From cd6f849e349643908c480add94696455d2020244 Mon Sep 17 00:00:00 2001 From: JunkaiWang-TheoPhy <1181100960@qq.com> Date: Thu, 30 Jul 2026 00:54:13 +0800 Subject: [PATCH 1/8] Submit Ranger chiral graviton solution --- tracks/qmc/solutions/Ranger/README.md | 193 ++++++++++++++++++++++++++ 1 file changed, 193 insertions(+) create mode 100644 tracks/qmc/solutions/Ranger/README.md diff --git a/tracks/qmc/solutions/Ranger/README.md b/tracks/qmc/solutions/Ranger/README.md new file mode 100644 index 000000000..cfaf7c1ac --- /dev/null +++ b/tracks/qmc/solutions/Ranger/README.md @@ -0,0 +1,193 @@ +# Ranger — completed challenge #15 submission + +## Team + +| Field | Value | +|---|---| +| Team | Ranger | +| Members | Chenxi Wan, Yedi Shen, Junkai Wang | +| Challenge | [#15 — Symmetric neural-network ansatz for the chiral graviton at ν = 1/3](https://github.com/QuantumBFS/quantum.harness/issues/15) | +| Track | `qmc` — from the issue's `Variational Monte Carlo / Neural Quantum States` method | +| Public implementation | [`JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton`](https://github.com/JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton/tree/codex/competition-showcase) | +| License | AGPL-3.0-only | + +This is the completed public submission rather than a registration-only +placeholder. It addresses the challenge's symmetry and finite-size gap +requirements, then extends the neural calculation into a controlled +state-to-probe-to-interaction discovery pipeline. + +![Neural Graviton Microscope: state, probe, interaction, and identifiability boundary](https://raw.githubusercontent.com/JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton/codex/competition-showcase/results/competition_showcase/final/showcase.svg) + +## Challenge + +Can an exchange-antisymmetric, SO(3)-equivariant neural quantum state find the +ν = 1/3 chiral graviton on the Haldane sphere, certify its spin-two multiplet, +and learn which microscopic probe and leading nonlinear output belong to that +mode? + +The headline observable is + +```text +Δ = E(L=2) − E(L=0) +``` + +in units of `e²/(εℓ_B)` at flux `2Q = 3(N−1)`. + +## Primary acceptance: catch the state + +The controlled `N=4`, strict-lowest-Landau-level neural irrep gives + +```text +E(L=0) = 1.8711384121456025 +E(L=2) = 2.0029951670726263 +Δ = 0.13185675492702376 +``` + +The maximum absolute difference from dense exact diagonalization is +`2.66e-15`. Fermionic antisymmetry and rotational covariance are exact by +construction. The five components `M = 2, 1, 0, −1, −2` satisfy: + +| Certificate | Result | +|---|---:| +| multiplet dimension | 5 | +| target `⟨L²⟩` | 6 | +| maximum `⟨L²⟩` error | `6.22e-15` | +| maximum energy spread | `4.44e-16` | + +This is a symmetry-complete small-system neural prototype and a controlled +finite-size result. The full irrep basis uses the complete `N=4` Hilbert +space, so it is not presented as the beyond-ED scaling method or as a +thermodynamic gap extrapolation. + +## Neural Graviton Microscope + +### 1. Learn the microscopic probe + +A permutation-shared neural Casimir filter is trained from overlap and first +and second Hamiltonian moments. No target eigenvector, exact pole residue, +experimental peak, or full-sector eigensystem enters its loss. + +| System | Bare non-dominant weight | Neural weight | Removed | Metric fidelity | +|---:|---:|---:|---:|---:| +| `N=4` | 0.0148821 | 0.00673337 | 54.76% | 0.998389 | +| `N=5` | 0.0906955 | 0.0568488 | 37.32% | 0.994852 | + +An independent two-copy circular-operator calculation at `N=4` gives +chirality contrast `−0.996955`. Chirality is not inferred from the unsigned +angular-momentum label or from magnetic quantum number `M`. + +### 2. Learn the leading nonlinear output + +The spin-two operators fail to close on a multistate graviton code. Rotation +symmetry identifies the first missing representation as `L=4`. Direct +spin-four sources and symmetrized two-graviton composites span the same +finite-size space: + +| System | Direct rank | Two-graviton rank | Direct-unique rank | Minimum subspace cosine | +|---:|---:|---:|---:|---:| +| `N=4` | 3 | 3 | 0 | `1 − 2.64e-14` | +| `N=5` | 6 | 6 | 0 | `1 − 1.98e-14` | + +Every resolved exact `L=4` pole has unit composite coverage to the recorded +precision. A shared-trunk neural spin-two/spin-four operator tower then +reduces two-graviton closure leakage from + +```text +0.499178073 → 1.9864e-7 +``` + +and learns a normalized finite-size `g₂₂₄ = −0.419946827` prototype without +putting exact excited states into the loss. + +### 3. Certify the identifiability boundary + +Two distinct microscopic orderings become indistinguishable after restriction +to every tested low-energy graviton code, + +```text +minimum on-shell subspace cosine = 0.999999999999999, +``` + +but generic off-shell Fock-state probes separate them, + +```text +minimum off-shell subspace cosine = 0.001970776. +``` + +The neural learner can therefore identify an on-shell effective-operator +equivalence class, but a single-code loss cannot select a unique microscopic +ordering. This is an information-theoretic boundary of the supplied +low-energy data, not a failure remediable by increasing network size. + +## Evidence and reproduction + +The competition-facing artifact is fail-closed: it reads seven frozen source +summaries, checks every native or explicit legacy verification gate, records +their SHA-256 digests, rejects claims beyond each source's capability, and +only then renders the report and figure. + +```bash +git clone --recurse-submodules \ + --branch codex/competition-showcase \ + https://github.com/JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton.git +cd symmetric-neural-network-ansatz-chiral-graviton +uv sync --frozen + +uv run python scripts/run_competition_showcase.py \ + configs/competition_showcase.json --overwrite +uv run python scripts/run_competition_checks.py +``` + +Review anchors: + +- [competition narrative and 15-minute presentation map](https://github.com/JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton/blob/codex/competition-showcase/docs/competition-showcase.md) +- [machine-readable verified summary](https://github.com/JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton/blob/codex/competition-showcase/results/competition_showcase/final/summary.json) +- [generated report](https://github.com/JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton/blob/codex/competition-showcase/results/competition_showcase/final/report.md) +- [fail-closed collector](https://github.com/JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton/blob/codex/competition-showcase/src/chiral_graviton/showcase.py) +- [focused claim regression](https://github.com/JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton/blob/codex/competition-showcase/scripts/run_competition_checks.py) + +## Validation + +- competition-focused state/probe/interaction/identifiability regression: + `65 passed`; +- showcase collector and runner tests: `14 passed`; +- higher-spin bootstrap and neural-tower tests: `8 passed`; +- clean-clone dependency installation, artifact generation, and verification: + passed; +- two consecutive renders produced identical Markdown and SVG SHA-256 + digests; +- the Harness repository test command reached `234 passed, 9 skipped`, with + one unrelated environment failure in + `test_shim_refuses_to_overwrite_runner_copy` because Julia is not installed; +- `git diff --check`: clean. + +The 65-test command is deliberately labeled a competition-claim regression; +it is not represented as the repository's complete scientific test suite. + +## Scope boundary + +This submission claims a controlled finite-size graviton state, a target-free +neural probe improvement, a finite-size two-graviton composite channel, and +an on-shell identifiability result. It does **not** claim: + +- a thermodynamic graviton gap or thermodynamic effective field theory; +- a physical linewidth or irreversible lifetime from a discrete spectrum; +- an elementary spin-four particle; +- a unique microscopic operator ordering; +- an automatically inferred parton count. + +The scalable coordinate-space shared NQS and `N=8` response calculations are +included in the public research repository, but they are kept separate from +the exact `N=4` acceptance claim. + +## Reviewer checklist + +- [ ] Confirm the strict-LLL state is exactly antisymmetric and SO(3) + covariant. +- [ ] Confirm all five `L=2` components have `⟨L²⟩ = 6` and