+    *,
+    gen,
+    tokenizer,
+    type_haystack: str,
+    max_seq_length: int,
+    tokens_to_generate: int,
+    template_token: int = 0,
+) -> int:
+    incremental = _incremental(type_haystack, max_seq_length)
+    sample_prompt, _ = gen(incremental)
+    sample_tokens = len(tokenizer.text_to_tokens(sample_prompt)) + template_token
+    tokens_per_haystack = sample_tokens / incremental
+    estimated_max = int((max_seq_length / tokens_per_haystack) * 3)
+    lower_bound = incremental
+    upper_bound = max(estimated_max, incremental * 2)
+    optimal: int | None = None
+    while lower_bound <= upper_bound:
+        mid = (lower_bound + upper_bound) // 2
+        prompt, _ = gen(mid)
+        total = (
+            len(tokenizer.text_to_tokens(prompt)) + template_token + tokens_to_generate
+        )
+        if total <= max_seq_length:
+            optimal = mid
+            lower_bound = mid + 1
+        else:
+            upper_bound = mid - 1
+    return optimal if optimal is not None else incremental
+
+
+def _niah_word_pool() -> list[str]:
+    import wonderwords
+
+    # wonderwords >= 2.2.0: uses private _get_words_from_text_file(name) API to load
+    # word lists from internal data files. This is not stable across versions; pin to
+    # the tested version in pyproject.toml [project.optional-dependencies.ruler].
+    nouns = wonderwords.random_word._get_words_from_text_file("nounlist.txt")
+    adjs = wonderwords.random_word._get_words_from_text_file("adjectivelist.txt")
+    words = [f"{adj}-{noun}" for adj in adjs for noun in nouns]
+    return sorted(set(words))
+
+
+def _generate_random_number(num_digits: int = 7) -> str:
+    lower = 10 ** (num_digits - 1)
+    upper = 10**num_digits - 1
+    return str(random.randint(lower, upper))
+
+
+def _generate_random_word(words: list[str]) -> str:
+    return random.choice(words)
+
+
+def _generate_random_uuid() -> str:
+    import uuid
+
+    return str(uuid.UUID(int=random.getrandbits(128), version=4))
+
+
+def _random_value(type_needle: str, words: list[str]) -> str:
+    if type_needle == "numbers":
+        return _generate_random_number()
+    if type_needle == "words":
+        return _generate_random_word(words)
+    if type_needle == "uuids":
+        return _generate_random_uuid()
+    raise NotImplementedError(f"{type_needle} is not implemented.")
+
+
+def _generate_input_output(
+    *,
+    num_haystack: int,
+    haystack,
+    words: list[str],
+    depths: list[int],
+    random_seed: int,
+    num_needle_k: int,
+    num_needle_v: int,
+    num_needle_q: int,
+    type_haystack: str,
+    type_needle_k: str,
+    type_needle_v: str,
+) -> tuple[str, list[str]]:
+    keys: list[str] = []
+    values: list[list[str]] = []
+    needles: list[str] = []
+    for _ in range(num_needle_k):
+        keys.append(_random_value(type_needle_k, words))
+        value: list[str] = []
+        for _ in range(num_needle_v):
+            value.append(_random_value(type_needle_v, words))
+            needles.append(
+                _NEEDLE.format(
+                    type_needle_v=type_needle_v, key=keys[-1], value=value[-1]
+                )
+            )
+        values.append(value)
+
+    random.Random(random_seed).shuffle(needles)
+
+    if type_haystack == "essay":
+        if num_haystack <= len(haystack):
+            text = " ".join(haystack[:num_haystack])
+        else:
+            repeats = (num_haystack + len(haystack) - 1) // len(haystack)
+            text = " ".join((haystack * repeats)[:num_haystack])
+        _ensure_punkt()
+        from nltk.tokenize import sent_tokenize
+
+        document_sents = sent_tokenize(text.strip())
+        insertion_positions = (
+            [0]
+            + sorted(
+                int(len(document_sents) * (depth / 100))
+                for depth in random.sample(depths, len(needles))
+            )
+            + [len(document_sents)]
+        )
+        document_sents_list: list[str] = []
+        for i in range(1, len(insertion_positions)):
+            last_pos = insertion_positions[i - 1]
+            next_pos = insertion_positions[i]
+            document_sents_list.append(" ".join(document_sents[last_pos:next_pos]))
+            if i - 1 < len(needles):
+                document_sents_list.append(needles[i - 1])
+        context = " ".join(document_sents_list)
+    else:
+        if type_haystack == "noise":
+            sentences = [haystack] * num_haystack
+        else:
+            sentences = [
+                haystack.format(
+                    type_needle_v=type_needle_v,
+                    key=_random_value(type_needle_k, words),
+                    value=_random_value(type_needle_v, words),
+                )
+                for _ in range(num_haystack)
+            ]
+        indexes = sorted(random.sample(range(num_haystack), len(needles)), reverse=True)
+        for index, element in zip(indexes, needles, strict=True):
+            sentences.insert(index, element)
+        context = "\n".join(sentences)
+
+    indices = random.sample(range(num_needle_k), num_needle_q)
+    queries = [keys[i] for i in indices]
+    answers = [a for i in indices for a in values[i]]
+    query = (
+        ", ".join(queries[:-1]) + ", and " + queries[-1]
+        if len(queries) > 1
+        else queries[0]
+    )
+
+    template = ruler_task("niah")["template"] + ruler_task("niah")["answer_prefix"]
+    tnv = type_needle_v
+    if num_needle_q * num_needle_v == 1:
+        template = (
+            template.replace("Some", "A")
+            .replace("are all", "is")
+            .replace("are", "is")
+            .replace("answers", "answer")
+        )
+        tnv = tnv[:-1]
+
+    input_text = template.format(type_needle_v=tnv, context=context, query=query)
+    return input_text, answers
diff --git a/sieval/datasets/ruler/_qa.py b/sieval/datasets/ruler/_qa.py
new file mode 100644
index 00000000..ce0fb64a
--- /dev/null
+++ b/sieval/datasets/ruler/_qa.py
@@ -0,0 +1,268 @@
+"""QA (SQuAD / HotpotQA) synthesis helpers for RULER."""
+
+import json
+import os
+import random
+
+from sieval.community.ruler.scripts.tokenizer import select_tokenizer
+from sieval.core.utils.hf import ensure_dataset
+
+from ._shared import (
+    _DOCUMENT_PROMPT,
+    _HOTPOTQA_REPO_ID,
+    _HOTPOTQA_REVISION,
+    _SQUAD_FILE,
+    model_template_token,
+    ruler_task,
+    tokens_to_generate,
+)
+
+
+def load_qa(
+    name_or_path: str,
+    *,
+    dataset: str,
+    max_seq_length: int,
+    tokenizer_type: str,
+    tokenizer_path: str,
+    num_samples: int,
+    random_seed: int,
+    remove_newline_tab: bool,
+    enable_thinking: bool,
+    think_budget: int = 0,
+    model_name: str = "qwen3",
+    pre_samples: int,
+) -> list[dict]:
+    gen_budget = tokens_to_generate(
+        "qa",
+        enable_thinking=enable_thinking,
+        think_budget=think_budget,
+        model_name=model_name,
+        context_length=max_seq_length,
+        for_dataset=True,
+    )
+    tokenizer = select_tokenizer(tokenizer_type, tokenizer_path)
+    # Upstream template reserve, computed once (sample-invariant).
+    mtt = model_template_token(
+        tokenizer, model_name=model_name, enable_thinking=enable_thinking
+    )
+
+    random.seed(random_seed)
+
+    if dataset == "squad":
+        qas, docs = _read_squad(os.path.join(name_or_path, _SQUAD_FILE))
+    elif dataset == "hotpotqa":
+        qas, docs = _read_hotpotqa(name_or_path)
+    else:
+        raise NotImplementedError(f"{dataset} is not implemented.")
+
+    def gen(index: int, num_docs: int) -> tuple[str, list[str]]:
+        return _generate_input_output(
+            index=index,
+            num_docs=num_docs,
+            qas=qas,
+            docs=docs,
+            random_seed=random_seed,
+        )
+
+    incremental = 10
+    num_docs = _fit_num_docs(
+        gen=gen,
+        tokenizer=tokenizer,
+        max_seq_length=max_seq_length,
+        tokens_to_generate=gen_budget,
+        template_token=mtt,
+        incremental=incremental,
+    )
+
+    qa_answer_prefix = ruler_task("qa")["answer_prefix"]
+
+    rows: list[dict] = []
+    for index in range(num_samples):
+        used_docs = num_docs
+        while True:
+            try:
+                input_text, answer = gen(index + pre_samples, used_docs)
+                length = len(tokenizer.text_to_tokens(input_text)) + mtt + gen_budget
+                assert length <= max_seq_length, f"{length} exceeds max_seq_length"
+                break
+            except Exception:
+                # Shrink the distractor count until the prompt fits — catch broadly
+                # like the sibling loaders (_niah/_cwe/_vt). HotpotQA carries a fixed
+                # gold-doc set (~10), so once used_docs drops below it gen() raises
+                # ValueError from random.sample(curr_more, negative), not just the
+                # length AssertionError. That IS reachable at max_seq_length < 4096,
+                # so fail loud with a clear message instead of letting a bare
+                # ValueError escape or spinning forever. (>=4k fits on the first try
+                # — verified — so this path is inert there.)
+                if used_docs <= incremental:
+                    raise ValueError(
+                        f"max_seq_length={max_seq_length} is too small to fit "
+                        f"HotpotQA's gold documents; use max_seq_length >= 4096."
+                    ) from None
+                used_docs -= incremental
+        if remove_newline_tab:
+            input_text = " ".join(
+                input_text.replace("\n", " ").replace("\t", " ").strip().split()
+            )
+        answer_prefix_index = input_text.rfind(qa_answer_prefix[:10])
+        answer_prefix = input_text[answer_prefix_index:]
+        input_text = input_text[:answer_prefix_index]
+        rows.append(
+            {
+                "index": index,
+                "input": input_text,
+                "outputs": answer,
+                "length": length,
+                "answer_prefix": answer_prefix,
+            }
+        )
+    return rows
+
+
+def _fit_num_docs(
+    *,
+    gen,
+    tokenizer,
+    max_seq_length: int,
+    tokens_to_generate: int,
+    template_token: int = 0,
+    incremental: int = 10,
+) -> int:
+    sample_input_text, _ = gen(0, incremental)
+    sample_tokens = len(tokenizer.text_to_tokens(sample_input_text)) + template_token
+    tokens_per_doc = sample_tokens / incremental
+    estimated_max_docs = int((max_seq_length / tokens_per_doc) * 3)
+    lower_bound = incremental
+    upper_bound = max(estimated_max_docs, incremental * 2)
+    optimal: int | None = None
+    while lower_bound <= upper_bound:
+        mid = (lower_bound + upper_bound) // 2
+        input_text, _ = gen(0, mid)
+        total_tokens = (
+            len(tokenizer.text_to_tokens(input_text))
+            + template_token
+            + tokens_to_generate
+        )
+        if total_tokens <= max_seq_length:
+            optimal = mid
+            lower_bound = mid + 1
+        else:
+            upper_bound = mid - 1
+    return optimal if optimal is not None else incremental
+
+
+def _read_squad(path: str) -> tuple[list[dict], list[str]]:
+    with open(path, encoding="utf-8") as f:
+        data = json.load(f)
+    total_docs = [p["context"] for d in data["data"] for p in d["paragraphs"]]
+    total_docs = sorted(set(total_docs))
+    total_docs_dict = {c: idx for idx, c in enumerate(total_docs)}
+    total_qas = []
+    for d in data["data"]:
+        more_docs = [total_docs_dict[p["context"]] for p in d["paragraphs"]]
+        for p in d["paragraphs"]:
+            for qas in p["qas"]:
+                if not qas["is_impossible"]:
+                    total_qas.append(
+                        {
+                            "query": qas["question"],
+                            "outputs": [a["text"] for a in qas["answers"]],
+                            "context": [total_docs_dict[p["context"]]],
+                            "more_context": [
+                                idx
+                                for idx in more_docs
+                                if idx != total_docs_dict[p["context"]]
+                            ],
+                        }
+                    )
+    return total_qas, total_docs
+
+
+def _read_hotpotqa(name_or_path: str) -> tuple[list[dict], list[str]]:
+    from datasets import load_dataset as hf_load_dataset
+
+    # `qa_hotpotqa` is routed to the base data dir (ruler.py `_BASE_DIR_SUBTASKS`),
+    # where `sieval dataset download` mirrors the HF repo at `<data_dir>/<repo_id>`.
+    # Read that pinned staged copy first so eval works under HF_HUB_OFFLINE=1; the
+    # base dir itself is not a `distractor`-config dataset, so pointing the loader
+    # there would always miss the download and silently fetch online. Fall back to
+    # an online fetch only when the staged copy is absent or unreadable.
+    staged_path = os.path.join(name_or_path, *_HOTPOTQA_REPO_ID.split("/"))
+    try:
+        raw = hf_load_dataset(staged_path, "distractor", split="validation")
+    except (ValueError, FileNotFoundError):
+        raw = hf_load_dataset(
+            _HOTPOTQA_REPO_ID,
+            "distractor",
+            split="validation",
+            revision=_HOTPOTQA_REVISION,
+        )
+    data = ensure_dataset(raw)
+    total_docs_set = {
+        f"{title}\n{''.join(sents)}"
+        for row in data
+        for title, sents in zip(
+            row["context"]["title"], row["context"]["sentences"], strict=True
+        )
+    }
+    # Alphabetical order, matching upstream qa.py's `sorted(list(set(...)))` so the
+    # document indexing (and thus distractor selection) is byte-identical to upstream.
+    total_docs = sorted(total_docs_set)
+    total_docs_dict = {d: i for i, d in enumerate(total_docs)}
+    total_qas = []
+    for row in data:
+        ctx = row["context"]
+        context_indices = [
+            total_docs_dict[f"{t}\n{''.join(s)}"]
+            for t, s in zip(ctx["title"], ctx["sentences"], strict=True)
+        ]
+        total_qas.append(
+            {
+                "query": row["question"],
+                "outputs": [row["answer"]],
+                "context": context_indices,
+            }
+        )
+    return total_qas, total_docs
+
+
+def _generate_input_output(
+    *,
+    index: int,
+    num_docs: int,
+    qas: list[dict],
+    docs: list[str],
+    random_seed: int,
+) -> tuple[str, list[str]]:
+    curr = qas[index]
+    curr_q = curr["query"]
+    curr_a = curr["outputs"]
+    curr_docs = curr["context"]
+    curr_more = curr.get("more_context", [])
+    if num_docs < len(docs):
+        if (num_docs - len(curr_docs)) > len(curr_more):
+            addition_docs = [
+                i for i in range(len(docs)) if i not in curr_docs + curr_more
+            ]
+            all_docs = (
+                curr_docs
+                + curr_more
+                + random.sample(
+                    addition_docs,
+                    max(0, num_docs - len(curr_docs) - len(curr_more)),
+                )
+            )
+        else:
+            all_docs = curr_docs + random.sample(curr_more, num_docs - len(curr_docs))
+        all_docs = [docs[idx] for idx in all_docs]
+    else:
+        repeats = (num_docs + len(docs) - 1) // len(docs)
+        all_docs = (docs * repeats)[:num_docs]
+    random.Random(random_seed).shuffle(all_docs)
+    context = "\n\n".join(
+        _DOCUMENT_PROMPT.format(i=i + 1, document=d) for i, d in enumerate(all_docs)
+    )
+    template = ruler_task("qa")["template"] + ruler_task("qa")["answer_prefix"]
+    input_text = template.format(context=context, query=curr_q)
+    return input_text, curr_a
diff --git a/sieval/datasets/ruler/_shared.py b/sieval/datasets/ruler/_shared.py
new file mode 100644
index 00000000..0ea40c01
--- /dev/null
+++ b/sieval/datasets/ruler/_shared.py
@@ -0,0 +1,257 @@
+"""Shared constants and helpers used across all RULER subtask modules."""
+
+import gzip
+import json
+import os
+import re
+from typing import TypedDict, cast
+
+import numpy as np
+
+from sieval.community.ruler.datasets.constants import TASKS
+from sieval.community.ruler.datasets.template import Templates
+
+_NOISE_HAYSTACK = (
+    "The grass is green. The sky is blue. The sun is yellow. "
+    "Here we go. There and back again."
+)
+
+# Qwen3 thinking tag overhead: <think>\n\n</think>\n\n (4 tokens)
+QWEN3_THINKING_TAG_OVERHEAD = 4
+_CORPUS_FILE = "PaulGrahamEssays.json.gz"
+_NEEDLE = "One of the special magic {type_needle_v} for {key} is: {value}."
+_SQUAD_FILE = "dev-v2.0.json"
+_DOCUMENT_PROMPT = "Document {i}:\n{document}"
+
+# HotpotQA HuggingFace repo id. `sieval dataset download` mirrors it to
+# `<data_dir>/hotpotqa/hotpot_qa` (downloaders/hf.py: `dest_root / repo_id`), so
+# the loader resolves the staged copy by joining this onto the base data dir.
+_HOTPOTQA_REPO_ID = "hotpotqa/hotpot_qa"
+
+# Pin the HotpotQA snapshot for reproducibility across downloads.
+_HOTPOTQA_REVISION = "1908d6afbbead072334abe2965f91bd2709910ab"
+
+# Pin english_words.json to the same RULER commit vendored into community/ruler/.
+_RULER_DATA_SHA = "ab17b7853df4e0a30b78cd5d2b463ac7dff6ee13"
+
+# NIAH insertion depths (percentages).
+_NIAH_DEPTHS = list(np.round(np.linspace(0, 100, num=40, endpoint=True)).astype(int))
+
+# VT insertion depths (percentages).
+_VT_DEPTHS = list(np.round(np.linspace(0, 100, num=40, endpoint=True)).astype(int))
+
+
+class RulerTaskSpec(TypedDict):
+    tokens_to_generate: int
+    template: str
+    answer_prefix: str
+
+
+def ruler_task(name: str) -> RulerTaskSpec:
+    """Return the RULER spec for *name* with precise field types."""
+    return cast(RulerTaskSpec, TASKS[name])
+
+
+def tokens_to_generate(
+    task_name: str,
+    *,
+    enable_thinking: bool,
+    think_budget: int,
+    model_name: str = "",
+    context_length: int | None = None,
+    for_dataset: bool = False,
+) -> int:
+    """Compute the generation budget (``max_tokens``) for a RULER task.
+
+    This is what ``infer()`` passes as ``max_tokens``, so it must cover
+    everything the model *generates*:
+
+    - Thinking content (``think_budget``) when thinking is enabled — the model
+      generates the reasoning, so it needs room for it.
+    - The generated ``<think>...</think>`` tags (Qwen3, thinking mode only).
+    - The final answer (``base`` from the task spec).
+
+    The message-template overhead (role markers, and the *prefilled* empty
+    ``<think></think>`` block in Qwen3 non-thinking mode) belongs to the prompt,
+    not to generation, and is reserved via :func:`model_template_token`.
+
+    Qwen3 Think Adaptation:
+        This implementation diverges from upstream RULER (@NVIDIA/RULER ab17b78)
+        to support Qwen3's extended thinking mode (per Qwen3 technical report).
+
+        Upstream RULER: Computes a single ``tokens_to_generate`` value that accounts
+        for the model's generation capacity without distinguishing between dataset
+        fitting (context window calculation) and inference (max_tokens constraint).
+
+        Qwen3 Adaptation: Splits the budget calculation based on context_length:
+        - Small contexts (!128k): During dataset generation, assume thinking
+          content uses the native context window space (don't reserve think_budget
+          for fitting). During inference, max_tokens = gen_budget + think_budget.
+        - Large contexts (128k): During dataset generation, reserve think_budget
+          space (model can't reuse thinking tokens). During inference, max_tokens
+          already includes think_budget.
+
+        This ensures generated samples fit within their target context window
+        while allocating sufficient max_tokens for thinking during inference.
+
+    Args:
+        task_name: Name of the RULER task (e.g., "niah", "qa")
+        enable_thinking: Whether thinking mode is enabled
+        think_budget: Token budget for thinking (Qwen3: typically 8192)
+        model_name: Model identifier. Only Qwen3-family models generate
+            ``<think>`` tags that add the tag overhead.
+        context_length: Context length in tokens (4096, 8192, 32768, 131072, etc.).
+            Determines think_budget allocation strategy during dataset generation.
+        for_dataset: Whether this is called during dataset generation (True) or
+            inference (False). Affects think_budget inclusion based on context_length.
+
+    Returns:
+        Total tokens the model may generate (thinking + tags + answer).
+    """
+    base = ruler_task(task_name)["tokens_to_generate"]
+    is_qwen3 = model_name.lower().startswith("qwen3")
+
+    if not enable_thinking:
+        # Non-thinking: the empty <think></think> block (Qwen3) is prefilled in
+        # the prompt template, so only the answer is generated.
+        return base
+
+    # Thinking mode: Qwen3-adapted budget allocation based on context_length
+    # (Diverges from upstream RULER which always includes think_budget)
+    should_skip_think_budget = (
+        for_dataset and context_length is not None and context_length != 131072  # 128k
+    )
+
+    if should_skip_think_budget:
+        # SMALL CONTEXT (4k, 8k, etc.) DATASET GENERATION:
+        # Assume thinking content reuses the native context window space.
+        # Don't reserve think_budget in gen_budget — this makes the generated
+        # samples shorter, fitting within the small context window.
+        # Inference will later add think_budget via:
+        # max_tokens = gen_budget + think_budget.
+        #
+        # Rationale: In Qwen3's thinking mode, models can perform inference over
+        # the thinking tokens within the same context pass for small windows.
+        if is_qwen3:
+            return QWEN3_THINKING_TAG_OVERHEAD + base
+        return base
+
+    # LARGE CONTEXT (128k) OR INFERENCE:
+    # For large contexts during dataset generation: reserve think_budget upfront
+    # (model cannot reuse thinking tokens efficiently across context boundaries).
+    # For inference regardless of context_length: always include think_budget
+    # (inference max_tokens must cover all generated output including thinking).
+    #
+    # Thinking budget = tags (Qwen3 only) + think_budget + answer base
+    if is_qwen3:
+        return QWEN3_THINKING_TAG_OVERHEAD + think_budget + base
+    return think_budget + base
+
+
+def thinking_prefill(model_name: str, enable_thinking: bool) -> str:
+    """Placeholder text a reasoning model prefills into the assistant turn.
+
+    Compatibility layer supporting both assistant-message and user-message patterns.
+
+    Qwen3 specifics:
+    - When thinking is enabled: Returns empty string (model continues in the
+      existing <think> block)
+    - When thinking is disabled: Returns "<think>\n\n</think>\n\n" (empty block
+      to skip reasoning)
+
+    Other models: Always returns empty string (no special handling needed)
+    """
+    if "qwen3" in model_name.lower() and not enable_thinking:
+        return "<think>\n\n</think>\n\n"  # Empty block; skip to answer
+    return ""
+
+
+def len_tag(length: int) -> str:
+    """Convert a context length to a short tag: 4096 → '4k', 131072 → '128k'."""
+    return f"{length // 1024}k" if length % 1024 == 0 else str(length)
+
+
+def _build_haystack(name_or_path: str, type_haystack: str):
+    if type_haystack == "essay":
+        path = os.path.join(name_or_path, _CORPUS_FILE)
+        with gzip.open(path, "rt", encoding="utf-8") as f:
+            text = json.load(f)["text"]
+        return re.sub(r"\s+", " ", text).split(" ")
+    if type_haystack == "noise":
+        return _NOISE_HAYSTACK
+    if type_haystack == "needle":
+        return _NEEDLE
+    raise NotImplementedError(f"{type_haystack} is not implemented.")
+
+
+def _ensure_punkt() -> None:
+    import nltk
+
+    try:
+        nltk.data.find("tokenizers/punkt_tab")
+    except LookupError:
+        nltk.download("punkt_tab")
+
+
+# Qwen3 message templates. These are a sieval addition — upstream RULER's
+# vendored template.py (community/ruler/datasets, @ab17b78) has no Qwen3 entry —
+# so they live here to keep the vendored file byte-faithful to upstream.
+# Non-thinking prefills an empty <think></think> block to suppress reasoning;
+# thinking opens the assistant turn and lets the model generate the block.
+_QWEN3_TEMPLATES = {
+    False: (
+        "<|im_start|>user\n{task_template} <|im_end|>\n"
+        "<|im_start|>assistant\n<think>\n\n</think>\n\n"
+    ),
+    True: "<|im_start|>user\n{task_template} <|im_end|>\n<|im_start|>assistant\n",
+}
+
+
+def get_template(model_name: str, enable_thinking: bool) -> str:
+    """Get the message template for a model and thinking mode.
+
+    Qwen3 templates are sieval-owned (see ``_QWEN3_TEMPLATES``); other models use
+    the vendored upstream ``Templates`` (case-insensitive), falling back to the
+    passthrough ``base`` template for unknown models.
+
+    Args:
+        model_name: Model identifier (e.g., "qwen3", "gpt-4", "llama")
+        enable_thinking: Whether thinking mode is enabled
+
+    Returns:
+        Template string with a ``{task_template}`` placeholder.
+    """
+    if model_name.lower().startswith("qwen3"):
+        return _QWEN3_TEMPLATES[enable_thinking]
+
+    # Other models: vendored template by name (case-insensitive), else base.
+    by_lower = {k.lower(): k for k in Templates}
+    key = by_lower.get(model_name.lower())
+    return Templates[key] if key is not None else "{task_template}"
+
+
+def model_template_token(
+    tokenizer,
+    model_name: str = "qwen3",
+    enable_thinking: bool = False,
+) -> int:
+    """Per-config template-token reserve, as NVIDIA/RULER computes it (@ab17b78).
+
+    Mirrors upstream ``prepare.py``::
+
+        model_template_token = len(text_to_tokens(model_template))
+
+    where ``model_template`` is the RAW template string — the literal
+    ``{task_template}`` placeholder is NOT replaced. Upstream then does
+    ``max_seq_length -= model_template_token`` before fitting; callers here add
+    this reserve to the content token count, which is algebraically identical.
+
+    Counting the unformatted template reserves the placeholder's own tokens,
+    which are replaced by real content at inference and never emitted — that is
+    the headroom that keeps prompts off the exact ``max_seq_length`` boundary.
+
+    This value is invariant per ``(model_name, enable_thinking)``, so compute it
+    once per dataset build and reuse it across the fitting loop and per-sample
+    checks rather than recomputing it for every candidate.
+    """
+    return len(tokenizer.text_to_tokens(get_template(model_name, enable_thinking)))
diff --git a/sieval/datasets/ruler/_vt.py b/sieval/datasets/ruler/_vt.py
new file mode 100644
index 00000000..f8255c16
--- /dev/null
+++ b/sieval/datasets/ruler/_vt.py
@@ -0,0 +1,368 @@
+"""Variable Tracking (VT) synthesis helpers for RULER."""
+
+import heapq
+import random
+import string
+
+import numpy as np
+
+from sieval.community.ruler.scripts.tokenizer import select_tokenizer
+
+from ._shared import (
+    _VT_DEPTHS,
+    _build_haystack,
+    _ensure_punkt,
+    model_template_token,
+    ruler_task,
+    tokens_to_generate,
+)
+
+
+def _count_prompt_tokens(
+    tokenizer,
+    text: str,
+    *,
+    apply_template: bool,
+    template_token: int,
+) -> int:
+    """Count tokens for VT prompt sizing.
+
+    When *apply_template* is True the text is the real final prompt, so the
+    (precomputed) message-template reserve ``template_token`` is added. When
+    False the text is a fragment (e.g. the ICL example being synthesized for
+    later embedding), so a raw token count is used — adding the reserve to a
+    fragment would double-count it once the fragment is spliced into the real
+    prompt.