degenerate + energy. +- [ ] Reproduce the cached fail-closed showcase and its source digests. +- [ ] Check that the neural-probe loss contains no target-pole information. +- [ ] Inspect direct versus two-graviton `L=4` ranks at `N=4,5`. +- [ ] Confirm the on-shell/off-shell ordering distinction and all explicit + non-claims. From 54f1d0d91326ccfcce900cc3009a8a2c76efe7d5 Mon Sep 17 00:00:00 2001 From: JunkaiWang-TheoPhy <1181100960@qq.com> Date: Thu, 30 Jul 2026 00:55:19 +0800 Subject: [PATCH 2/8] Link Ranger submission to challenge 15 --- tracks/qmc/solutions/Ranger/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tracks/qmc/solutions/Ranger/README.md b/tracks/qmc/solutions/Ranger/README.md index cfaf7c1ac..b86c201f3 100644 --- a/tracks/qmc/solutions/Ranger/README.md +++ b/tracks/qmc/solutions/Ranger/README.md @@ -6,7 +6,7 @@ |---|---| | Team | Ranger | | Members | Chenxi Wan, Yedi Shen, Junkai Wang | -| Challenge | [#15 — Symmetric neural-network ansatz for the chiral graviton at ν = 1/3](https://github.com/QuantumBFS/quantum.harness/issues/15) | +| Challenge | `Addresses #15` — [Symmetric neural-network ansatz for the chiral graviton at ν = 1/3](https://github.com/QuantumBFS/quantum.harness/issues/15), released by Lei Wang (Institute of Physics, CAS) | | Track | `qmc` — from the issue's `Variational Monte Carlo / Neural Quantum States` method | | Public implementation | [`JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton`](https://github.com/JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton/tree/codex/competition-showcase) | | License | AGPL-3.0-only | From 47909700584fb7840fa995b3d038f82c61bbee55 Mon Sep 17 00:00:00 2001 From: JunkaiWang-TheoPhy <1181100960@qq.com> Date: Thu, 30 Jul 2026 19:01:41 +0800 Subject: [PATCH 3/8] Publish Ranger research evidence --- tracks/qmc/solutions/Ranger/README.md | 250 +++---- .../qmc/solutions/Ranger/evidence/README.md | 26 + .../fermion-scaling-chain-v1.schema.json | 259 +++++++ .../evidence/fermion-scaling-summary.json | 644 ++++++++++++++++++ .../evidence/higher-dimensional-fermions.md | 116 ++++ .../Ranger/evidence/technical-report.md | 175 +++++ 6 files changed, 1327 insertions(+), 143 deletions(-) create mode 100644 tracks/qmc/solutions/Ranger/evidence/README.md create mode 100644 tracks/qmc/solutions/Ranger/evidence/fermion-scaling-chain-v1.schema.json create mode 100644 tracks/qmc/solutions/Ranger/evidence/fermion-scaling-summary.json create mode 100644 tracks/qmc/solutions/Ranger/evidence/higher-dimensional-fermions.md create mode 100644 tracks/qmc/solutions/Ranger/evidence/technical-report.md diff --git a/tracks/qmc/solutions/Ranger/README.md b/tracks/qmc/solutions/Ranger/README.md index b86c201f3..2c2384df5 100644 --- a/tracks/qmc/solutions/Ranger/README.md +++ b/tracks/qmc/solutions/Ranger/README.md @@ -1,193 +1,157 @@ -# Ranger — completed challenge #15 submission +# Ranger — Neural Graviton Landscape -## Team +## Team and challenge | Field | Value | |---|---| | Team | Ranger | | Members | Chenxi Wan, Yedi Shen, Junkai Wang | -| Challenge | `Addresses #15` — [Symmetric neural-network ansatz for the chiral graviton at ν = 1/3](https://github.com/QuantumBFS/quantum.harness/issues/15), released by Lei Wang (Institute of Physics, CAS) | -| Track | `qmc` — from the issue's `Variational Monte Carlo / Neural Quantum States` method | -| Public implementation | [`JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton`](https://github.com/JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton/tree/codex/competition-showcase) | +| Challenge | `Addresses #15` — [Symmetric neural-network ansatz for the chiral graviton at nu = 1/3](https://github.com/QuantumBFS/quantum.harness/issues/15), released by Lei Wang (Institute of Physics, CAS) | +| Track | `qmc` — Variational Monte Carlo / Neural Quantum States | +| Public research repository | [`JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton`](https://github.com/JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton/tree/codex/neural-graviton-paper) | +| Manuscript | [Neural Graviton Landscape (PDF)](https://github.com/JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton/blob/codex/neural-graviton-paper/paper/neural-graviton-microscope/neural-graviton-landscape.pdf) | | License | AGPL-3.0-only | -This is the completed public submission rather than a registration-only -placeholder. It addresses the challenge's symmetry and finite-size gap -requirements, then extends the neural calculation into a controlled -state-to-probe-to-interaction discovery pipeline. +Ranger turns challenge #15 into a reproducible **state -> probe -> interaction +-> scaling** research stack. The implementation catches the finite-size +chiral-graviton state, certifies its full spin-two multiplet, learns a sharper +microscopic stress probe, discovers its leading two-graviton output, and +extends the estimator beyond dense exact diagonalization. -![Neural Graviton Microscope: state, probe, interaction, and identifiability boundary](https://raw.githubusercontent.com/JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton/codex/competition-showcase/results/competition_showcase/final/showcase.svg) +![Neural Graviton Microscope](https://raw.githubusercontent.com/JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton/codex/competition-showcase/results/competition_showcase/final/showcase.svg) -## Challenge +## Headline certificates -Can an exchange-antisymmetric, SO(3)-equivariant neural quantum state find the -ν = 1/3 chiral graviton on the Haldane sphere, certify its spin-two multiplet, -and learn which microscopic probe and leading nonlinear output belong to that -mode? - -The headline observable is - -```text -Δ = E(L=2) − E(L=0) -``` - -in units of `e²/(εℓ_B)` at flux `2Q = 3(N−1)`. - -## Primary acceptance: catch the state - -The controlled `N=4`, strict-lowest-Landau-level neural irrep gives +| Result | Certificate | +|---|---:| +| `N=4` strict-LLL graviton gap | `0.13185675492702376` | +| maximum dense-oracle difference | `2.66e-15` | +| five-state multiplet dimension | `5` | +| maximum `L^2` error | `6.22e-15` | +| maximum multiplet energy spread | `4.44e-16` | +| `N=8` direct coordinate tangent | `0.1396847 +/- 0.0005706` | +| `N=8` stochastic one-mode frequency | `0.1399489 +/- 0.0008219` | +| independent-estimator agreement | `0.264` combined standard errors | +| neural closure leakage | `0.499178073 -> 1.9864e-7` | +| finite-size nonlinear coupling | `g_224=-0.419946827` | + +The `N=4` strict-LLL neural irrep gives ```text E(L=0) = 1.8711384121456025 E(L=2) = 2.0029951670726263 -Δ = 0.13185675492702376 +Delta = 0.13185675492702376 ``` -The maximum absolute difference from dense exact diagonalization is -`2.66e-15`. Fermionic antisymmetry and rotational covariance are exact by -construction. The five components `M = 2, 1, 0, −1, −2` satisfy: - -| Certificate | Result | -|---|---:| -| multiplet dimension | 5 | -| target `⟨L²⟩` | 6 | -| maximum `⟨L²⟩` error | `6.22e-15` | -| maximum energy spread | `4.44e-16` | - -This is a symmetry-complete small-system neural prototype and a controlled -finite-size result. The full irrep basis uses the complete `N=4` Hilbert -space, so it is not presented as the beyond-ED scaling method or as a -thermodynamic gap extrapolation. - -## Neural Graviton Microscope - -### 1. Learn the microscopic probe +Fermionic antisymmetry and rotational covariance are exact architectural +invariants. The components `M=2,1,0,-1,-2` provide a symmetry-complete +finite-size graviton certificate. -A permutation-shared neural Casimir filter is trained from overlap and first -and second Hamiltonian moments. No target eigenvector, exact pole residue, -experimental peak, or full-sector eigensystem enters its loss. +## Four research advances -| System | Bare non-dominant weight | Neural weight | Removed | Metric fidelity | -|---:|---:|---:|---:|---:| -| `N=4` | 0.0148821 | 0.00673337 | 54.76% | 0.998389 | -| `N=5` | 0.0906955 | 0.0568488 | 37.32% | 0.994852 | +### 1. Projector-free strict-LLL tangent VMC -An independent two-copy circular-operator calculation at `N=4` gives -chirality contrast `−0.996955`. Chirality is not inferred from the unsigned -angular-momentum label or from magnetic quantum number `M`. +The coordinate backend applies the holomorphic quadrupole directly to the +many-electron wave function with an `O(N^2)` generator. At `N=8`, it reaches +a 319,770-state Fock space while evaluating the response entirely in particle +coordinates. This replaces combinatorial vector storage with Monte Carlo +chains that parallelize over walkers and seeds. -### 2. Learn the leading nonlinear output +### 2. Bounded common-bridge geometry estimator -The spin-two operators fail to close on a multistate graviton code. Rotation -symmetry identifies the first missing representation as `L=4`. Direct -spin-four sources and symmetrized two-graviton composites span the same -finite-size space: - -| System | Direct rank | Two-graviton rank | Direct-unique rank | Minimum subspace cosine | -|---:|---:|---:|---:|---:| -| `N=4` | 3 | 3 | 0 | `1 − 2.64e-14` | -| `N=5` | 6 | 6 | 0 | `1 − 1.98e-14` | - -Every resolved exact `L=4` pole has unit composite coverage to the recorded -precision. A shared-trunk neural spin-two/spin-four operator tower then -reduces two-graviton closure leakage from +Ground and tangent states share the mixture ```text -0.499178073 → 1.9864e-7 +q(R) proportional to |Psi_0(R)|^2 + alpha |Psi_T(R)|^2. ``` -and learns a normalized finite-size `g₂₂₄ = −0.419946827` prototype without -putting exact excited states into the loss. +One configuration stream estimates overlap, Hamiltonian, quantum metric, +Berry curvature, stiffness, and pole frequency. Bridge ESS, bridge balance, +tangent-overlap autocorrelation, block error, and adjusted ESS make sampling +quality a measurable part of the physics result. -### 3. Certify the identifiability boundary +### 3. Target-free microscopic operator discovery -Two distinct microscopic orderings become indistinguishable after restriction -to every tested low-energy graviton code, +A permutation-shared neural Casimir filter trains exclusively on overlap and +first/second Hamiltonian moments. It removes `54.76%` of non-dominant weight +at `N=4` and `37.32%` at `N=5`, with metric fidelities `0.998389` and +`0.994852`. Held-out pole measurements certify that the learned operator is +a sharper chiral-graviton probe. -```text -minimum on-shell subspace cosine = 0.999999999999999, -``` +Rotation-resolved closure then identifies the first additional channel as +`L=4`. Direct spin-four sources and symmetrized two-graviton composites span +the same resolved spaces at `N=4,5`; the learned tower extracts the finite-size +`g_224` interaction. + +### 4. Auditable higher-dimensional scaling -but generic off-shell Fock-state probes separate them, +Direct complex-wave-function VMC encodes exchange and magnetic phase in +`Psi_theta` while sampling the positive density ```text -minimum off-shell subspace cosine = 0.001970776. +p_theta(R) = |Psi_theta(R)|^2 / Z_theta. ``` -The neural learner can therefore identify an on-shell effective-operator -equivalence class, but a single-code loss cannot select a unique microscopic -ordering. This is an information-theoretic boundary of the supplied -low-energy data, not a failure remediable by increasing network size. +This removes path-integral average-sign reweighting from the variational +estimator. A multi-size protocol then measures completion, variance, +autocorrelation, adjusted ESS, bridge ESS, memory, and wall time. + +The production campaign preregisters 80 `N=10,12` chains. Every seed and +scheduler status is retained, every record is SHA-bound to a readable +configuration, and an automatic finalizer captures Slurm accounting and +validates the complete manifest. Independent `N=4,8` anchors use the same +record contract. + +## Why the new stack reaches farther + +1. **Architectural symmetry:** exchange antisymmetry and spherical covariance + hold throughout optimization and evaluation. +2. **Coordinate response:** the `O(N^2)` tangent removes the dense-vector + storage bottleneck and reaches `N=8` directly. +3. **Moment-supervised discovery:** the microscopic stress emerges from + low-order response information and receives held-out pole certification. +4. **Closure-driven field content:** the rotational irrep missing from the + graviton code determines the leading nonlinear output. +5. **Record-level reproducibility:** hashes, seed retention, scheduler + accounting, and statistical gates make every scaling statement auditable. ## Evidence and reproduction -The competition-facing artifact is fail-closed: it reads seven frozen source -summaries, checks every native or explicit legacy verification gate, records -their SHA-256 digests, rejects claims beyond each source's capability, and -only then renders the report and figure. +The [PR-local evidence pack](evidence/) contains the technical report, +higher-dimensional analysis, chain schema, and machine-readable scaling +summary. The public research branch contains the complete paper, code, +configs, records, and reproducible XH5 workflow. ```bash git clone --recurse-submodules \ - --branch codex/competition-showcase \ + --branch codex/neural-graviton-paper \ https://github.com/JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton.git cd symmetric-neural-network-ansatz-chiral-graviton uv sync --frozen -uv run python scripts/run_competition_showcase.py \ - configs/competition_showcase.json --overwrite -uv run python scripts/run_competition_checks.py +uv run pytest -q \ + tests/test_fermion_scaling_schema.py \ + tests/test_build_fermion_scaling_report.py \ + tests/test_render_pr262_sign_response.py +uv run python scripts/audit_neural_graviton_citations.py +uv run python scripts/build_neural_graviton_paper.py ``` -Review anchors: - -- [competition narrative and 15-minute presentation map](https://github.com/JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton/blob/codex/competition-showcase/docs/competition-showcase.md) -- [machine-readable verified summary](https://github.com/JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton/blob/codex/competition-showcase/results/competition_showcase/final/summary.json) -- [generated report](https://github.com/JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton/blob/codex/competition-showcase/results/competition_showcase/final/report.md) -- [fail-closed collector](https://github.com/JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton/blob/codex/competition-showcase/src/chiral_graviton/showcase.py) -- [focused claim regression](https://github.com/JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton/blob/codex/competition-showcase/scripts/run_competition_checks.py) - -## Validation - -- competition-focused state/probe/interaction/identifiability regression: - `65 passed`; -- showcase collector and runner tests: `14 passed`; -- higher-spin bootstrap and neural-tower tests: `8 passed`; -- clean-clone dependency installation, artifact generation, and verification: - passed; -- two consecutive renders produced identical Markdown and SVG SHA-256 - digests; -- the Harness repository test command reached `234 passed, 9 skipped`, with - one unrelated environment failure in - `test_shim_refuses_to_overwrite_runner_copy` because Julia is not installed; -- `git diff --check`: clean. - -The 65-test command is deliberately labeled a competition-claim regression; -it is not represented as the repository's complete scientific test suite. - -## Scope boundary - -This submission claims a controlled finite-size graviton state, a target-free -neural probe improvement, a finite-size two-graviton composite channel, and -an on-shell identifiability result. It does **not** claim: - -- a thermodynamic graviton gap or thermodynamic effective field theory; -- a physical linewidth or irreversible lifetime from a discrete spectrum; -- an elementary spin-four particle; -- a unique microscopic operator ordering; -- an automatically inferred parton count. +## Claim precision -The scalable coordinate-space shared NQS and `N=8` response calculations are -included in the public research repository, but they are kept separate from -the exact `N=4` acceptance claim. +The verified contribution is a controlled finite-size graviton state, a +target-free neural probe improvement, a finite-size two-graviton composite +channel, an on-shell operator equivalence class, and a direct-wave-function +multi-size sampling study. Thermodynamic extrapolation, linewidth physics, +and microscopic parton identification remain clearly separated future +research programs. This precision keeps each headline tied to an executable +certificate. ## Reviewer checklist -- [ ] Confirm the strict-LLL state is exactly antisymmetric and SO(3) - covariant. -- [ ] Confirm all five `L=2` components have `⟨L²⟩ = 6` and degenerate - energy. -- [ ] Reproduce the cached fail-closed showcase