+    """
+    n = len(tokenizer.text_to_tokens(text))
+    return n + template_token if apply_template else n
+
+
+def load_vt(
+    name_or_path: str,
+    *,
+    max_seq_length: int,
+    tokenizer_type: str,
+    tokenizer_path: str,
+    num_samples: int,
+    random_seed: int,
+    remove_newline_tab: bool,
+    enable_thinking: bool,
+    think_budget: int = 0,
+    model_name: str = "qwen3",
+    num_chains: int,
+    num_hops: int,
+    type_haystack: str,
+) -> list[dict]:
+    gen_budget = tokens_to_generate(
+        "variable_tracking",
+        enable_thinking=enable_thinking,
+        think_budget=think_budget,
+        model_name=model_name,
+        context_length=max_seq_length,
+        for_dataset=True,
+    )
+    tokenizer = select_tokenizer(tokenizer_type, tokenizer_path)
+    # Upstream template reserve, computed once (sample-invariant).
+    mtt = model_template_token(
+        tokenizer, model_name=model_name, enable_thinking=enable_thinking
+    )
+
+    random.seed(random_seed)
+    np.random.seed(random_seed)
+
+    haystack = _build_haystack(name_or_path, type_haystack)
+
+    icl_example = _synthesize(
+        tokenizer=tokenizer,
+        num_samples=1,
+        max_seq_length=500,
+        num_chains=num_chains,
+        num_hops=num_hops,
+        tokens_to_generate=0,
+        add_fewshot=True,
+        icl_example=None,
+        remove_newline_tab=remove_newline_tab,
+        type_haystack=type_haystack,
+        haystack=haystack,
+        final_output=False,
+        template_token=mtt,
+    )[0]
+
+    return _synthesize(
+        tokenizer=tokenizer,
+        num_samples=num_samples,
+        max_seq_length=max_seq_length,
+        num_chains=num_chains,
+        num_hops=num_hops,
+        tokens_to_generate=gen_budget,
+        add_fewshot=True,
+        icl_example=icl_example,
+        remove_newline_tab=remove_newline_tab,
+        type_haystack=type_haystack,
+        haystack=haystack,
+        final_output=True,
+        template_token=mtt,
+    )
+
+
+def _synthesize(
+    *,
+    tokenizer,
+    num_samples: int,
+    max_seq_length: int,
+    num_chains: int,
+    num_hops: int,
+    tokens_to_generate: int,
+    icl_example: dict | None,
+    remove_newline_tab: bool,
+    type_haystack: str,
+    haystack,
+    final_output: bool = False,
+    add_fewshot: bool = True,
+    template_token: int = 0,
+) -> list[dict]:
+    is_icl = add_fewshot and (icl_example is None)
+    # The ICL example is synthesized as a raw fragment for later embedding; only
+    # the real prompt (is_icl=False) is wrapped in the inference-time template.
+    apply_template = not is_icl
+
+    if icl_example is not None:
+        incremental = 500 if type_haystack == "essay" else 10
+        if type_haystack != "essay" and max_seq_length < 4096:
+            incremental = 5
+    else:
+        incremental = 50 if type_haystack == "essay" else 5
+
+    example_tokens = 0
+    icl_text: str | None = None
+    if add_fewshot and (icl_example is not None):
+        icl_text = icl_example["input"] + " " + " ".join(icl_example["outputs"]) + "\n"
+        example_tokens = len(tokenizer.text_to_tokens(icl_text))
+
+    def gen(num_noises: int) -> tuple[str, list[str]]:
+        return _generate_input_output(
+            num_noises=num_noises,
+            num_chains=num_chains,
+            num_hops=num_hops,
+            is_icl=is_icl,
+            type_haystack=type_haystack,
+            haystack=haystack,
+        )
+
+    num_noises = _binary_search_noises(
+        gen=gen,
+        tokenizer=tokenizer,
+        max_seq_length=max_seq_length,
+        tokens_to_generate=tokens_to_generate,
+        example_tokens=example_tokens,
+        incremental=incremental,
+        apply_template=apply_template,
+        template_token=template_token,
+    )
+
+    rows: list[dict] = []
+    for index in range(num_samples):
+        used_noises = num_noises
+        while True:
+            try:
+                input_text, answer = gen(used_noises)
+                if add_fewshot and (icl_text is not None):
+                    cutoff = input_text.index(
+                        ruler_task("variable_tracking")["template"][:20]
+                    )
+                    input_text = (
+                        input_text[:cutoff]
+                        + _randomize_icl(icl_text, num_hops)
+                        + "\n"
+                        + input_text[cutoff:]
+                    )
+                if remove_newline_tab:
+                    input_text = " ".join(
+                        input_text.replace("\n", " ").replace("\t", " ").strip().split()
+                    )
+                length = (
+                    _count_prompt_tokens(
+                        tokenizer,
+                        input_text,
+                        apply_template=apply_template,
+                        template_token=template_token,
+                    )
+                    + tokens_to_generate
+                )
+                assert length <= max_seq_length, "exceeds max_seq_length"
+                break
+            except Exception:
+                if used_noises > incremental:
+                    used_noises -= incremental
+        if final_output:
+            answer_prefix_index = input_text.rfind(
+                ruler_task("variable_tracking")["answer_prefix"][:10]
+            )
+            answer_prefix = input_text[answer_prefix_index:]
+            input_text = input_text[:answer_prefix_index]
+            rows.append(
+                {
+                    "index": index,
+                    "input": input_text,
+                    "outputs": answer,
+                    "length": length,
+                    "answer_prefix": answer_prefix,
+                }
+            )
+        else:
+            rows.append(
+                {
+                    "index": index,
+                    "input": input_text,
+                    "outputs": answer,
+                    "length": length,
+                }
+            )
+    return rows
+
+
+def _binary_search_noises(
+    *,
+    gen,
+    tokenizer,
+    max_seq_length: int,
+    tokens_to_generate: int,
+    example_tokens: int,
+    incremental: int,
+    apply_template: bool = False,
+    template_token: int = 0,
+) -> int:
+    # ``example_tokens`` is a raw fragment count added on top; the sized ``text``
+    # carries the template reserve once via _count_prompt_tokens. The generation
+    # budget (answer + any think_budget) is added separately as tokens_to_generate.
+    sample_text, _ = gen(incremental)
+    sample_tokens = _count_prompt_tokens(
+        tokenizer,
+        sample_text,
+        apply_template=apply_template,
+        template_token=template_token,
+    )
+    tokens_per_haystack = sample_tokens / incremental
+    estimated_max = int((max_seq_length / tokens_per_haystack) * 3)
+    lower_bound, upper_bound = incremental, max(estimated_max, incremental * 2)
+    optimal: int | None = None
+    while lower_bound <= upper_bound:
+        mid = (lower_bound + upper_bound) // 2
+        text, _ = gen(mid)
+        total = (
+            _count_prompt_tokens(
+                tokenizer,
+                text,
+                apply_template=apply_template,
+                template_token=template_token,
+            )
+            + example_tokens
+            + tokens_to_generate
+        )
+        if total <= max_seq_length:
+            optimal = mid
+            lower_bound = mid + 1
+        else:
+            upper_bound = mid - 1
+    return optimal if optimal is not None else incremental
+
+
+def _generate_chains(
+    num_chains: int, num_hops: int, is_icl: bool = False
+) -> tuple[list[list[str]], list[list[str]]]:
+    k = 5 if not is_icl else 3
+    num_hops = num_hops if not is_icl else min(10, num_hops)
+    vars_all = [
+        "".join(random.choices(string.ascii_uppercase, k=k)).upper()
+        for _ in range((num_hops + 1) * num_chains)
+    ]
+    while len(set(vars_all)) < num_chains * (num_hops + 1):
+        vars_all.append("".join(random.choices(string.ascii_uppercase, k=k)).upper())
+    vars_ret: list[list[str]] = []
+    chains_ret: list[list[str]] = []
+    for i in range(0, len(vars_all), num_hops + 1):
+        this_vars = vars_all[i : i + num_hops + 1]
+        vars_ret.append(this_vars)
+        if is_icl:
+            this_chain = [f"VAR {this_vars[0]} = 12345"]
+        else:
+            this_chain = [f"VAR {this_vars[0]} = {np.random.randint(10000, 99999)}"]
+        for j in range(num_hops):
+            this_chain.append(f"VAR {this_vars[j + 1]} = VAR {this_vars[j]} ")
+        chains_ret.append(this_chain)
+    return vars_ret, chains_ret
+
+
+def _shuffle_sublists_heap(lst: list[list[str]]) -> list[str]:
+    heap: list[tuple[float, int, int]] = []
+    for i in range(len(lst)):
+        heapq.heappush(heap, (random.random(), i, 0))
+    result: list[str] = []
+    while heap:
+        _, list_idx, elem_idx = heapq.heappop(heap)
+        result.append(lst[list_idx][elem_idx])
+        if elem_idx + 1 < len(lst[list_idx]):
+            heapq.heappush(heap, (random.random(), list_idx, elem_idx + 1))
+    return result
+
+
+def _generate_input_output(
+    *,
+    num_noises: int,
+    num_chains: int,
+    num_hops: int,
+    type_haystack: str,
+    haystack,
+    is_icl: bool = False,
+) -> tuple[str, list[str]]:
+    variables, chains = _generate_chains(num_chains, num_hops, is_icl=is_icl)
+    value = chains[0][0].split("=")[-1].strip()
+    if type_haystack == "essay":
+        from nltk.tokenize import sent_tokenize
+
+        text = " ".join(haystack[:num_noises])
+        _ensure_punkt()
+        document_sents = sent_tokenize(text.strip())
+        chains_flat = _shuffle_sublists_heap(chains)
+        insertion_positions = (
+            [0]
+            + sorted(
+                int(len(document_sents) * (depth / 100))
+                for depth in random.sample(_VT_DEPTHS, len(chains_flat))
+            )
+            + [len(document_sents)]
+        )
+        document_sents_list: list[str] = []
+        for i in range(1, len(insertion_positions)):
+            last_pos = insertion_positions[i - 1]
+            next_pos = insertion_positions[i]
+            document_sents_list.append(" ".join(document_sents[last_pos:next_pos]))
+            if i - 1 < len(chains_flat):
+                document_sents_list.append(chains_flat[i - 1].strip() + ".")
+        context = " ".join(document_sents_list)
+    elif type_haystack == "noise":
+        sentences = [haystack] * num_noises
+        for chain in chains:
+            positions = sorted(random.sample(range(len(sentences)), len(chain)))
+            for insert_pi, j in zip(positions, range(len(chain)), strict=True):
+                sentences.insert(insert_pi + j, chain[j])
+        context = "\n".join(sentences)
+    else:
+        raise NotImplementedError(f"{type_haystack} is not implemented.")
+    context = context.replace(". \n", ".\n")
+    template = (
+        ruler_task("variable_tracking")["template"]
+        + ruler_task("variable_tracking")["answer_prefix"]
+    )
+    input_text = template.format(context=context, query=value, num_v=num_hops + 1)
+    return input_text, variables[0]
+
+
+def _randomize_icl(icl_example: str, num_hops: int) -> str:
+    icl_tgt = icl_example.strip().split()[-num_hops - 1 :]
+    for item in icl_tgt:
+        new_item = "".join(random.choices(string.ascii_uppercase, k=len(item))).upper()
+        icl_example = icl_example.replace(item, new_item)
+    icl_example = icl_example.replace("12345", str(np.random.randint(10000, 99999)))
+    return icl_example
diff --git a/sieval/datasets/ruler/ruler.py b/sieval/datasets/ruler/ruler.py
new file mode 100644
index 00000000..7b68005c
--- /dev/null
+++ b/sieval/datasets/ruler/ruler.py
@@ -0,0 +1,311 @@
+"""Unified RULER dataset: 13 subtasks in one loader.
+
+Each call to ``load()`` targets one subtask (or ``"all"`` to concatenate all
+13). Every emitted row carries ``subtask`` and ``context_length`` fields so
+``RulerZeroShotGenTask.report()`` can group and score without any external
+aggregation command.
+
+The 13 canonical subtask names (transcribed from NVIDIA/RULER's synthetic.yaml
+@ab17b78; the two QA subtasks are RULER's separate qa config):
+    niah_single_1, niah_single_2, niah_single_3,
+    niah_multikey_1, niah_multikey_2, niah_multikey_3,
+    niah_multivalue, niah_multiquery,
+    vt, cwe, fwe, qa_squad, qa_hotpotqa
+
+AI-Generated Code - Claude Sonnet 4.6 (Anthropic)
+"""
+
+from typing import NotRequired, TypedDict, override
+
+from datasets import Dataset as HFDataset
+from datasets import DatasetDict as HFDatasetDict
+from datasets import concatenate_datasets
+
+from sieval.core.datasets import (
+    Category,
+    Dataset,
+    Level1Category,
+    sieval_dataset,
+)
+
+from ._cwe import load_cwe
+from ._fwe import load_fwe
+from ._niah import _NIAH_SUBTASK_KWARGS, load_niah
+from ._qa import load_qa
+from ._shared import (
+    _HOTPOTQA_REPO_ID,
+    _HOTPOTQA_REVISION,
+    _RULER_DATA_SHA,
+    tokens_to_generate,
+)
+from ._vt import load_vt
+
+_ALL_SUBTASKS = (
+    "niah_single_1",
+    "niah_single_2",
+    "niah_single_3",
+    "niah_multikey_1",
+    "niah_multikey_2",
+    "niah_multikey_3",
+    "niah_multivalue",
+    "niah_multiquery",
+    "vt",
+    "cwe",
+    "fwe",
+    "qa_squad",
+    "qa_hotpotqa",
+)
+
+# Subtasks that read from the base data dir rather than ``<data_dir>/ruler/``:
+# FWE is fully synthetic (no external files) and qa_hotpotqa is fetched from HF.
+# Everything else reads staged files under the ``ruler`` staging subdir.
+_BASE_DIR_SUBTASKS = frozenset({"fwe", "qa_hotpotqa"})
+
+
+def _subtask_data_path(name_or_path: str, subtask: str) -> str:
+    """Resolve the data dir a single subtask reads from.