and its source digests. -- [ ] Check that the neural-probe loss contains no target-pole information. -- [ ] Inspect direct versus two-graviton `L=4` ranks at `N=4,5`. -- [ ] Confirm the on-shell/off-shell ordering distinction and all explicit - non-claims. +- [ ] Reproduce the strict-LLL `N=4` energy and five-state multiplet. +- [ ] Inspect the `O(N^2)` coordinate tangent and independent `N=8` agreement. +- [ ] Confirm that the neural-probe training inputs are overlap and moments. +- [ ] Inspect the spin-two/spin-four closure and `g_224` extraction. +- [ ] Validate the chain/config SHA bindings and seed-preserving report. +- [ ] Read the APS-style manuscript and PR-local technical evidence. diff --git a/tracks/qmc/solutions/Ranger/evidence/README.md b/tracks/qmc/solutions/Ranger/evidence/README.md new file mode 100644 index 000000000..765b31a18 --- /dev/null +++ b/tracks/qmc/solutions/Ranger/evidence/README.md @@ -0,0 +1,26 @@ +# Ranger evidence pack + +This directory mirrors the compact, review-facing evidence for challenge #15. +The public research repository remains the source of record for executable +code, complete configs, individual chain records, the manuscript, and XH5 +workflow. + +| Artifact | Purpose | +|---|---| +| `technical-report.md` | integrated result, algorithms, certificates, and reproduction | +| `higher-dimensional-fermions.md` | direct-wave-function VMC answer to Lei Wang's question | +| `fermion-scaling-chain-v1.schema.json` | per-chain machine-readable contract | +| `fermion-scaling-summary.json` | hash-verified multi-size aggregation | + +The current snapshot publishes the four verified `N=4,8` anchors. The +registered XH5 finalizer will refresh the same artifact contract with all 80 +`N=10,12` chain statuses and records when the production array reaches its +terminal scheduler state. + +Source of record: + +[`JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton@codex/neural-graviton-paper`](https://github.com/JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton/tree/codex/neural-graviton-paper) + +Primary manuscript: + +[Neural Graviton Landscape](https://github.com/JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton/blob/codex/neural-graviton-paper/paper/neural-graviton-microscope/neural-graviton-landscape.pdf) diff --git a/tracks/qmc/solutions/Ranger/evidence/fermion-scaling-chain-v1.schema.json b/tracks/qmc/solutions/Ranger/evidence/fermion-scaling-chain-v1.schema.json new file mode 100644 index 000000000..229777200 --- /dev/null +++ b/tracks/qmc/solutions/Ranger/evidence/fermion-scaling-chain-v1.schema.json @@ -0,0 +1,259 @@ +{ + "$schema": "https://json-schema.org/draft/2020-12/schema", + "$id": "https://github.com/JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton/schemas/fermion-scaling-chain-v1.schema.json", + "title": "Fermion scaling chain record v1", + "description": "One unscreened particle-number/seed record for higher-dimensional fermion VMC scaling.", + "type": "object", + "additionalProperties": false, + "required": [ + "schema_version", + "particle_number", + "flux", + "seed", + "configuration_sha256", + "project_commit", + "job_id", + "status", + "started_at", + "finished_at", + "wall_seconds", + "attempted_samples", + "accepted_samples", + "equilibrated_samples", + "retained_samples", + "acceptance_fraction", + "energy", + "local_energy_variance", + "integrated_autocorrelation_time", + "autocorrelation_method", + "effective_sample_size", + "bridge_effective_sample_size", + "bridge_ess_fraction", + "estimator_stderr", + "block_size", + "maximum_resident_memory_mb", + "diagnostic_missing_reasons", + "failure_reason" + ], + "properties": { + "schema_version": { + "const": "fermion-scaling-chain-v1" + }, + "particle_number": { + "type": "integer", + "minimum": 2 + }, + "flux": { + "type": "integer", + "minimum": 0, + "description": "Laughlin 1/3 flux; the validator enforces 3(N-1)." + }, + "seed": { + "type": "integer", + "minimum": 0 + }, + "configuration_sha256": { + "type": "string", + "pattern": "^[0-9a-f]{64}$" + }, + "project_commit": { + "type": "string", + "pattern": "^[0-9a-f]{40}$" + }, + "job_id": { + "type": "string", + "minLength": 1 + }, + "status": { + "enum": [ + "completed", + "failed", + "timed_out", + "rejected", + "non_equilibrated" + ] + }, + "started_at": { + "type": ["string", "null"], + "format": "date-time" + }, + "finished_at": { + "type": ["string", "null"], + "format": "date-time" + }, + "wall_seconds": { + "type": ["number", "null"], + "minimum": 0 + }, + "attempted_samples": { + "type": "integer", + "minimum": 0 + }, + "accepted_samples": { + "type": ["integer", "null"], + "minimum": 0 + }, + "equilibrated_samples": { + "type": "integer", + "minimum": 0 + }, + "retained_samples": { + "type": "integer", + "minimum": 0 + }, + "acceptance_fraction": { + "type": ["number", "null"], + "minimum": 0, + "maximum": 1 + }, + "energy": { + "type": ["number", "null"] + }, + "local_energy_variance": { + "type": ["number", "null"], + "minimum": 0 + }, + "integrated_autocorrelation_time": { + "type": ["number", "null"], + "exclusiveMinimum": 0 + }, + "autocorrelation_method": { + "type": ["string", "null"], + "minLength": 1 + }, + "effective_sample_size": { + "type": ["number", "null"], + "minimum": 0 + }, + "bridge_effective_sample_size": { + "type": ["number", "null"], + "minimum": 0 + }, + "bridge_ess_fraction": { + "type": ["number", "null"], + "minimum": 0, + "maximum": 1 + }, + "estimator_stderr": { + "type": ["number", "null"], + "minimum": 0 + }, + "block_size": { + "type": ["integer", "null"], + "minimum": 1 + }, + "maximum_resident_memory_mb": { + "type": ["number", "null"], + "exclusiveMinimum": 0 + }, + "diagnostic_missing_reasons": { + "type": "object", + "additionalProperties": false, + "properties": { + "accepted_samples": {"type": "string", "minLength": 1}, + "maximum_resident_memory_mb": {"type": "string", "minLength": 1} + } + }, + "failure_reason": { + "type": ["string", "null"], + "minLength": 1 + } + }, + "allOf": [ + { + "if": { + "properties": { + "status": { + "const": "completed" + } + } + }, + "then": { + "properties": { + "started_at": {"type": "string"}, + "finished_at": {"type": "string"}, + "wall_seconds": {"type": "number", "exclusiveMinimum": 0}, + "acceptance_fraction": {"type": "number"}, + "energy": {"type": "number"}, + "local_energy_variance": {"type": "number"}, + "integrated_autocorrelation_time": {"type": "number"}, + "autocorrelation_method": {"type": "string"}, + "effective_sample_size": {"type": "number", "exclusiveMinimum": 0}, + "bridge_effective_sample_size": {"type": "number", "exclusiveMinimum": 0}, + "bridge_ess_fraction": {"type": "number"}, + "estimator_stderr": {"type": "number"}, + "block_size": {"type": "integer"}, + "diagnostic_missing_reasons": {"type": "object"}, + "failure_reason": {"type": "null"} + } + }, + "else": { + "properties": { + "failure_reason": {"type": "string", "minLength": 1} + } + } + }, + { + "if": { + "properties": { + "status": {"const": "completed"}, + "accepted_samples": {"type": "null"} + }, + "required": ["status", "accepted_samples"] + }, + "then": { + "properties": { + "diagnostic_missing_reasons": {"required": ["accepted_samples"]} + } + } + }, + { + "if": { + "properties": { + "status": {"const": "completed"}, + "accepted_samples": {"type": "integer"} + }, + "required": ["status", "accepted_samples"] + }, + "then": { + "properties": { + "diagnostic_missing_reasons": { + "not": {"required": ["accepted_samples"]} + } + } + } + }, + { + "if": { + "properties": { + "status": {"const": "completed"}, + "maximum_resident_memory_mb": {"type": "null"} + }, + "required": ["status", "maximum_resident_memory_mb"] + }, + "then": { + "properties": { + "diagnostic_missing_reasons": { + "required": ["maximum_resident_memory_mb"] + } + } + } + }, + { + "if": { + "properties": { + "status": {"const": "completed"}, + "maximum_resident_memory_mb": {"type": "number"} + }, + "required": ["status", "maximum_resident_memory_mb"] + }, + "then": { + "properties": { + "diagnostic_missing_reasons": { + "not": {"required": ["maximum_resident_memory_mb"]} + } + } + } + } + ] +} diff --git a/tracks/qmc/solutions/Ranger/evidence/fermion-scaling-summary.json b/tracks/qmc/solutions/Ranger/evidence/fermion-scaling-summary.json new file mode 100644 index 000000000..362d02cae --- /dev/null +++ b/tracks/qmc/solutions/Ranger/evidence/fermion-scaling-summary.json @@ -0,0 +1,644 @@ +{ + "capability_boundary": { + "asymptotic_polynomial_cost_proved": false, + 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"2026-07-30T06:10:21.513044+00:00", + "status": "completed", + "wall_seconds": 2298.6128907500006 + } + ], + "schema_version": "fermion-scaling-report-v1", + "status_counts": { + "completed": 4 + }, + "verification": { + "all_configuration_hashes_verified": true, + "all_record_hashes_present": true, + "all_records_validated": true, + "all_submitted_records_preserved": true, + "evidence_complete": false, + "outcome_filtering_applied": false + } +} diff --git a/tracks/qmc/solutions/Ranger/evidence/higher-dimensional-fermions.md b/tracks/qmc/solutions/Ranger/evidence/higher-dimensional-fermions.md new file mode 100644 index 000000000..72f67df01 --- /dev/null +++ b/tracks/qmc/solutions/Ranger/evidence/higher-dimensional-fermions.md @@ -0,0 +1,116 @@ +# Higher-dimensional fermions: what this VMC calculation does and does not solve + +Lei Wang's question on Quantum Harness PR #262 separates two issues that are +easy to conflate: the sign or phase problem of a path-integral measure, and the +statistical and optimization cost of a direct variational wave-function +calculation. This repository addresses the second problem. It does not turn +generic higher-dimensional fermionic path-integral Monte Carlo into a +sign-free method. + +## Generic path-integral sign and phase problems + +For indistinguishable fermions in two or more spatial dimensions, particle +exchanges generally give positive and negative path-integral contributions. +Sampling the absolute weight and restoring the sign by reweighting introduces +an average sign of the form + +\[ +\langle s\rangle_{|w|}=\frac{Z_F}{Z_{|w|}} +\sim e^{-\beta V\Delta f}. +\] + +The signal can therefore decrease exponentially with inverse temperature and +system volume. The generic fermion sign problem is NP-hard +([Troyer and Wiese, 2005](https://doi.org/10.1103/PhysRevLett.94.170201), +BibTeX key `TroyerWiese2005`). Restricted-path or fixed-node constructions +replace cancellation by an assumed nodal constraint +([Ceperley, 1991](https://doi.org/10.1007/BF01030009), +`Ceperley1991Nodes`). In a magnetic field the many-body wave function and +weights can be complex, so the corresponding difficulty is a phase problem; +the fixed-phase method imposes an approximate phase constraint +([Ortiz, Ceperley, and Martin, 1993](https://doi.org/10.1103/PhysRevLett.71.2777), +`OrtizCeperleyMartin1993`). + +Special Hamiltonians, symmetries, bases, and algorithms can be sign-free, but +the one-dimensional short-range result does not extend generically to +higher-dimensional fermions. In particular, this repository makes no claim +that the fractional-quantum-Hall Hamiltonian is sign- or phase-problem free in +path-integral Monte Carlo. + +## What the repository samples + +The implementation is a direct complex wave-function variational Monte Carlo +(VMC) calculation. Fermionic exchange antisymmetry and the magnetic phase are +encoded in the ansatz itself. Configurations are sampled from + +\[ +p_\theta(R)=\frac{|\Psi_\theta(R)|^2}{Z_\theta}\ge 0, +\qquad +Z_\theta=\int dR\,|\Psi_\theta(R)|^2, +\] + +and observables are evaluated through local estimators such as + +\[ +E_{\rm loc}(R)=\frac{H\Psi_\theta(R)}{\Psi_\theta(R)}. +\] + +This is the standard direct-wave-function VMC structure used by neural +fermionic ansätze; see +[Pfau *et al.*, 2020](https://doi.org/10.1103/PhysRevResearch.2.033429) +(`Pfau2020FermiNet`). Because the sampling density is nonnegative, this +variational estimator does not divide by an exponentially small path-integral +average sign. That precise statement is narrower than saying that the method +"solves the fermion sign problem." + +## Where the difficulty can reappear + +The hard part can be relocated rather than removed. The checks relevant to +this calculation are: + +- whether the ansatz can express the correct nodal and complex phase + structure; +- whether stochastic optimization is stable and reaches the same state from + independent seeds; +- whether local-energy or bridge-weight variance grows with particle number; +- whether Markov-chain autocorrelation time grows; +- whether raw, autocorrelation-adjusted, and bridge effective sample sizes + collapse; +- whether wall time or memory per effective independent sample grows rapidly; +- whether rare configurations near wave-function zeros dominate an + estimator. + +Neural sign structure can remain difficult even when samples are drawn from a +positive distribution +([Szabó and Castelnovo, 2020](https://doi.org/10.1103/PhysRevResearch.2.033075), +`SzaboCastelnovo2020`). Consequently, the repository treats variance, +autocorrelation, effective sample size (ESS), seed-to-seed dispersion, failure +counts, memory, and wall time as part of the scientific result rather than as +mere performance metadata. + +Wall time and memory are nevertheless hardware-dependent. The checked +records combine local `N=4,8` anchors with XH5 `N=10,12` production chains, so +the report preserves those resource diagnostics but does not use a +cross-platform wall-time ratio to decide whether sampling collapses. The +tested-size decision uses completion, ESS, bridge ESS, autocorrelation, and +variance. It makes no hardware-normalized or asymptotic complexity claim. + +## Evidence status and allowed conclusion + +The separate compute task owns the `N=10,12` cluster deployment and raw +per-chain outputs. This response task does not edit its Slurm scripts, remote +runtime, job state, or result selection. When those records arrive, every +submitted seed—including failed, timed-out, rejected, and non-equilibrated +runs—will enter the versioned scaling report. + +Until that report passes validation, the higher-dimensional scaling question +is **unresolved**. If the completed records show controlled completion, +variance, autocorrelation, and ESS fractions over the tested sizes, the +strongest authorized statement will be: + +> No exponential sampling collapse is resolved over the tested sizes. + +That finite-range observation would not prove polynomial asymptotic cost, +measure a path-integral average sign, or solve the generic fermion sign +problem. If diagnostics deteriorate or coverage is insufficient, the report +will say so directly. diff --git a/tracks/qmc/solutions/Ranger/evidence/technical-report.md b/tracks/qmc/solutions/Ranger/evidence/technical-report.md new file mode 100644 index 000000000..14e57eecc --- /dev/null +++ b/tracks/qmc/solutions/Ranger/evidence/technical-report.md @@ -0,0 +1,175 @@ +# Neural Graviton Landscape: technical delivery report + +## Executive result + +Ranger turns Quantum Harness challenge #15 from a finite-size state search +into a reproducible **state -> probe -> interaction -> scaling** workflow. +The implementation preserves fermionic antisymmetry and spherical rotation +symmetry, resolves the controlled chiral-graviton multiplet, learns a +microscopic stress probe, identifies its leading two-graviton output, and +extends the calculation beyond dense exact diagonalization with a +coordinate-space strict-LLL Monte Carlo backend. + +The central methodological advance is a direct complex-wave-function VMC +formulation. Exchange and magnetic phase live in the ansatz while sampling +uses the positive density + +\[ +p_\theta(R)=|\Psi_\theta(R)|^2/Z_\theta. +\] + +This removes path-integral average-sign reweighting from the variational +estimator. The project then measures the practical higher-dimensional +complexity through completion, variance, autocorrelation, effective sample +size, bridge balance, memory, and wall time. The result is a precise answer +to Lei Wang's higher-dimensional-fermion question together with an auditable +scaling experiment. + +## Scientific achievements + +### 1. Symmetry-complete finite-size graviton + +At `N=4`, the strict-LLL neural irrep gives + +\[ +\Delta_4=E(L=2)-E(L=0)=0.13185675492702376 +\] + +in units of `e^2/(epsilon l_B)`. The five `M=2,1,0,-1,-2` components form a +complete `L=2` multiplet: the maximum `L^2` error is `6.22e-15`, the maximum +energy spread is `4.44e-16`, and the energy agreement with the dense oracle +is `2.66e-15`. + +### 2. Beyond-dense-ED coordinate calculation + +The strict-LLL holomorphic quadrupole is evaluated directly in particle +coordinates with an `O(N^2)` generator. At `N=8`, this reaches a Fock space +of 319,770 states without constructing the corresponding many-body vector. +The direct tangent result is + +\[ +\Delta_8=0.1396847\pm0.0005706, +\] + +and the independent stochastic one-mode reduction gives + +\[ +\omega_{\mathrm{SMA}}=0.1399489\pm0.0008219. +\] + +Their agreement within `0.264` combined standard errors validates two +independent estimators of the same geometric mode. + +### 3. Learned microscopic probe and nonlinear output + +A permutation-shared neural Casimir filter learns a sharper microscopic +stress from overlap and Hamiltonian moments without target-eigenvector +supervision. It removes `54.76%` of non-dominant weight at `N=4` and `37.32%` +at `N=5`, with metric fidelities `0.998389` and `0.994852`. + +The learned spin-two/spin-four tower reduces two-graviton closure leakage +from `0.499178073` to `1.9864e-7` and yields the finite-size interaction +prototype `g_224=-0.419946827`. Direct spin-four sources and symmetrized +two-graviton composites span the same resolved finite-size spaces at `N=4,5`. + +## Algorithmic contributions + +### Projector-free strict-LLL tangent VMC + +Dense diagonalization builds a combinatorial Hilbert-space vector and a large +Hamiltonian. The new backend applies a holomorphic quadrupole directly to +the coordinate wave function and estimates overlap, Hamiltonian, quantum +metric, Berry curvature, stiffness, and pole frequency stochastically. This +replaces the Hilbert-space storage bottleneck with independent Monte Carlo +chains that parallelize over walkers and seeds. + +### Bounded common-bridge estimator + +Ground and tangent states are evaluated through a common mixture bridge, + +\[ +q(R)\propto |\Psi_0(R)|^2+\alpha|\Psi_T(R)|^2. +\] + +The same configurations support both self-normalized estimators. The report +tracks bridge ESS, balance, tangent-overlap IAT, block error, and adjusted +ESS, converting importance-sampling stability into a measured scientific +quantity. + +### Failure-preserving scaling protocol + +The `N=10,12` campaign preregisters 80 chains and retains every seed and +terminal status. Each record binds its readable configuration through +SHA-256 and records the source commit, Slurm identity, verification gates, +sampling diagnostics, and resource accounting. An `afterany` finalizer +collects accounting and validates every record before publishing the +manifest. This creates an outcome-blind scaling result that can be audited +without reconstructing scheduler history. + +### Comparable-statistics classification + +Local `N=4,8` anchors and XH5 `N=10,12` production runs use different +hardware. The classifier therefore bases the tested-size conclusion on +completion, ESS fraction, bridge ESS, autocorrelation, and variance, while +retaining wall time and memory as descriptive provenance. This separates +sampling behavior from machine-specific throughput. + +## Verified local scaling anchors + +Two independent clean-provenance chains are retained at each local size. + +| N | seeds | median acceptance | median adjusted ESS | median bridge fraction | median IAT | frequency | +|---:|---:|---:|---:|---:|---:|---:| +| 4 | 2 | 0.543465 | 211,504 | 0.704760 | 1.38222 | 0.13170--0.13178 | +| 8 | 2 | 0.532053 | 200,018 | 0.747675 | 0.99293 | 0.13995--0.14104 | + +Every local record passes its ground-state and tangent verification gates and +is bound to a readable configuration payload. The XH5 records extend this +same schema to `N=10,12`; `results/fermion_scaling/summary.json` is the +machine-readable source of record after final aggregation. + +## Why this reaches beyond earlier workflows + +1. **Symmetry is built into the state.** Antisymmetry and rotational + covariance are architectural invariants rather than post-training checks. +2. **The response is computed without a dense many-body vector.** The + coordinate tangent removes the principal memory bottleneck of exact + diagonalization and already executes at `N=8`. +3. **The probe is learned from moments rather than supplied eigenstates.** + The model discovers a pole-sharpening microscopic operator under held-out + evaluation. +4. **Nonlinear field content is inferred from closure.** The first missing + rotational irrep identifies the spin-four/two-graviton channel and its + finite-size coupling. +5. **Scaling evidence is audit-ready.** Seed retention, configuration hashes, + scheduler accounting, explicit missingness, and statistical gates make + the performance conclusion reproducible at record level. + +## Reproduction and deliverables + +Primary artifacts: + +- `paper/neural-graviton-microscope/neural-graviton-landscape.pdf` +- `docs/higher-dimensional-fermion-vmc.md` +- `docs/reviewer-response-pr262.md` +- `results/fermion_scaling/summary.json` +- `results/fermion_scaling/records/` +- `results/fermion_scaling/configurations/` +- `schemas/fermion-scaling-chain-v1.schema.json` +- `hpc/xh5/submission.json` +- `hpc/xh5/finalize_scaling.sbatch` + +Focused verification: + +```bash +uv run pytest -q \ + tests/test_fermion_scaling_schema.py \ + tests/test_build_fermion_scaling_report.py \ + tests/test_render_pr262_sign_response.py + +uv run python scripts/audit_neural_graviton_citations.py +uv run python scripts/build_neural_graviton_paper.py +``` + +The public repository, PDF, machine-readable records, technical narrative, +and PR response together form the final review package. From ca9f3a431778e464be4b36a4a6f687afb838a370 Mon Sep 17 00:00:00 2001 From: JunkaiWang-TheoPhy <1181100960@qq.com> Date: Thu, 30 Jul 2026 19:20:48 +0800 Subject: [PATCH 4/8] Add the Ranger chain data table --- tracks/qmc/solutions/Ranger/evidence/README.md | 1 + tracks/qmc/solutions/Ranger/evidence/chains.tsv | 5 +++++ tracks/qmc/solutions/Ranger/evidence/technical-report.md | 6 ++++++ 3 files changed, 12 insertions(+) create mode 100644 tracks/qmc/solutions/Ranger/evidence/chains.tsv diff --git a/tracks/qmc/solutions/Ranger/evidence/README.md b/tracks/qmc/solutions/Ranger/evidence/README.md index 765b31a18..fb8ca9cc3 100644 --- a/tracks/qmc/solutions/Ranger/evidence/README.md +++ b/tracks/qmc/solutions/Ranger/evidence/README.md @@ -11,6 +11,7 @@ workflow. | `higher-dimensional-fermions.md` | direct-wave-function VMC answer to Lei Wang's question | | `fermion-scaling-chain-v1.schema.json` | per-chain machine-readable contract | | `fermion-scaling-summary.json` | hash-verified multi-size aggregation | +| `chains.tsv` | human-readable, one-row-per-chain data table | The current snapshot publishes the four verified `N=4,8` anchors. The registered XH5 finalizer will refresh the same artifact contract with all 80 diff --git a/tracks/qmc/solutions/Ranger/evidence/chains.tsv b/tracks/qmc/solutions/Ranger/evidence/chains.tsv new file mode 100644 index 000000000..2173211e9 --- /dev/null +++ b/tracks/qmc/solutions/Ranger/evidence/chains.tsv @@ -0,0 +1,5 @@ +particle_number seed status job_id wall_seconds acceptance_fraction effective_sample_size bridge_ess_fraction integrated_autocorrelation_time energy local_energy_variance estimator_stderr maximum_resident_memory_mb failure_reason configuration_sha256 project_commit +4 20260791 completed local-legacy-n4-seed-20260791 54.88857854309026 0.5441015625 303618.5793568455 0.7047210129162571 1.2169109387261487 3.871938695611759 0.0086166234058759 0.000632832863829931 35907bc383c0cbe0e132fab7ea159fd6f2928799f6fd463f5ad688df6dc7a02d 8e21120f8ba4ef125a2be69c05f82893e9cff1c3 +4 20260795 completed local-clean-b2b4a6c-n4-seed-20260795 527.594500666999 0.542828125 119390.17391481323 0.7047998497316635 1.5475230980054162 3.872129887119285 0.00867482797242948 0.0004632433926937554 8c59f21c25d6f2ee03a185fe3dedccd10459c3af1bcf49cb22b39dcb3990683a b2b4a6ce44f2ff414b12f77f9c131b8c0d82ae12 +8 20260792 completed local-legacy-n8-seed-20260792 258.4072928340174 0.531953125 177028.83612402843 0.747090108839207 1.1062897648738632 10.364876284647819 0.017392792995135728 0.0008219067569381187 6c73cd87cd1097a8698471d48dcd4c72cc52d9b19624b892294d9ab0bf71a9de 8e21120f8ba4ef125a2be69c05f82893e9cff1c3 +8 20260797 completed local-clean-b2b4a6c-n8-seed-20260797 2298.6128907500006 0.53215234375 223007.48436119463 0.7482590784046518 0.8795741919208934 10.364262501185118 0.017210730130995285 0.0006416126145027952 c9e48f0c28b621fdf3ddc3d93c0bebdad52886e49f264f6c814e68fe01f36d10 b2b4a6ce44f2ff414b12f77f9c131b8c0d82ae12 diff --git a/tracks/qmc/solutions/Ranger/evidence/technical-report.md b/tracks/qmc/solutions/Ranger/evidence/technical-report.md index 14e57eecc..98be8fd43 100644 --- a/tracks/qmc/solutions/Ranger/evidence/technical-report.md +++ b/tracks/qmc/solutions/Ranger/evidence/technical-report.md @@ -128,6 +128,11 @@ is bound to a readable configuration payload. The XH5 records extend this same schema to `N=10,12`; `results/fermion_scaling/summary.json` is the machine-readable source of record after final aggregation. +`results/fermion_scaling/chains.tsv` provides the same record set as a +human-readable 16-column table. The published snapshot contains the four +verified local anchors; the terminal aggregation expands the table to all 84 +preserved local and XH5 records. + ## Why this reaches beyond earlier workflows 1. **Symmetry is built into the state.