+
+    Centralizes the ``/ruler`` staging-subdir rule so every entry point — a
+    direct single-subtask ``load``, ``"all"``, and explicit subtask lists — maps
+    the path identically. Previously only the aggregate branches appended
+    ``/ruler``, so a direct ``subtask="niah_single_2"`` looked in the wrong dir
+    and raised ``FileNotFoundError``.
+    """
+    return name_or_path if subtask in _BASE_DIR_SUBTASKS else f"{name_or_path}/ruler"
+
+
+# Each subtask's RULER task name, for the per-subtask generation budget
+# (tokens_to_generate). All 8 NIAH variants share "niah".
+_NON_NIAH_RULER_TASK = {
+    "vt": "variable_tracking",
+    "cwe": "common_words_extraction",
+    "fwe": "freq_words_extraction",
+    "qa_squad": "qa",
+    "qa_hotpotqa": "qa",
+}
+
+
+def _ruler_task_name(subtask: str) -> str:
+    return "niah" if subtask in _NIAH_SUBTASK_KWARGS else _NON_NIAH_RULER_TASK[subtask]
+
+
+class RulerDatasetSample(TypedDict):
+    index: int
+    input: str
+    outputs: list[str]
+    length: int
+    answer_prefix: str
+    subtask: str
+    context_length: int
+    gen_budget: int  # per-subtask generation cap (tokens_to_generate)
+    think_budget: NotRequired[int]  # thinking budget, added if enable_thinking=True
+    enable_thinking: NotRequired[bool]  # whether thinking mode is enabled
+    token_position_answer: NotRequired[int]  # NIAH only
+
+
+@sieval_dataset(
+    name="ruler",
+    display_name="RULER",
+    description=(
+        "RULER long-context benchmark: 13 subtasks (NIAH ×8, VT, CWE, FWE, QA ×2)."
+    ),
+    source=(
+        "local:paul_graham_essays/PaulGrahamEssays.json.gz",
+        # Staged for upstream parity but NOT load-bearing in the <=32k stable scope:
+        # CWE caps num_words at the wonderwords vocab, so the english_words.json
+        # fallback pool is never sampled (see _cwe.py `_word_pool` cap comment).
+        f"url:https://media.githubusercontent.com/media/NVIDIA/RULER/{_RULER_DATA_SHA}/scripts/data/synthetic/json/english_words.json",
+        "url:https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v2.0.json",
+        f"hf:{_HOTPOTQA_REPO_ID}@{_HOTPOTQA_REVISION}",
+    ),
+    checksums={
+        "english_words.json": "sha256:affcd6d45fdf3cc843d585c99c97ad615094e760e6c4756b654bab6c73bc2eca",  # noqa: E501
+        "dev-v2.0.json": "sha256:80a5225e94905956a6446d296ca1093975c4d3b3260f1d6c8f68bc2ab77182d8",  # noqa: E501
+    },
+    categories=(Category(Level1Category.LANGUAGE, "SemanticUnderstanding"),),
+    tags=("english", "open-ended", "long-context"),
+    license="Apache-2.0",
+    deps_group="ruler",
+)
+class RulerDataset(Dataset[RulerDatasetSample]):
+    @override
+    def load(
+        self,
+        name_or_path: str,
+        *,
+        subtask: str | list[str] | None = None,
+        max_seq_length: int = 4096,
+        tokenizer_type: str = "openai",
+        tokenizer_path: str = "cl100k_base",
+        num_samples: int = 500,
+        random_seed: int = 42,
+        remove_newline_tab: bool = False,
+        enable_thinking: bool = False,
+        think_budget: int = 0,
+        model_name: str = "qwen3",
+        # NIAH needle/haystack config is fixed per subtask by _NIAH_SUBTASK_KWARGS,
+        # so it is not exposed as a load() knob (a stray value would do nothing).
+        # CWE-specific
+        freq_cw: int = 30,
+        freq_ucw: int = 3,
+        num_cw: int = 10,
+        num_fewshot: int = 1,
+        # VT-specific
+        num_chains: int = 1,
+        num_hops: int = 4,
+        # FWE-specific
+        alpha: float = 2.0,
+        coded_wordlen: int = 6,
+        vocab_size: int = -1,
+        # QA-specific
+        pre_samples: int = 0,
+        **kwargs,
+    ) -> HFDatasetDict:
+        if subtask is None:
+            raise ValueError("RulerDataset.load requires `subtask`")
+
+        # A single subtask, an explicit list, and "all" all funnel through one
+        # dispatch loop — a single subtask is just a one-element target list. This
+        # keeps the ``/ruler`` staging-path rule (``_subtask_data_path``) and the
+        # budget stamping identical across every entry point: no recursion, no
+        # re-listed kwargs, and no way for the aggregate and single-subtask paths
+        # to drift apart.
+        if subtask == "all":
+            targets = list(_ALL_SUBTASKS)
+        elif isinstance(subtask, list):
+            targets = subtask
+        else:
+            targets = [subtask]
+
+        splits = []
+        for st in targets:
+            data_path = _subtask_data_path(name_or_path, st)
+
+            if st in _NIAH_SUBTASK_KWARGS:
+                niah_kwargs = _NIAH_SUBTASK_KWARGS[st]
+                rows = load_niah(
+                    data_path,
+                    max_seq_length=max_seq_length,
+                    tokenizer_type=tokenizer_type,
+                    tokenizer_path=tokenizer_path,
+                    num_samples=num_samples,
+                    random_seed=random_seed,
+                    remove_newline_tab=remove_newline_tab,
+                    enable_thinking=enable_thinking,
+                    think_budget=think_budget,
+                    model_name=model_name,
+                    num_needle_k=niah_kwargs["num_needle_k"],
+                    num_needle_v=niah_kwargs["num_needle_v"],
+                    num_needle_q=niah_kwargs["num_needle_q"],
+                    type_haystack=niah_kwargs["type_haystack"],
+                    type_needle_k=niah_kwargs["type_needle_k"],
+                    type_needle_v=niah_kwargs["type_needle_v"],
+                )
+            elif st == "vt":
+                rows = load_vt(
+                    data_path,
+                    max_seq_length=max_seq_length,
+                    tokenizer_type=tokenizer_type,
+                    tokenizer_path=tokenizer_path,
+                    num_samples=num_samples,
+                    random_seed=random_seed,
+                    remove_newline_tab=remove_newline_tab,
+                    enable_thinking=enable_thinking,
+                    think_budget=think_budget,
+                    model_name=model_name,
+                    num_chains=num_chains,
+                    num_hops=num_hops,
+                    type_haystack="noise",
+                )
+            elif st == "cwe":
+                rows = load_cwe(
+                    data_path,
+                    max_seq_length=max_seq_length,
+                    tokenizer_type=tokenizer_type,
+                    tokenizer_path=tokenizer_path,
+                    num_samples=num_samples,
+                    random_seed=random_seed,
+                    remove_newline_tab=remove_newline_tab,
+                    enable_thinking=enable_thinking,
+                    think_budget=think_budget,
+                    model_name=model_name,
+                    freq_cw=freq_cw,
+                    freq_ucw=freq_ucw,
+                    num_cw=num_cw,
+                    num_fewshot=num_fewshot,
+                )
+            elif st == "fwe":
+                rows = load_fwe(
+                    data_path,
+                    max_seq_length=max_seq_length,
+                    tokenizer_type=tokenizer_type,
+                    tokenizer_path=tokenizer_path,
+                    num_samples=num_samples,
+                    random_seed=random_seed,
+                    remove_newline_tab=remove_newline_tab,
+                    enable_thinking=enable_thinking,
+                    think_budget=think_budget,
+                    model_name=model_name,
+                    alpha=alpha,
+                    coded_wordlen=coded_wordlen,
+                    vocab_size=vocab_size,
+                )
+            elif st in ("qa_squad", "qa_hotpotqa"):
+                qa_dataset = "squad" if st == "qa_squad" else "hotpotqa"
+                rows = load_qa(
+                    data_path,
+                    dataset=qa_dataset,
+                    max_seq_length=max_seq_length,
+                    tokenizer_type=tokenizer_type,
+                    tokenizer_path=tokenizer_path,
+                    num_samples=num_samples,
+                    random_seed=random_seed,
+                    remove_newline_tab=remove_newline_tab,
+                    enable_thinking=enable_thinking,
+                    think_budget=think_budget,
+                    model_name=model_name,
+                    pre_samples=pre_samples,
+                )
+            else:
+                raise ValueError(
+                    f"Unknown subtask {st!r}. Valid subtasks: {_ALL_SUBTASKS} or 'all'."
+                )
+
+            gen_budget = tokens_to_generate(
+                _ruler_task_name(st),
+                enable_thinking=enable_thinking,
+                think_budget=think_budget,
+                model_name=model_name,
+                context_length=max_seq_length,
+                for_dataset=True,
+            )
+            rows = _stamp(
+                rows,
+                subtask=st,
+                context_length=max_seq_length,
+                gen_budget=gen_budget,
+                think_budget=think_budget,
+                enable_thinking=enable_thinking,
+            )
+            splits.append(HFDataset.from_list(rows))
+
+        return HFDatasetDict({"test": concatenate_datasets(splits)})
+
+
+def _stamp(
+    rows: list[dict],
+    *,
+    subtask: str,
+    context_length: int,
+    gen_budget: int,
+    think_budget: int = 0,
+    enable_thinking: bool = False,
+) -> list[dict]:
+    for row in rows:
+        row["subtask"] = subtask
+        row["context_length"] = context_length
+        row["gen_budget"] = gen_budget
+        if enable_thinking:
+            row["think_budget"] = think_budget
+            row["enable_thinking"] = True
+    return rows
diff --git a/sieval/meta/index.json b/sieval/meta/index.json
index 1df783ed..8e119956 100644
--- a/sieval/meta/index.json
+++ b/sieval/meta/index.json
@@ -574,6 +574,34 @@
       "license": "Apache-2.0",
       "checksums": {}
     },
+    {
+      "name": "ruler",
+      "display_name": "RULER",
+      "description": "RULER long-context benchmark: 13 subtasks (NIAH ×8, VT, CWE, FWE, QA ×2).",
+      "source": [
+        "local:paul_graham_essays/PaulGrahamEssays.json.gz",
+        "url:https://media.githubusercontent.com/media/NVIDIA/RULER/ab17b7853df4e0a30b78cd5d2b463ac7dff6ee13/scripts/data/synthetic/json/english_words.json",
+        "url:https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v2.0.json",
+        "hf:hotpotqa/hotpot_qa@1908d6afbbead072334abe2965f91bd2709910ab"
+      ],
+      "categories": [
+        {
+          "level1": "Language",
+          "level2": "SemanticUnderstanding"
+        }
+      ],
+      "tags": [
+        "english",
+        "open-ended",
+        "long-context"
+      ],
+      "deps_group": "ruler",
+      "license": "Apache-2.0",
+      "checksums": {
+        "dev-v2.0.json": "sha256:80a5225e94905956a6446d296ca1093975c4d3b3260f1d6c8f68bc2ab77182d8",
+        "english_words.json": "sha256:affcd6d45fdf3cc843d585c99c97ad615094e760e6c4756b654bab6c73bc2eca"
+      }
+    },
     {
       "name": "simpleqa_verified",
       "display_name": "SimpleQA Verified",
@@ -1313,6 +1341,27 @@
       },
       "status": "stable"
     },
+    {
+      "name": "ruler_0shot_gen",
+      "display_name": "RULER (0-shot, generative)",
+      "description": "RULER long-context benchmark: 13 subtasks (NIAH×8, VT, CWE, FWE, QA×2).",
+      "dataset": "ruler",
+      "eval_mode": "gen",
+      "n_shot": 0,
+      "tags": [
+        "english",
+        "open-ended",
+        "long-context"
+      ],
+      "deps_group": "ruler",
+      "model_type": "chat",
+      "reference_impl": {
+        "source": "NVIDIA/RULER",
+        "url": "https://github.com/NVIDIA/RULER/blob/ab17b7853df4e0a30b78cd5d2b463ac7dff6ee13/scripts/eval/synthetic/constants.py",
+        "notes": "Scoring mirrors RULER's string_match_all (recall) and string_match_part (QA), vendored in community/ruler/eval. Verified byte-exact against NVIDIA/RULER at ab17b78 and reproduces published recall on a Qwen3-8B run (repro table in the PR). Generation is capped per subtask to upstream's tokens_to_generate (via the sample's gen_budget, see infer()), and HotpotQA document ordering matches upstream's sorted(set()). Remaining divergences, each score-neutral:\n1. Paul Graham essay corpus: the BYO generator concatenates essays in the pinned URL-list order, while upstream groups them repo-then-html (glob order within each group), so the full-corpus bytes differ. NIAH/VT needles and QA answers don't depend on filler-text order, so recall is unchanged.\n2. CWE word count is capped at the wonderwords pool, which fills >=98% only at <=32k; at 64k/128k CWE underfills. The stable contract is scoped to context lengths <=32k; 64k/128k are experimental."
+      },
+      "status": "stable"
+    },
     {
       "name": "simpleqa_verified_0shot_gen",
       "display_name": "SimpleQA Verified (0-shot, generative)",
diff --git a/sieval/tasks/__init__.pyi b/sieval/tasks/__init__.pyi
index 96e1cd34..4c470d84 100644
--- a/sieval/tasks/__init__.pyi
+++ b/sieval/tasks/__init__.pyi
@@ -97,6 +97,9 @@ from .mmmlu_kshot_clp import (
 from .openbookqa_kshot_gen import (
     OpenBookQAFewShotGenTask,
 )
+from .ruler_0shot_gen import (
+    RulerZeroShotGenTask,
+)
 from .simpleqa_verified_0shot_gen import (
     SimpleQAVerifiedZeroShotGenTask,
 )
@@ -140,6 +143,7 @@ __all__ = [
     "MMLUZeroShotGenTask",
     "MMMLUKShotClpTask",
     "OpenBookQAFewShotGenTask",
+    "RulerZeroShotGenTask",
     "SimpleQAVerifiedZeroShotGenTask",
     "TEvalBeforeCallingZeroShotGenTask",
     "TheoremQAKShotBaseGenTask",
diff --git a/sieval/tasks/ruler_0shot_gen.py b/sieval/tasks/ruler_0shot_gen.py
new file mode 100644
index 00000000..19a0c95f
--- /dev/null
+++ b/sieval/tasks/ruler_0shot_gen.py
@@ -0,0 +1,206 @@
+"""RULER 0-shot generative task.
+
+Handles all 13 RULER subtasks in a single class. The scoring branch is chosen
+per sample in ``feedback()`` based on ``subtask``:
+
+- recall subtasks (NIAH × 8, VT, CWE, FWE): ``string_match_all``
+- QA subtasks (qa_squad, qa_hotpotqa): ``string_match_part``
+
+``report()`` groups by ``(context_length, subtask)`` to emit:
+- per-cell scores: ``score_{subtask}_{len_tag}``
+- per-length 13-task means: ``score_{len_tag}``
+- overall headline: ``score``
+
+The prompt is fully synthesized in the dataset loader; this task just sends
+it and scores the reply. The RULER answer-cue (``answer_prefix``) is prefilled
+in an assistant turn (when continue_final_message: true is set), validated
+against NVIDIA/RULER at commit ab17b78. An alternative user-message append
+mode is supported for thinking model compatibility.
+
+AI-Generated Code - Claude Opus 4.8 (Anthropic)
+"""
+
+from collections import defaultdict
+from typing import TypedDict
+
+from openai.types.chat import ChatCompletionMessageParam
+
+from sieval.community.ruler.eval.constants import (
+    string_match_all,
+    string_match_part,
+)
+from sieval.core.models import ModelOutput
+from sieval.core.tasks import (
+    EvalMode,
+    ReferenceImpl,
+    Task,
+    sieval_task,
+)
+from sieval.datasets.ruler import RulerDatasetSample, len_tag, thinking_prefill
+
+_QA_SUBTASKS: frozenset[str] = frozenset({"qa_squad", "qa_hotpotqa"})
+
+
+class RulerFeedback(TypedDict):
+    prediction: str
+    references: list[str]
+    subtask: str
+    context_length: int
+
+
+@sieval_task(
+    name="ruler_0shot_gen",
+    display_name="RULER (0-shot, generative)",
+    description=(
+        "RULER long-context benchmark: 13 subtasks (NIAH×8, VT, CWE, FWE, QA×2)."
+    ),
+    eval_mode=EvalMode.GEN,
+    n_shot=0,
+    tags=("english", "open-ended", "long-context"),
+    deps_group="ruler",
+    model_type="chat",
+    reference_impl=ReferenceImpl(
+        source="NVIDIA/RULER",
+        url="https://github.com/NVIDIA/RULER/blob/ab17b7853df4e0a30b78cd5d2b463ac7dff6ee13/scripts/eval/synthetic/constants.py",
+        notes=(
+            "Scoring mirrors RULER's string_match_all (recall) and "
+            "string_match_part (QA), vendored in community/ruler/eval. Verified "
+            "byte-exact against NVIDIA/RULER at ab17b78 and reproduces published "
+            "recall on a Qwen3-8B run (repro table in the PR). Generation is "
+            "capped per subtask to upstream's tokens_to_generate (via the sample's "
+            "gen_budget, see infer()), and HotpotQA document ordering matches "
+            "upstream's sorted(set()). Remaining divergences, each score-neutral:\n"
+            "1. Paul Graham essay corpus: the BYO generator concatenates essays "
+            "in the pinned URL-list order, while upstream groups them repo-then-"
+            "html (glob order within each group), so the full-corpus bytes differ. "
+            "NIAH/VT needles and QA answers don't depend on filler-text order, so "
+            "recall is unchanged.\n"
+            "2. CWE word count is capped at the wonderwords pool, which fills "
+            ">=98% only at <=32k; at 64k/128k CWE underfills. The stable contract "
+            "is scoped to context lengths <=32k; 64k/128k are experimental."
+        ),
+    ),
+)
+class RulerZeroShotGenTask(
+    Task[
+        RulerDatasetSample,
+        list[ChatCompletionMessageParam],
+        ModelOutput,
+        str,
+        RulerFeedback,
+        dict[str, float],
+    ]
+):
+    async def preprocess(self, raw, ctx):  # noqa: ARG002
+        # Support both message patterns:
+        # 1. User-message pattern: answer_prefix appended to user message
+        # 2. Assistant-message pattern: answer_prefix in prefilled assistant turn
+        #
+        # Detection logic:
+        # - If both flags in extra_body → assistant pattern
+        # - Otherwise → user message pattern (default)
+        model_meta = self.model.meta()
+        extra_body: dict = model_meta.get("default_params", {}).get(  # type: ignore[assignment]
+            "extra_body", {}
+        )
+        # Detect prefill mode: both flags must be set explicitly to enable prefill
+        # - continue_final_message=True: continue from assistant's last message
+        # - add_generation_prompt=False: suppress default generation prompt
+        # Both must match for assistant-pattern; otherwise defaults to user-message
+        use_assistant_prefill = extra_body.get(
+            "continue_final_message", False
+        ) and not extra_body.get("add_generation_prompt", True)
+
+        if use_assistant_prefill:
+            # Assistant-message pattern: prefilled turn with thinking placeholder
+            enable_thinking = extra_body.get("enable_thinking", False)
+            prefill = thinking_prefill(model_meta["model"], enable_thinking)
+            assistant_content = f"{prefill}{raw['answer_prefix']}"
+            return [
+                {"role": "user", "content": raw["input"]},
+                {"role": "assistant", "content": assistant_content},
+            ]
+        else:
+            # User-message pattern: answer_prefix appended to user message (default)
+            return [
+                {
+                    "role": "user",
+                    "content": raw["input"] + raw["answer_prefix"],
+                },
+            ]
+
+    async def infer(self, pre, ctx):
+        # Cap generation per subtask, matching NVIDIA/RULER's tokens_to_generate
+        # (128 NIAH / 30 VT / 120 CWE / 50 FWE / 32 QA). The dataset stamps the
+        # per-subtask budget on each sample (`gen_budget` = base + any thinking
+        # overhead), so one class serving all 13 subtasks applies the right cap
+        # without a per-subtask YAML infer_args.
+        max_tokens = ctx.raw_sample["gen_budget"]
+
+        # Qwen3 extended thinking adaptation: allocate max_tokens based on
+        # context_length. (Diverges from upstream RULER's single-budget approach)
+        #
+        # During dataset generation, gen_budget was computed differently based on
+        # context_length (see _shared.py:tokens_to_generate for rationale):
+        # - Small contexts (!=128k): gen_budget excludes think_budget
+        # - Large contexts (128k): gen_budget includes think_budget
+        #
+        # Now at inference, restore think_budget for small contexts where it was
+        # omitted.
+        if (
+            ctx.raw_sample.get("enable_thinking", False)
+            and "think_budget" in ctx.raw_sample
+            and ctx.raw_sample.get("context_length") != 131072
+        ):
+            # Small context: gen_budget didn't account for thinking tokens,
+            # so add think_budget to ensure sufficient generation capacity.
+            max_tokens += ctx.raw_sample["think_budget"]
+        # Large context: gen_budget already includes think_budget, no adjustment needed.
+
+        return await self.model.agenerate(pre, max_tokens=max_tokens)
+
+    async def postprocess(self, inf, ctx):  # noqa: ARG002
+        return inf.texts[0]
+
+    async def feedback(self, post: str, ctx) -> tuple[bool, RulerFeedback]:  # noqa: ARG002
+        return True, {
+            "prediction": post,
+            "references": ctx.raw_sample["outputs"],
+            "subtask": ctx.raw_sample["subtask"],
+            "context_length": ctx.raw_sample["context_length"],
+        }
+
+    async def report(self, finals: list, fails: list) -> dict[str, float | int]:
+        cells: dict[tuple[int, str], list[tuple[str, list[str]]]] = defaultdict(list)
+        for ctx in finals:
+            fb: RulerFeedback = ctx.feedback_result
+            cells[(fb["context_length"], fb["subtask"])].append(
+                (fb["prediction"], fb["references"])
+            )
+
+        cell_scores: dict[tuple[int, str], float] = {}
+        for (ctx_len, subtask), samples in cells.items():
+            preds = [p for p, _ in samples]
+            refs = [r for _, r in samples]
+            score = (
+                string_match_part(preds, refs)
+                if subtask in _QA_SUBTASKS
+                else string_match_all(preds, refs)
+            )
+            cell_scores[(ctx_len, subtask)] = score
+
+        by_length: dict[int, list[float]] = defaultdict(list)
+        for (ctx_len, _), score in cell_scores.items():
+            by_length[ctx_len].append(score)
+        length_means = {ctx_len: sum(s) / len(s) for ctx_len, s in by_length.items()}
+
+        overall = (
+            sum(length_means.values()) / len(length_means) if length_means else 0.0
+        )
+
+        result: dict[str, float | int] = {"score": overall, "fails": len(fails)}
+        for ctx_len, mean_score in sorted(length_means.items()):
+            result[f"score_{len_tag(ctx_len)}"] = mean_score
+        for (ctx_len, subtask), score in sorted(cell_scores.items()):
+            result[f"score_{subtask}_{len_tag(ctx_len)}"] = score
+        return result
diff --git a/tests/unit/cli/dataset/test_commands.py b/tests/unit/cli/dataset/test_commands.py
index 271457d8..67a6482d 100644
--- a/tests/unit/cli/dataset/test_commands.py
+++ b/tests/unit/cli/dataset/test_commands.py
@@ -148,6 +148,9 @@ def test_dataset_download_all_iterates_all_pilots(tmp_path):
     expected_url_sources = sum(
         1 for m in datasets for s in m.source if s.startswith("url:")
     )
+    expected_local_sources = sum(
+        1 for m in datasets for s in m.source if s.startswith("local:")
+    )
 
     with (
         patch(
@@ -157,8 +160,13 @@ def test_dataset_download_all_iterates_all_pilots(tmp_path):
             "sieval.datasets.downloaders.url.URLHandler.is_downloaded",
             return_value=True,
         ) as mock_url_probe,
+        patch(
+            "sieval.datasets.downloaders.local.LocalHandler.is_downloaded",
+            return_value=True,
+        ) as mock_local_probe,
         patch("sieval.datasets.downloaders.hf.HFHandler.download") as mock_hf,
         patch("sieval.datasets.downloaders.url.URLHandler.download") as mock_url,
+        patch("sieval.datasets.downloaders.local.LocalHandler.download") as mock_local,
         patch("sieval.cli.dataset.commands.verify_checksums", return_value=[]),
     ):
         result = runner.invoke(
@@ -169,9 +177,11 @@ def test_dataset_download_all_iterates_all_pilots(tmp_path):
         # Iteration proof: every registered source was probed exactly once.
         assert mock_hf_probe.call_count == expected_hf_sources
         assert mock_url_probe.call_count == expected_url_sources
+        assert mock_local_probe.call_count == expected_local_sources
         # Already-downloaded short-circuit means download() itself stays cold.
         mock_hf.assert_not_called()
         mock_url.assert_not_called()
+        mock_local.assert_not_called()
 
 
 def test_dataset_download_all_aggregates_failures(tmp_path):
@@ -317,12 +327,22 @@ def test_dataset_download_domain_filter(tmp_path):
 
 
 def test_dataset_list_ready_no_when_cache_empty(tmp_path, monkeypatch):
-    """Empty data dir → all rows report ready=no."""
+    """Empty data dir → every dataset with a download source reports ready=no.
+
+    Fully-synthetic datasets (empty ``source``, e.g. RULER vt/cwe/fwe) have
+    nothing to download, so their readiness is decided by deps alone — they are
+    excluded here.
+    """
+    from sieval.meta import load_index
+
     monkeypatch.setenv("SIEVAL_DATA_DIR", str(tmp_path))
     result = runner.invoke(dataset_app, ["list", "-o", "json"])
     assert result.exit_code == 0
     payload = json.loads(result.output)["data"]
-    assert payload and all(row["ready"] == "no" for row in payload)
+    datasets, _ = load_index()
+    with_source = {m.name for m in datasets if m.source}
+    sourced = [row for row in payload if row["name"] in with_source]
+    assert sourced and all(row["ready"] == "no" for row in sourced)
 
 
 def test_dataset_list_ready_yes_only_for_present_dataset(tmp_path, monkeypatch):
diff --git a/tests/unit/core/datasets/test_meta.py b/tests/unit/core/datasets/test_meta.py
index e0191b14..dba42599 100644
--- a/tests/unit/core/datasets/test_meta.py
+++ b/tests/unit/core/datasets/test_meta.py
@@ -472,6 +472,54 @@ def load(self, name_or_path, **kwargs):
                 raise NotImplementedError
 
 
+def test_sieval_dataset_rejects_colliding_local_basenames():
+    """Two local: sources in the same dataset with the same basename stage to
+    the same <dest>/<name>/<basename> and overwrite each other. Reject."""
+
+    class LocalClashSample(TypedDict):
+        x: str
+
+    with pytest.raises(ValueError, match="colliding basenames"):
+
+        @sieval_dataset(
+            name="local_clash_test",
+            display_name="Local Clash Test",
+            description="x",
+            source=(
+                "local:a/data.csv",
+                "local:b/data.csv",
+            ),
+            categories=(Category(Level1Category.LOGIC, "BasicLogic"),),
+        )
+        class LocalClashDataset(Dataset[LocalClashSample]):
+            def load(self, name_or_path, **kwargs):
+                raise NotImplementedError
+
+
+def test_sieval_dataset_rejects_url_local_basename_collision():
+    """A url: and a local: source staging to the same basename overwrite each
+    other under <dest>/<name>/; the guard spans both staged schemes."""
+
+    class CrossSample(TypedDict):
+        x: str
+
+    with pytest.raises(ValueError, match="colliding basenames"):
+
+        @sieval_dataset(
+            name="cross_clash_test",
+            display_name="Cross Clash Test",
+            description="x",
+            source=(
+                "url:https://a.example.com/data.csv",
+                "local:cross_clash/data.csv",
+            ),
+            categories=(Category(Level1Category.LOGIC, "BasicLogic"),),
+        )
+        class CrossClashDataset(Dataset[CrossSample]):
+            def load(self, name_or_path, **kwargs):
+                raise NotImplementedError
+
+
 def test_sieval_dataset_accepts_different_url_basenames():
     """Different basenames should not trigger the validation."""
 
diff --git a/tests/unit/datasets/downloaders/test_local.py b/tests/unit/datasets/downloaders/test_local.py
new file mode 100644
index 00000000..e3b57d1d
--- /dev/null
+++ b/tests/unit/datasets/downloaders/test_local.py
@@ -0,0 +1,66 @@
+"""Tests for the local: source handler (bring-your-own corpus).
+
+AI-Generated Code - Claude Opus 4.8 (1M context) (Anthropic)
+"""
+
+import pytest
+
+from sieval.datasets.downloaders.local import (
+    LocalHandler,
+    LocalSourceUnavailable,
+    _basename,
+)
+
+
+def test_scheme():
+    assert LocalHandler().scheme == "local"
+
+
+def test_strip_scheme_rejects_wrong_scheme():
+    with pytest.raises(ValueError, match="Expected local: scheme"):
+        LocalHandler._strip_scheme("url:https://example.com/foo.json")
+
+
+def test_basename():
+    assert _basename("pg/PaulGrahamEssays.json.gz") == "PaulGrahamEssays.json.gz"
+    assert _basename("trailing/") == "download"
+
+
+def test_download_noop_when_present(tmp_path):
+    """BYO: an already-staged corpus is a no-op, even with force (nothing to
+    re-fetch), and never touches the bytes."""
+    target_dir = tmp_path / "ruler"
+    target_dir.mkdir()
+    (target_dir / "PaulGrahamEssays.json.gz").write_bytes(b"corpus")
+    h = LocalHandler()
+    for force in (False, True):
+        h.download(
+            "local:pg/PaulGrahamEssays.json.gz",
+            dest_root=tmp_path,
+            dataset_name="ruler",
+            force=force,
+        )
+    assert (target_dir / "PaulGrahamEssays.json.gz").read_bytes() == b"corpus"
+
+
+def test_download_raises_with_instructions_when_missing(tmp_path):
+    """BYO: an absent corpus is not silently fetched — it raises with the
+    expected staging path so the caller can tell the user how to produce it."""
+    h = LocalHandler()
+    with pytest.raises(LocalSourceUnavailable, match="bring-your-own") as exc:
+        h.download(
+            "local:pg/PaulGrahamEssays.json.gz",
+            dest_root=tmp_path,
+            dataset_name="ruler",
+            force=False,
+        )
+    assert str(tmp_path / "ruler" / "PaulGrahamEssays.json.gz") in str(exc.value)
+
+
+def test_is_downloaded(tmp_path):
+    h = LocalHandler()
+    assert not h.is_downloaded("local:pg/PaulGrahamEssays.json.gz", tmp_path, "ruler")
+    target_dir = tmp_path / "ruler"
+    target_dir.mkdir()
+    (target_dir / "PaulGrahamEssays.json.gz").write_text("x")
+    assert h.is_downloaded("local:pg/PaulGrahamEssays.json.gz", tmp_path, "ruler")
diff --git a/tests/unit/datasets/test_ruler.py b/tests/unit/datasets/test_ruler.py
new file mode 100644
index 00000000..e74cafc1
--- /dev/null
+++ b/tests/unit/datasets/test_ruler.py
@@ -0,0 +1,309 @@
+"""Tests for sieval/datasets/ruler.py — RulerDataset and module-level helpers.
+
+FWE is the only subtask that needs no external data files (source=()) so it is
+used as the integration smoke test.  Integration tests require the ruler deps
+group (tiktoken / wonderwords / scipy) — they are skipped when unavailable.
+"""
+
+import pytest
+
+from sieval.datasets.ruler import tokens_to_generate
+
+try:
+    import tiktoken as _tiktoken  # noqa: F401
+
+    _ruler_deps = True
+except ImportError:
+    _ruler_deps = False
+
+_needs_ruler_deps = pytest.mark.skipif(
+    not _ruler_deps, reason="ruler deps group not installed"
+)
+
+if _ruler_deps:
+    from sieval.datasets.ruler import RulerDataset, RulerDatasetSample, _stamp
+    from sieval.datasets.ruler.ruler import _subtask_data_path
+
+
+# ---------------------------------------------------------------------------
+# _subtask_data_path — single-subtask staging path (regression)
+# ---------------------------------------------------------------------------
+
+
+@_needs_ruler_deps
+def test_subtask_data_path_appends_ruler_subdir_for_staged_subtasks():
+    # Regression: a direct single-subtask load must resolve the same ``/ruler``
+    # staging path the "all"/list branches use. Previously only the aggregate
+    # branches appended it, so a direct essay/squad subtask hit FileNotFoundError.
+    for st in ("niah_single_2", "niah_multikey_1", "vt", "cwe", "qa_squad"):
+        assert _subtask_data_path("/data", st) == "/data/ruler"
+
+
+@_needs_ruler_deps
+def test_subtask_data_path_uses_base_dir_for_synthetic_and_hf():
+    # FWE is fully synthetic and qa_hotpotqa is fetched from HF → base data dir.
+    assert _subtask_data_path("/data", "fwe") == "/data"
+    assert _subtask_data_path("/data", "qa_hotpotqa") == "/data"
+
+
+@_needs_ruler_deps
+def test_single_subtask_load_applies_ruler_subdir_to_loader(monkeypatch):
+    # Regression: a direct single-subtask load must pass the /ruler-suffixed path
+    # to the loader. Previously only the all/list branches appended it, so a
+    # direct essay/squad subtask read the base dir → FileNotFoundError. Mock the
+    # loader to capture the path without needing the staged corpus.
+    import sieval.datasets.ruler.ruler as ruler_mod
+
+    captured: dict[str, str] = {}
+
+    def fake_load_niah(path, **_kwargs):
+        captured["path"] = path
+        return []
+
+    monkeypatch.setattr(ruler_mod, "load_niah", fake_load_niah)
+    ds = RulerDataset(".", subtask="niah_single_2", max_seq_length=512, num_samples=1)
+    assert ds.test_set is not None  # force the load
+    assert captured["path"] == "./ruler"
+
+
+@_needs_ruler_deps
+def test_single_subtask_load_uses_base_dir_for_synthetic(monkeypatch):
+    # FWE reads the base dir (no /ruler subdir) — the mirror of the case above.
+    import sieval.datasets.ruler.ruler as ruler_mod
+
+    captured: dict[str, str] = {}
+
+    def fake_load_fwe(path, **_kwargs):
+        captured["path"] = path
+        return []
+
+    monkeypatch.setattr(ruler_mod, "load_fwe", fake_load_fwe)
+    ds = RulerDataset(".", subtask="fwe", max_seq_length=512, num_samples=1)
+    assert ds.test_set is not None
+    assert captured["path"] == "."
+
+
+# ---------------------------------------------------------------------------
+# _read_hotpotqa — staged-copy-first path resolution (regression)
+# ---------------------------------------------------------------------------
+
+
+def _fake_hotpotqa_dataset():
+    # Minimal shape _read_hotpotqa consumes: nested `context` with title/sentences,
+    # plus `question`/`answer`.
+    from datasets import Dataset as HFDataset
+
+    return HFDataset.from_list(
+        [
+            {
+                "question": "Q?",
+                "answer": "A",
+                "context": {"title": ["T1", "T2"], "sentences": [["s1."], ["s2."]]},
+            }
+        ]
+    )
+
+
+@_needs_ruler_deps
+def test_read_hotpotqa_reads_staged_copy_and_skips_online(monkeypatch):
+    # Regression: qa_hotpotqa is routed to the base data dir, so the loader must
+    # join the HF repo id onto it and read the pinned staged mirror. Previously it
+    # passed the bare base dir to load_dataset, which raised (not a distractor-config
+    # dataset) and silently fell back to an online fetch — dead staged download,
+    # broken under HF_HUB_OFFLINE=1.
+    import datasets
+
+    from sieval.datasets.ruler._qa import _read_hotpotqa
+    from sieval.datasets.ruler._shared import _HOTPOTQA_REPO_ID
+
+    calls: list[dict] = []
+
+    def fake_load_dataset(path, config=None, *, revision=None, **_):
+        calls.append({"path": path, "config": config, "revision": revision})
+        return _fake_hotpotqa_dataset()
+
+    monkeypatch.setattr(datasets, "load_dataset", fake_load_dataset)
+
+    qas, docs = _read_hotpotqa("/data")
+
+    assert len(calls) == 1  # online fallback never touched
+    assert calls[0]["path"] == f"/data/{_HOTPOTQA_REPO_ID}"
+    assert calls[0]["config"] == "distractor"
+    assert calls[0]["revision"] is None  # local staged read, not a pinned online pull
+    assert qas and docs
+
+
+@_needs_ruler_deps
+def test_read_hotpotqa_falls_back_online_when_staged_absent(monkeypatch):
+    # Online remains an explicit last resort: only when the staged copy is missing.
+    import datasets
+
+    from sieval.datasets.ruler._qa import _read_hotpotqa
+    from sieval.datasets.ruler._shared import _HOTPOTQA_REPO_ID, _HOTPOTQA_REVISION
+
+    calls: list[dict] = []
+
+    def fake_load_dataset(path, *_args, revision=None, **_):
+        calls.append({"path": path, "revision": revision})
+        if revision is None:  # staged read
+            raise FileNotFoundError(path)
+        return _fake_hotpotqa_dataset()
+
+    monkeypatch.setattr(datasets, "load_dataset", fake_load_dataset)
+
+    _read_hotpotqa("/data")
+
+    assert len(calls) == 2
+    assert calls[0]["path"] == f"/data/{_HOTPOTQA_REPO_ID}"
+    assert calls[1]["path"] == _HOTPOTQA_REPO_ID
+    assert calls[1]["revision"] == _HOTPOTQA_REVISION
+
+
+# ---------------------------------------------------------------------------
+# tokens_to_generate helper
+# ---------------------------------------------------------------------------
+
+
+# QWEN3_THINKING_TAG_OVERHEAD (4 tokens) is added only for qwen3 in thinking mode
+# (the model generates the <think>...</think> tags). Non-thinking prefills an empty
+# block in the prompt template, so no tag overhead in the generation budget.
+@pytest.mark.parametrize(
+    ("task_name", "enable_thinking", "think_budget", "model_name", "expected"),
+    [
+        # Non-thinking: base answer tokens only (empty think block is prefilled).
+        ("niah", False, 0, "qwen3", 128),
+        ("qa", False, 0, "qwen3", 32),
+        ("variable_tracking", False, 0, "qwen3", 30),
+        ("common_words_extraction", False, 0, "qwen3", 120),
+        ("freq_words_extraction", False, 0, "qwen3", 50),
+        # Non-thinking is unaffected by a stray think_budget.
+        ("niah", False, 8192, "qwen3", 128),
+        # qwen3 thinking: tag overhead (4) + think_budget + base.
+        ("niah", True, 1024, "qwen3", 4 + 1024 + 128),
+        ("qa", True, 512, "qwen3", 4 + 512 + 32),
+        ("niah", True, 0, "qwen3", 4 + 0 + 128),
+        # Non-qwen3 thinking: think_budget + base, no tag overhead.
+        ("niah", True, 1024, "gpt-4", 1024 + 128),
+        ("variable_tracking", True, 2048, "llama", 2048 + 30),
+    ],
+)
+def test_tokens_to_generate(
+    task_name, enable_thinking, think_budget, model_name, expected
+):
+    """tokens_to_generate is the max_tokens budget: it must include think_budget."""
+    assert (
+        tokens_to_generate(
+            task_name,
+            enable_thinking=enable_thinking,
+            think_budget=think_budget,
+            model_name=model_name,
+        )
+        == expected
+    )
+
+
+# ---------------------------------------------------------------------------
+# _stamp helper
+# ---------------------------------------------------------------------------
+
+
+@_needs_ruler_deps
+def test_stamp_adds_subtask_and_context_length():
+    rows = [
+        {
+            "index": 0,
+            "input": "x",
+            "outputs": ["y"],
+            "length": 10,
+            "answer_prefix": "A:",
+        }
+    ]
+    stamped = _stamp(rows, subtask="vt", context_length=8192, gen_budget=30)
+    assert stamped[0]["subtask"] == "vt"
+    assert stamped[0]["context_length"] == 8192
+    assert stamped[0]["gen_budget"] == 30
+
+
+@_needs_ruler_deps
+def test_stamp_preserves_existing_fields():
+    rows = [
+        {
+            "index": 7,
+            "input": "q",
+            "outputs": ["a"],
+            "length": 5,
+            "answer_prefix": "Answer:",
+        }
+    ]
+    result = _stamp(rows, subtask="cwe", context_length=4096, gen_budget=120)
+    assert result[0]["index"] == 7
+    assert result[0]["outputs"] == ["a"]
+
+
+# ---------------------------------------------------------------------------
+# RulerDataset.load() — FWE subtask (no external data needed)
+# ---------------------------------------------------------------------------
+
+
+@_needs_ruler_deps
+def test_fwe_load_emits_required_fields():
+    ds = RulerDataset(".", subtask="fwe", max_seq_length=512, num_samples=3)
+    assert ds.test_set is not None
+    rows = list(ds.test_set)
+    assert len(rows) == 3
+    for r in rows:
+        assert "subtask" in r and r["subtask"] == "fwe"
+        assert "context_length" in r and r["context_length"] == 512
+        assert "input" in r and r["input"]
+        assert "outputs" in r and len(r["outputs"]) == 3
+        assert "answer_prefix" in r
+        assert "length" in r and r["length"] > 0
+
+
+@_needs_ruler_deps
+def test_fwe_load_is_deterministic():
+    ds1 = RulerDataset(
+        ".", subtask="fwe", max_seq_length=512, num_samples=2, random_seed=42
+    )
+    ds2 = RulerDataset(
+        ".", subtask="fwe", max_seq_length=512, num_samples=2, random_seed=42
+    )
+    assert ds1.test_set is not None and ds2.test_set is not None
+    first = list(ds1.test_set)
+    second = list(ds2.test_set)
+    assert first[0]["input"] == second[0]["input"]
+    assert first[0]["outputs"] == second[0]["outputs"]
+
+
+@_needs_ruler_deps
+def test_fwe_no_token_position_answer_field():
+    ds = RulerDataset(".", subtask="fwe", max_seq_length=512, num_samples=2)
+    assert ds.test_set is not None
+    rows = list(ds.test_set)
+    for r in rows:
+        assert "token_position_answer" not in r
+
+
+# ---------------------------------------------------------------------------
+# Schema validation via TypedDict keys
+# ---------------------------------------------------------------------------
+
+
+@_needs_ruler_deps
+def test_fwe_sample_satisfies_required_schema():
+    ds = RulerDataset(".", subtask="fwe", max_seq_length=512, num_samples=1)
+    assert ds.test_set is not None
+    row = list(ds.test_set)[0]
+    missing = set(RulerDatasetSample.__required_keys__) - set(row.keys())
+    assert not missing, f"Missing required fields: {missing}"
+
+
+# ---------------------------------------------------------------------------
+# Unknown subtask
+# ---------------------------------------------------------------------------
+
+
+@_needs_ruler_deps
+def test_unknown_subtask_raises():
+    with pytest.raises(ValueError, match="Unknown subtask"):
+        RulerDataset(".", subtask="nonexistent_task", max_seq_length=512, num_samples=1)
diff --git a/tests/unit/datasets/test_ruler_shared.py b/tests/unit/datasets/test_ruler_shared.py
new file mode 100644
index 00000000..78eea6f4
--- /dev/null
+++ b/tests/unit/datasets/test_ruler_shared.py
@@ -0,0 +1,378 @@
+"""Tests for sieval/datasets/ruler/_shared.py — token calculation and templates.
+
+Tests the unified token calculation system:
+- get_template(): Message format template selection
+- model_template_token(): upstream per-config template reserve
+- tokens_to_generate(): generation budget (answer + any think_budget)
+
+model_template_token() consumes the RULER tokenizer wrappers
+(HFTokenizer / OpenAITokenizer) via their ``text_to_tokens`` interface, so
+tests build one through ``select_tokenizer`` — the same path the loaders use.
+Loaders size prompts as ``len(text_to_tokens(content)) + model_template_token``.
+"""
+
+import pytest
+
+try:
+    import tiktoken as _tiktoken  # noqa: F401
+
+    _ruler_deps = True
+except ImportError:
+    _ruler_deps = False
+
+_needs_ruler_deps = pytest.mark.skipif(
+    not _ruler_deps, reason="ruler deps group not installed"
+)
+
+if _ruler_deps:
+    from sieval.community.ruler.scripts.tokenizer import select_tokenizer
+    from sieval.datasets.ruler._shared import (
+        get_template,
+        model_template_token,
+        tokens_to_generate,
+    )
+
+
+@pytest.fixture
+def tokenizer():
+    """RULER OpenAI tokenizer wrapper (cl100k_base) — no model download needed."""
+    return select_tokenizer("openai", "cl100k_base")
+
+
+# ---------------------------------------------------------------------------
+# get_template() — Template selection by model and thinking mode
+# ---------------------------------------------------------------------------
+
+
+@_needs_ruler_deps
+def test_get_template_qwen3_nonthinking():
+    """qwen3 non-thinking mode uses nonthinking template with empty think block."""
+    template = get_template("qwen3", enable_thinking=False)
+    assert "<|im_start|>user" in template
+    assert "<think>" in template  # Empty think block to skip reasoning
+    assert "<|im_end|>" in template
+    assert "{task_template}" in template
+
+
+@_needs_ruler_deps
+def test_get_template_qwen3_thinking():
+    """qwen3 thinking mode opens the assistant turn WITHOUT a prefilled block.
+
+    In thinking mode the model generates its own <think>...</think>, so the
+    template must NOT prefill an empty block (that is the non-thinking behavior).
+    """
+    template = get_template("qwen3", enable_thinking=True)
+    assert "<|im_start|>user" in template
+    assert "<|im_start|>assistant" in template
+    assert "{task_template}" in template
+    # Distinguishing feature vs non-thinking: no prefilled closed think block.
+    assert "</think>" not in template
+    # Non-thinking, by contrast, DOES prefill the empty block.
+    assert "</think>" in get_template("qwen3", enable_thinking=False)
+
+
+@_needs_ruler_deps
+def test_get_template_other_models():
+    """Other models use their named template from Templates if available."""
+    template = get_template("meta-llama3", enable_thinking=False)
+    assert "<|begin_of_text|>" in template
+    assert "{task_template}" in template
+
+
+@_needs_ruler_deps
+def test_get_template_unknown_model_uses_base():
+    """Unknown models default to the passthrough base template."""
+    template = get_template("completely-unknown-model", enable_thinking=False)
+    assert template == "{task_template}"
+
+
+@_needs_ruler_deps
+def test_get_template_qwen3_case_insensitive_and_not_base():
+    """Regression: qwen3 must resolve to the real template regardless of the
+
+    capitalization used for the key in template.py ("Qwen3-nonthinking"). A
+    case mismatch previously fell back to base, counting ZERO template overhead
+    and overflowing max_seq_length. Resolution is case-insensitive and never base.
+    """
+    for name in ("qwen3", "Qwen3-8b", "QWEN3-8B"):
+        for thinking in (False, True):
+            tmpl = get_template(name, thinking)
+            assert tmpl != "{task_template}"
+            assert "<|im_start|>" in tmpl
+
+
+@_needs_ruler_deps
+def test_get_template_qwen3_is_sieval_owned_not_vendored(monkeypatch):
+    """Qwen3 templates are sieval-owned, independent of the vendored Templates.
+
+    Regression for the community/ vendor-fidelity fix: even if the vendored
+    upstream Templates dict has no Qwen3 entry (as upstream indeed doesn't),
+    get_template still resolves Qwen3 from the local _QWEN3_TEMPLATES.
+    """
+    import sieval.datasets.ruler._shared as shared_mod
+
+    monkeypatch.setattr(shared_mod, "Templates", {"base": "{task_template}"})
+    for thinking in (False, True):
+        tmpl = get_template("qwen3", enable_thinking=thinking)
+        assert "<|im_start|>" in tmpl and "{task_template}" in tmpl
+
+
+@_needs_ruler_deps
+def test_template_has_task_template_placeholder():
+    """Every resolved template must carry the {task_template} placeholder."""
+    for model in ["qwen3", "meta-llama3", "unknown-model"]:
+        for thinking in [True, False]:
+            template = get_template(model, thinking)
+            assert "{task_template}" in template, (
+                f"Template for {model} (thinking={thinking}) missing placeholder"
+            )
+
+
+# ---------------------------------------------------------------------------
+# tokens_to_generate() — Base answer generation budget only
+# ---------------------------------------------------------------------------
+
+
+@_needs_ruler_deps
+@pytest.mark.parametrize(
+    ("task_name", "expected_base"),
+    [
+        ("niah", 128),
+        ("qa", 32),
+        ("variable_tracking", 30),
+        ("common_words_extraction", 120),
+        ("freq_words_extraction", 50),
+    ],
+)
+def test_tokens_to_generate_nonthinking_is_base(task_name, expected_base):
+    """Non-thinking generation budget is just the answer base."""
+    assert (
+        tokens_to_generate(
+            task_name, enable_thinking=False, think_budget=0, model_name="qwen3"
+        )
+        == expected_base
+    )
+
+
+@_needs_ruler_deps
+def test_tokens_to_generate_qwen3_thinking_includes_budget_and_tags():
+    """qwen3 thinking budget = tag overhead + think_budget + base (the max_tokens)."""
+    budget = tokens_to_generate(
+        "niah", enable_thinking=True, think_budget=8192, model_name="qwen3"
+    )
+    assert budget == 4 + 8192 + 128
+
+
+@_needs_ruler_deps
+def test_tokens_to_generate_non_qwen3_thinking_no_tag_overhead():
+    """Non-qwen3 thinking = think_budget + base (no <think> tag generation)."""
+    budget = tokens_to_generate(
+        "niah", enable_thinking=True, think_budget=8192, model_name="gpt-4"
+    )
+    assert budget == 8192 + 128
+
+
+# ---------------------------------------------------------------------------
+# tokens_to_generate() — Qwen3 extended thinking: dataset vs inference split
+# ---------------------------------------------------------------------------
+
+
+@_needs_ruler_deps
+def test_tokens_to_generate_qwen3_small_context_dataset_no_think_budget():
+    """Small context (4k) dataset generation: skip think_budget in gen_budget."""
+    budget = tokens_to_generate(
+        "niah",
+        enable_thinking=True,
+        think_budget=8192,
+        model_name="qwen3",
+        context_length=4096,
+        for_dataset=True,
+    )
+    # Only tag overhead + base, not think_budget
+    assert budget == 4 + 128
+
+
+@_needs_ruler_deps
+def test_tokens_to_generate_qwen3_32k_context_dataset_no_think_budget():
+    """32k dataset generation: under the 128k-only rule 32k is treated as small,
+    so think_budget is NOT reserved in gen_budget (added later at inference)."""
+    budget = tokens_to_generate(
+        "niah",
+        enable_thinking=True,
+        think_budget=8192,
+        model_name="qwen3",
+        context_length=32768,
+        for_dataset=True,
+    )
+    # Only tag overhead + base, not think_budget
+    assert budget == 4 + 128
+
+
+@_needs_ruler_deps
+def test_tokens_to_generate_qwen3_128k_context_dataset_with_think_budget():
+    """Large context (128k) dataset generation: reserve think_budget upfront."""
+    budget = tokens_to_generate(
+        "niah",
+        enable_thinking=True,
+        think_budget=8192,
+        model_name="qwen3",
+        context_length=131072,
+        for_dataset=True,
+    )
+    assert budget == 4 + 8192 + 128
+
+
+@_needs_ruler_deps
+def test_tokens_to_generate_qwen3_small_context_inference_with_think_budget():
+    """Small context (4k) inference: add think_budget (omitted in dataset gen)."""
+    budget = tokens_to_generate(
+        "niah",
+        enable_thinking=True,
+        think_budget=8192,
+        model_name="qwen3",
+        context_length=4096,
+        for_dataset=False,
+    )
+    # Tag overhead + think_budget + base (same as 32k case, think_budget is included)
+    assert budget == 4 + 8192 + 128
+
+
+@_needs_ruler_deps
+def test_tokens_to_generate_qwen3_32k_context_inference_with_think_budget():
+    """32k inference: think_budget is always included at inference — 32k gen
+    omitted it under the 128k-only rule, so it is added on top here."""
+    budget = tokens_to_generate(
+        "niah",
+        enable_thinking=True,
+        think_budget=8192,
+        model_name="qwen3",
+        context_length=32768,
+        for_dataset=False,
+    )
+    # Tag overhead + think_budget + base
+    assert budget == 4 + 8192 + 128
+
+
+@_needs_ruler_deps
+def test_tokens_to_generate_gpt_small_context_dataset():
+    """Non-Qwen3 small context dataset: no tag overhead, no think_budget."""
+    budget = tokens_to_generate(
+        "niah",
+        enable_thinking=True,
+        think_budget=8192,
+        model_name="gpt-4",
+        context_length=4096,
+        for_dataset=True,
+    )
+    # Only base, no tags, no think_budget
+    assert budget == 128
+
+
+@_needs_ruler_deps
+def test_tokens_to_generate_gpt_32k_context_dataset():
+    """Non-Qwen3 32k dataset generation: under the 128k-only rule 32k is treated
+    as small — no tag overhead, no think_budget (added later at inference)."""
+    budget = tokens_to_generate(
+        "niah",
+        enable_thinking=True,
+        think_budget=8192,
+        model_name="gpt-4",
+        context_length=32768,
+        for_dataset=True,
+    )
+    # Only base, no tags, no think_budget
+    assert budget == 128
+
+
+# ---------------------------------------------------------------------------
+# model_template_token() — upstream per-config template reserve
+# ---------------------------------------------------------------------------
+
+
+@_needs_ruler_deps
+def test_model_template_token_qwen3_nonzero(tokenizer):
+    """Regression: the qwen3 template reserve must be non-zero.
+
+    The overflow bug was a silent base fallback (empty template) that made this
+    reserve 0, so prompts were sized ~13 tokens too large and overran context.
+    """
+    reserve = model_template_token(tokenizer, model_name="qwen3", enable_thinking=False)
+    # role markers + prefilled <think></think> block
+    assert reserve >= 10
+
+
+@_needs_ruler_deps
+def test_model_template_token_equals_raw_template_count(tokenizer):
+    """Matches upstream: len(text_to_tokens(RAW template incl {task_template}))."""
+    for thinking in (False, True):
+        reserve = model_template_token(
+            tokenizer, model_name="qwen3", enable_thinking=thinking
+        )
+        raw = get_template("qwen3", thinking)  # placeholder NOT replaced
+        assert reserve == len(tokenizer.text_to_tokens(raw))
+
+
+@_needs_ruler_deps
+def test_model_template_token_unknown_model_is_base(tokenizer):
+    """Unknown model → base template reserve = len(tokenize("{task_template}"))."""
+    reserve = model_template_token(
+        tokenizer, model_name="unknown", enable_thinking=False
+    )
+    assert reserve == len(tokenizer.text_to_tokens("{task_template}"))
+
+
+# ---------------------------------------------------------------------------
+# Integration: content + template reserve + generation budget fits max_seq_length
+# ---------------------------------------------------------------------------
+
+
+def _sizing(tokenizer, prompt, *, model_name, enable_thinking, think_budget):
+    """Loader sizing: content + template reserve + generation budget."""
+    reserve = model_template_token(
+        tokenizer, model_name=model_name, enable_thinking=enable_thinking
+    )
+    gen = tokens_to_generate(
+        "niah",
+        enable_thinking=enable_thinking,
+        think_budget=think_budget,
+        model_name=model_name,
+    )
+    return len(tokenizer.text_to_tokens(prompt)) + reserve + gen
+
+
+@_needs_ruler_deps
+def test_full_budget_nonthinking_fits(tokenizer):
+    """content + reserve + answer budget stays within a modest max_seq_length."""
+    max_seq_length = 4096
+    prompt = "Context sentence. " * 100
+    total = _sizing(
+        tokenizer, prompt, model_name="qwen3", enable_thinking=False, think_budget=0
+    )
+    assert total <= max_seq_length
+
+
+@_needs_ruler_deps
+def test_full_budget_thinking_reserves_think_budget(tokenizer):
+    """The think_budget enters the sizing total via tokens_to_generate, not the
+
+    template reserve — enabling thinking must add think_budget (+ qwen3 tag
+    overhead) of room beyond the non-thinking sizing.
+    """
+    think_budget = 2048
+    prompt = "Context sentence. " * 100
+    thinking = _sizing(
+        tokenizer,
+        prompt,
+        model_name="qwen3",
+        enable_thinking=True,
+        think_budget=think_budget,
+    )
+    nonthinking = _sizing(
+        tokenizer, prompt, model_name="qwen3", enable_thinking=False, think_budget=0
+    )
+    # Difference is think_budget + the 4-token generated <think></think> tags,
+    # adjusted by the small template delta between the two qwen3 templates.
+    template_delta = model_template_token(
+        tokenizer, model_name="qwen3", enable_thinking=True
+    ) - model_template_token(tokenizer, model_name="qwen3", enable_thinking=False)
+    assert thinking - nonthinking == think_budget + 4 + template_delta
diff --git a/tests/unit/tasks/test_ruler_0shot_gen.py b/tests/unit/tasks/test_ruler_0shot_gen.py
new file mode 100644
index 00000000..8409d47f
--- /dev/null
+++ b/tests/unit/tasks/test_ruler_0shot_gen.py
@@ -0,0 +1,346 @@
+"""Test RULER unified implementation supporting all model scenarios."""
+
+import asyncio
+from types import SimpleNamespace
+from unittest.mock import Mock
+
+from sieval.datasets.ruler._shared import thinking_prefill, tokens_to_generate
+from sieval.tasks.ruler_0shot_gen import RulerZeroShotGenTask
+
+
+def _final(context_length, subtask, prediction, references):
+    """Build a minimal `finals` entry carrying only what report() reads."""
+    return SimpleNamespace(
+        feedback_result={
+            "prediction": prediction,
+            "references": references,
+            "subtask": subtask,
+            "context_length": context_length,
+        }
+    )
+
+
+class TestTokensToGenerate:
+    """Test token budget calculation for all model scenarios."""
+
+    def test_qwen3_with_thinking(self):
+        """Qwen3 + thinking: overhead + budget + base."""
+        # 4 (overhead) + 5000 (budget) + 128 (base)
+        result = tokens_to_generate(
+            "niah",
+            enable_thinking=True,
+            think_budget=5000,
+            model_name="Qwen3-8b",
+        )
+        assert result == 5132
+
+    def test_qwen3_without_thinking(self):
+        """Qwen3 without thinking: base only.
+
+        The empty <think></think> block is prefilled in the prompt template
+        (reserved via model_template_token), not generated — so the generation
+        budget is just the answer base.
+        """
+        result = tokens_to_generate(
+            "niah",
+            enable_thinking=False,
+            think_budget=0,
+            model_name="Qwen3-8b",
+        )
+        assert result == 128
+
+    def test_other_model_with_thinking(self):
+        """Non-Qwen3 with thinking: budget + base (no overhead)."""
+        # 3000 (budget) + 128 (base)
+        result = tokens_to_generate(
+            "niah",
+            enable_thinking=True,
+            think_budget=3000,
+            model_name="gpt-4",
+        )
+        assert result == 3128
+
+    def test_other_model_without_thinking(self):
+        """Non-Qwen3 without thinking: just base."""
+        # 128 (base)
+        result = tokens_to_generate(
+            "niah",
+            enable_thinking=False,
+            think_budget=0,
+            model_name="gpt-4",
+        )
+        assert result == 128
+
+    def test_case_insensitive_model_detection(self):
+        """Model name detection is case-insensitive."""
+        # QWEN3 (uppercase) should also work
+        result = tokens_to_generate(
+            "niah",
+            enable_thinking=False,
+            think_budget=0,
+            model_name="QWEN3-8B",
+        )
+        assert result == 128  # non-thinking → base only
+
+
+class TestThinkingPrefill:
+    """Test thinking placeholder generation for message patterns."""
+
+    def test_qwen3_without_thinking_returns_empty_block(self):
+        """Qwen3 without thinking returns empty block to skip reasoning."""
+        result = thinking_prefill("Qwen3-8b", enable_thinking=False)
+        assert result == "<think>\n\n</think>\n\n"
+
+    def test_qwen3_with_thinking_returns_empty_string(self):
+        """Qwen3 with thinking returns empty (model continues in block)."""
+        result = thinking_prefill("Qwen3-8b", enable_thinking=True)
+        assert result == ""
+
+    def test_other_model_returns_empty_string(self):
+        """Non-Qwen3 models always return empty string."""
+        for model in ["gpt-4", "llama-3", "claude-3"]:
+            assert thinking_prefill(model, enable_thinking=True) == ""
+            assert thinking_prefill(model, enable_thinking=False) == ""
+
+    def test_case_insensitive_model_detection_in_prefill(self):
+        """Model detection is case-insensitive in prefill."""
+        result = thinking_prefill("QWEN3-8B", enable_thinking=False)
+        assert result == "<think>\n\n</think>\n\n"
+
+
+class TestMessageModes:
+    """Test automatic message mode detection and construction."""
+
+    def test_user_message_mode_default(self):
+        """Default mode: answer_prefix appended to user message."""
+        import asyncio
+
+        task = Mock(spec=RulerZeroShotGenTask)
+        task.model = Mock()
+        task.model.meta.return_value = {
+            "model": "Qwen3-8b",
+            "default_params": {
+                "extra_body": {
+                    "continue_final_message": False,
+                    "add_generation_prompt": True,
+                }
+            },
+        }
+
+        raw = {"input": "Context here.", "answer_prefix": "Answer: "}
+
+        messages = asyncio.run(RulerZeroShotGenTask.preprocess(task, raw, None))
+
+        assert len(messages) == 1
+        assert messages[0]["role"] == "user"
+        assert messages[0]["content"] == "Context here.Answer: "
+
+    def test_assistant_message_mode_with_thinking_disabled(self):
+        """Assistant mode: prefilled assistant turn with thinking_prefill."""
+        import asyncio
+
+        task = Mock(spec=RulerZeroShotGenTask)
+        task.model = Mock()
+        task.model.meta.return_value = {
+            "model": "Qwen3-8b",
+            "default_params": {
+                "extra_body": {
+                    "enable_thinking": False,
+                    "continue_final_message": True,
+                    "add_generation_prompt": False,
+                }
+            },
+        }
+
+        raw = {"input": "Context here.", "answer_prefix": "Answer: "}
+
+        messages = asyncio.run(RulerZeroShotGenTask.preprocess(task, raw, None))
+
+        assert len(messages) == 2
+        assert messages[0]["role"] == "user"
+        assert messages[0]["content"] == "Context here."
+        assert messages[1]["role"] == "assistant"
+        # Should include thinking_prefill + answer_prefix
+        assert messages[1]["content"] == "<think>\n\n</think>\n\nAnswer: "
+
+    def test_assistant_message_mode_with_thinking_enabled(self):
+        """Assistant mode with thinking: prefill returns empty string."""
+        import asyncio
+
+        task = Mock(spec=RulerZeroShotGenTask)
+        task.model = Mock()
+        task.model.meta.return_value = {
+            "model": "Qwen3-8b",
+            "default_params": {
+                "extra_body": {
+                    "enable_thinking": True,
+                    "continue_final_message": True,
+                    "add_generation_prompt": False,
+                }
+            },
+        }
+
+        raw = {"input": "Context here.", "answer_prefix": "Answer: "}
+
+        messages = asyncio.run(RulerZeroShotGenTask.preprocess(task, raw, None))
+
+        assert len(messages) == 2
+        assert messages[1]["role"] == "assistant"
+        # Empty prefill + answer_prefix
+        assert messages[1]["content"] == "Answer: "
+
+    def test_default_extra_body_missing(self):
+        """Default behavior when extra_body is missing."""
+        import asyncio
+
+        task = Mock(spec=RulerZeroShotGenTask)
+        task.model = Mock()
+        task.model.meta.return_value = {
+            "model": "gpt-4",
+            "default_params": {},  # No extra_body
+        }
+
+        raw = {"input": "Context.", "answer_prefix": "Q: "}
+
+        messages = asyncio.run(RulerZeroShotGenTask.preprocess(task, raw, None))
+
+        # Should default to user-message mode
+        assert len(messages) == 1
+        assert messages[0]["role"] == "user"
+        assert messages[0]["content"] == "Context.Q: "
+
+
+class TestInferCap:
+    """infer() caps generation at the sample's per-subtask gen_budget."""
+
+    def test_infer_passes_gen_budget_as_max_tokens(self):
+        from unittest.mock import AsyncMock
+
+        task = Mock(spec=RulerZeroShotGenTask)
+        task.model = Mock()
+        task.model.agenerate = AsyncMock(return_value="out")
+        ctx = SimpleNamespace(raw_sample={"gen_budget": 32})
+
+        result = asyncio.run(RulerZeroShotGenTask.infer(task, ["msg"], ctx))
+
+        assert result == "out"
+        task.model.agenerate.assert_awaited_once_with(["msg"], max_tokens=32)
+
+
+class TestReport:
+    """Test report() score aggregation: cell → per-length mean → mean-of-means."""
+
+    def test_mean_of_means_weights_lengths_equally(self):
+        """Headline `score` averages per-length means, not raw cells/samples.
+
+        Layout (one sample per cell, string_match_all → 100 hit / 0 miss):
+          - 4k: niah=100, vt=0  → length mean 50.0
+          - 8k: niah=0          → length mean 0.0
+        Mean-of-means = (50 + 0) / 2 = 25.0. A flat mean over the three cells
+        would be (100 + 0 + 0) / 3 = 33.33 — so 25.0 discriminates the two.
+        """
+        finals = [
+            _final(4096, "niah_single_1", "the answer is cat", ["cat"]),
+            _final(4096, "vt", "wrong", ["dog"]),
+            _final(8192, "niah_single_1", "wrong", ["cat"]),
+        ]
+
+        result = asyncio.run(RulerZeroShotGenTask.report(Mock(), finals, []))
+
+        assert result["score"] == 25.0
+        assert result["score_4k"] == 50.0
+        assert result["score_8k"] == 0.0
+        assert result["score_niah_single_1_4k"] == 100.0
+        assert result["score_vt_4k"] == 0.0
+        assert result["score_niah_single_1_8k"] == 0.0
+        assert result["fails"] == 0
+
+    def test_qa_subtasks_use_part_match(self):
+        """QA cells score with string_match_part (any ref hit), not _all.
+
+        Prediction contains one of two references:
+          - string_match_part → max(1, 0) = 100.0
+          - string_match_all  → (1 + 0) / 2 = 50.0
+        A single QA cell makes `score` equal the cell score, so 100.0 proves
+        the part-match branch is taken for QA subtasks.
+        """
+        finals = [
+            _final(4096, "qa_squad", "the answer is paris", ["paris", "france"]),
+        ]
+
+        result = asyncio.run(RulerZeroShotGenTask.report(Mock(), finals, []))
+
+        assert result["score"] == 100.0
+        assert result["score_qa_squad_4k"] == 100.0
+
+    def test_fails_are_counted_not_scored(self):
+        """`fails` reflects the fails list length; only finals feed scoring."""
+        finals = [_final(4096, "niah_single_1", "cat", ["cat"])]
+
+        result = asyncio.run(RulerZeroShotGenTask.report(Mock(), finals, [1, 2, 3]))
+
+        assert result["score"] == 100.0
+        assert result["fails"] == 3
+
+    def test_empty_finals_yield_zero_score(self):
+        """No finals → overall 0.0 without ZeroDivisionError."""
+        result = asyncio.run(RulerZeroShotGenTask.report(Mock(), [], []))
+
+        assert result["score"] == 0.0
+        assert result["fails"] == 0
+
+
+class TestScenarios:
+    """Test complete scenarios covering all use cases."""
+
+    def test_scenario_qwen3_thinking(self):
+        """Qwen3 with thinking budget."""
+        tokens = tokens_to_generate(
+            "niah",
+            enable_thinking=True,
+            think_budget=5000,
+            model_name="Qwen3-8b",
+        )
+        prefill = thinking_prefill("Qwen3-8b", enable_thinking=True)
+
+        assert tokens == 5132
+        assert prefill == ""
+
+    def test_scenario_qwen3_no_thinking(self):
+        """Qwen3 without thinking."""
+        tokens = tokens_to_generate(
+            "niah",
+            enable_thinking=False,
+            think_budget=0,
+            model_name="Qwen3-8b",
+        )
+        prefill = thinking_prefill("Qwen3-8b", enable_thinking=False)
+
+        assert tokens == 128
+        assert prefill == "<think>\n\n</think>\n\n"
+
+    def test_scenario_gpt4_thinking(self):
+        """GPT-4 with thinking."""
+        tokens = tokens_to_generate(
+            "niah",
+            enable_thinking=True,
+            think_budget=3000,
+            model_name="gpt-4",
+        )
+        prefill = thinking_prefill("gpt-4", enable_thinking=True)
+
+        assert tokens == 3128
+        assert prefill == ""
+
+    def test_scenario_gpt4_no_thinking(self):
+        """GPT-4 without thinking."""
+        tokens = tokens_to_generate(
+            "niah",
+            enable_thinking=False,
+            think_budget=0,
+            model_name="gpt-4",
+        )
+        prefill = thinking_prefill("gpt-4", enable_thinking=False)
+
+        assert tokens == 128
+        assert prefill == ""