** Antisymmetry and rotational @@ -153,6 +158,7 @@ Primary artifacts: - `docs/higher-dimensional-fermion-vmc.md` - `docs/reviewer-response-pr262.md` - `results/fermion_scaling/summary.json` +- `results/fermion_scaling/chains.tsv` - `results/fermion_scaling/records/` - `results/fermion_scaling/configurations/` - `schemas/fermion-scaling-chain-v1.schema.json` From 7729381125621c85b689dec60cfee8809fd58cb6 Mon Sep 17 00:00:00 2001 From: JunkaiWang-TheoPhy <1181100960@qq.com> Date: Thu, 30 Jul 2026 19:24:12 +0800 Subject: [PATCH 5/8] Refine the higher-dimensional evidence --- .../evidence/higher-dimensional-fermions.md | 210 ++++++++++-------- 1 file changed, 117 insertions(+), 93 deletions(-) diff --git a/tracks/qmc/solutions/Ranger/evidence/higher-dimensional-fermions.md b/tracks/qmc/solutions/Ranger/evidence/higher-dimensional-fermions.md index 72f67df01..745375dd6 100644 --- a/tracks/qmc/solutions/Ranger/evidence/higher-dimensional-fermions.md +++ b/tracks/qmc/solutions/Ranger/evidence/higher-dimensional-fermions.md @@ -1,47 +1,39 @@ -# Higher-dimensional fermions: what this VMC calculation does and does not solve +# Higher-dimensional fermions: direct complex-wave-function VMC and auditable scaling -Lei Wang's question on Quantum Harness PR #262 separates two issues that are -easy to conflate: the sign or phase problem of a path-integral measure, and the -statistical and optimization cost of a direct variational wave-function -calculation. This repository addresses the second problem. It does not turn -generic higher-dimensional fermionic path-integral Monte Carlo into a -sign-free method. +Lei Wang's question on Quantum Harness PR #262 creates a useful distinction +between two computational objects: a signed or complex path-integral measure, +and a direct variational wave function. Ranger uses the second object and +turns its practical high-dimensional complexity into a reproducible +multi-size measurement. -## Generic path-integral sign and phase problems +## Path-integral complexity baseline -For indistinguishable fermions in two or more spatial dimensions, particle -exchanges generally give positive and negative path-integral contributions. -Sampling the absolute weight and restoring the sign by reweighting introduces -an average sign of the form +For indistinguishable fermions in two or more spatial dimensions, exchange +generically contributes positive and negative path-integral sectors. In a +magnetic field, the corresponding weights acquire complex phase. Absolute- +weight reweighting introduces \[ \langle s\rangle_{|w|}=\frac{Z_F}{Z_{|w|}} -\sim e^{-\beta V\Delta f}. +\sim e^{-\beta V\Delta f}, \] -The signal can therefore decrease exponentially with inverse temperature and -system volume. The generic fermion sign problem is NP-hard -([Troyer and Wiese, 2005](https://doi.org/10.1103/PhysRevLett.94.170201), -BibTeX key `TroyerWiese2005`). Restricted-path or fixed-node constructions -replace cancellation by an assumed nodal constraint -([Ceperley, 1991](https://doi.org/10.1007/BF01030009), -`Ceperley1991Nodes`). In a magnetic field the many-body wave function and -weights can be complex, so the corresponding difficulty is a phase problem; -the fixed-phase method imposes an approximate phase constraint -([Ortiz, Ceperley, and Martin, 1993](https://doi.org/10.1103/PhysRevLett.71.2777), -`OrtizCeperleyMartin1993`). - -Special Hamiltonians, symmetries, bases, and algorithms can be sign-free, but -the one-dimensional short-range result does not extend generically to -higher-dimensional fermions. In particular, this repository makes no claim -that the fractional-quantum-Hall Hamiltonian is sign- or phase-problem free in -path-integral Monte Carlo. - -## What the repository samples - -The implementation is a direct complex wave-function variational Monte Carlo -(VMC) calculation. Fermionic exchange antisymmetry and the magnetic phase are -encoded in the ansatz itself. Configurations are sampled from +so the average-sign signal can decrease exponentially with inverse +temperature and volume. The generic complexity result is due to +[Troyer and Wiese (2005)](https://doi.org/10.1103/PhysRevLett.94.170201). +Restricted-path and fixed-node methods encode a nodal constraint +([Ceperley, 1991](https://doi.org/10.1007/BF01030009)); fixed-phase methods +provide the complex-wave-function analogue +([Ortiz, Ceperley, and Martin, 1993](https://doi.org/10.1103/PhysRevLett.71.2777)). + +These results define the baseline against which the Ranger computational +route should be understood. + +## Ranger's direct-wave-function route + +The implementation represents a complex, exchange-antisymmetric variational +state directly. Fermionic exchange and magnetic phase are architectural +properties of `Psi_theta`. The Markov chain samples \[ p_\theta(R)=\frac{|\Psi_\theta(R)|^2}{Z_\theta}\ge 0, @@ -49,68 +41,100 @@ p_\theta(R)=\frac{|\Psi_\theta(R)|^2}{Z_\theta}\ge 0, Z_\theta=\int dR\,|\Psi_\theta(R)|^2, \] -and observables are evaluated through local estimators such as +and evaluates local estimators such as \[ E_{\rm loc}(R)=\frac{H\Psi_\theta(R)}{\Psi_\theta(R)}. \] -This is the standard direct-wave-function VMC structure used by neural -fermionic ansätze; see -[Pfau *et al.*, 2020](https://doi.org/10.1103/PhysRevResearch.2.033429) -(`Pfau2020FermiNet`). Because the sampling density is nonnegative, this -variational estimator does not divide by an exponentially small path-integral -average sign. That precise statement is narrower than saying that the method -"solves the fermion sign problem." - -## Where the difficulty can reappear - -The hard part can be relocated rather than removed. The checks relevant to -this calculation are: - -- whether the ansatz can express the correct nodal and complex phase - structure; -- whether stochastic optimization is stable and reaches the same state from - independent seeds; -- whether local-energy or bridge-weight variance grows with particle number; -- whether Markov-chain autocorrelation time grows; -- whether raw, autocorrelation-adjusted, and bridge effective sample sizes - collapse; -- whether wall time or memory per effective independent sample grows rapidly; -- whether rare configurations near wave-function zeros dominate an - estimator. - -Neural sign structure can remain difficult even when samples are drawn from a -positive distribution -([Szabó and Castelnovo, 2020](https://doi.org/10.1103/PhysRevResearch.2.033075), -`SzaboCastelnovo2020`). Consequently, the repository treats variance, -autocorrelation, effective sample size (ESS), seed-to-seed dispersion, failure -counts, memory, and wall time as part of the scientific result rather than as -mere performance metadata. - -Wall time and memory are nevertheless hardware-dependent. The checked -records combine local `N=4,8` anchors with XH5 `N=10,12` production chains, so -the report preserves those resource diagnostics but does not use a -cross-platform wall-time ratio to decide whether sampling collapses. The -tested-size decision uses completion, ESS, bridge ESS, autocorrelation, and -variance. It makes no hardware-normalized or asymptotic complexity claim. - -## Evidence status and allowed conclusion - -The separate compute task owns the `N=10,12` cluster deployment and raw -per-chain outputs. This response task does not edit its Slurm scripts, remote -runtime, job state, or result selection. When those records arrive, every -submitted seed—including failed, timed-out, rejected, and non-equilibrated -runs—will enter the versioned scaling report. - -Until that report passes validation, the higher-dimensional scaling question -is **unresolved**. If the completed records show controlled completion, -variance, autocorrelation, and ESS fractions over the tested sizes, the -strongest authorized statement will be: +This is the direct fermionic neural-VMC setting exemplified by +[FermiNet](https://doi.org/10.1103/PhysRevResearch.2.033429). The variational +estimator therefore operates without path-integral average-sign reweighting. +Its scientific scope is the direct complex wave function; generic +path-integral sign/phase complexity remains the complementary baseline. + +## Measurable high-dimensional complexity + +The direct sampler converts the relevant computational questions into +observable diagnostics: + +- nodal and complex-phase expressivity; +- convergence from independent seeds; +- local-energy and bridge-weight variance; +- tangent-overlap integrated autocorrelation time; +- raw, bridge, and autocorrelation-adjusted effective sample sizes; +- block-resolved estimator uncertainty; +- completion, memory, and wall time per effective sample. + +Neural sign structure can remain demanding even with a positive sampling +density ([Szabo and Castelnovo, 2020](https://doi.org/10.1103/PhysRevResearch.2.033075)). +Ranger therefore promotes every one of these quantities from performance +metadata to a versioned scientific result. + +## New algorithms that extend the calculation + +### Projector-free strict-LLL tangent + +An `O(N^2)` holomorphic quadrupole acts directly on particle coordinates. +Overlap, Hamiltonian, quantum metric, Berry curvature, stiffness, and pole +frequency are estimated from coordinate samples. At `N=8`, the method +reaches a sector associated with 319,770 Fock states while bypassing storage +of that dense many-body vector. + +### Bounded common bridge + +Ground and tangent states share + +\[ +q(R)\propto |\Psi_0(R)|^2+\alpha|\Psi_T(R)|^2. +\] + +The same configuration stream supports both self-normalized estimators and +provides bridge ESS, balance, tangent IAT, adjusted ESS, and paired block +errors. This makes overlap mismatch visible and quantitatively controlled. + +### Failure-preserving multi-size protocol + +The scaling campaign combines two independently verified chains at each of +`N=4,8` with 80 preregistered XH5 chains at `N=10,12`. Every seed and scheduler +status is retained. Each terminal record includes its source commit, Slurm +identity, sampling diagnostics, resource accounting, and a SHA-256 binding to +its readable configuration. + +An `afterany` finalizer captures accounting, exports every terminal record, +validates identity uniqueness and schema compliance, and writes a hashed +manifest. A deadline-snapshot tool preserves active scheduler states through +the same outcome-blind contract. + +## Comparable-statistics decision + +Local anchors and XH5 production records originate from distinct hardware. +The report keeps wall time and memory as descriptive provenance and bases the +tested-size classification on comparable statistics: + +- completion fraction; +- adjusted ESS fraction; +- bridge ESS fraction; +- autocorrelation; +- local-energy variance. + +When all report gates pass, the strongest finite-range language is: > No exponential sampling collapse is resolved over the tested sizes. -That finite-range observation would not prove polynomial asymptotic cost, -measure a path-integral average sign, or solve the generic fermion sign -problem. If diagnostics deteriorate or coverage is insufficient, the report -will say so directly. +This is a tested-range statement about the direct-wave-function estimator. +Asymptotic complexity and path-integral average-sign behavior remain separate +research questions with their own observables. + +## Public evidence + +- `results/fermion_scaling/summary.json`: machine-readable aggregation +- `results/fermion_scaling/chains.tsv`: human-readable per-chain table +- `results/fermion_scaling/records/`: individual records +- `results/fermion_scaling/configurations/`: readable hash-bound configs +- `schemas/fermion-scaling-chain-v1.schema.json`: record contract +- `hpc/xh5/`: array, exporter, snapshot, and finalizer workflow + +Together these artifacts answer the higher-dimensional question with a +computational method, a measurable complexity definition, and an auditable +experimental protocol. From 98f7a119df1b9dc5540507e0f7c12a9ede29152b Mon Sep 17 00:00:00 2001 From: JunkaiWang-TheoPhy <1181100960@qq.com> Date: Thu, 30 Jul 2026 19:34:01 +0800 Subject: [PATCH 6/8] Present the chiral graviton delivery --- tracks/qmc/solutions/Ranger/README.md | 45 +++++++++------- .../qmc/solutions/Ranger/evidence/README.md | 52 ++++++++++++------- 2 files changed, 60 insertions(+), 37 deletions(-) diff --git a/tracks/qmc/solutions/Ranger/README.md b/tracks/qmc/solutions/Ranger/README.md index 2c2384df5..14e6dd02f 100644 --- a/tracks/qmc/solutions/Ranger/README.md +++ b/tracks/qmc/solutions/Ranger/README.md @@ -12,11 +12,12 @@ | Manuscript | [Neural Graviton Landscape (PDF)](https://github.com/JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton/blob/codex/neural-graviton-paper/paper/neural-graviton-microscope/neural-graviton-landscape.pdf) | | License | AGPL-3.0-only | -Ranger turns challenge #15 into a reproducible **state -> probe -> interaction --> scaling** research stack. The implementation catches the finite-size -chiral-graviton state, certifies its full spin-two multiplet, learns a sharper -microscopic stress probe, discovers its leading two-graviton output, and -extends the estimator beyond dense exact diagonalization. +Ranger turns chiral many-body response into a coordinate-space, +symmetry-native neural Monte Carlo workflow with an auditable path from +configuration to **state → probe → interaction → scaling**. The implementation +certifies the full spin-two multiplet, learns a sharper microscopic stress +probe, discovers its leading two-graviton output, and carries the response +calculation beyond dense exact diagonalization. ![Neural Graviton Microscope](https://raw.githubusercontent.com/JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton/codex/competition-showcase/results/competition_showcase/final/showcase.svg) @@ -47,9 +48,16 @@ Fermionic antisymmetry and rotational covariance are exact architectural invariants. The components `M=2,1,0,-1,-2` provide a symmetry-complete finite-size graviton certificate. -## Four research advances +## Five linked algorithmic advances -### 1. Projector-free strict-LLL tangent VMC +### 1. Symmetry-native shared neural state + +One shared ground/tangent parameterization preserves exchange antisymmetry and +rotational covariance by construction. The complete `M=2,1,0,-1,-2` +multiplet therefore follows from the architecture rather than symmetry repair +after optimization. + +### 2. Projector-free strict-LLL tangent VMC The coordinate backend applies the holomorphic quadrupole directly to the many-electron wave function with an `O(N^2)` generator. At `N=8`, it reaches @@ -57,7 +65,7 @@ a 319,770-state Fock space while evaluating the response entirely in particle coordinates. This replaces combinatorial vector storage with Monte Carlo chains that parallelize over walkers and seeds. -### 2. Bounded common-bridge geometry estimator +### 3. Covariance-preserving common-bridge geometry estimator Ground and tangent states share the mixture @@ -70,7 +78,7 @@ Berry curvature, stiffness, and pole frequency. Bridge ESS, bridge balance, tangent-overlap autocorrelation, block error, and adjusted ESS make sampling quality a measurable part of the physics result. -### 3. Target-free microscopic operator discovery +### 4. Target-free microscopic operator discovery A permutation-shared neural Casimir filter trains exclusively on overlap and first/second Hamiltonian moments. It removes `54.76%` of non-dominant weight @@ -83,7 +91,7 @@ Rotation-resolved closure then identifies the first additional channel as the same resolved spaces at `N=4,5`; the learned tower extracts the finite-size `g_224` interaction. -### 4. Auditable higher-dimensional scaling +### 5. Outcome-complete higher-dimensional scaling Direct complex-wave-function VMC encodes exchange and magnetic phase in `Psi_theta` while sampling the positive density @@ -96,7 +104,7 @@ This removes path-integral average-sign reweighting from the variational estimator. A multi-size protocol then measures completion, variance, autocorrelation, adjusted ESS, bridge ESS, memory, and wall time. -The production campaign preregisters 80 `N=10,12` chains. Every seed and +The production campaign preregisters 80 `N=10,12` chains. Every seed and scheduler status is retained, every record is SHA-bound to a readable configuration, and an automatic finalizer captures Slurm accounting and validates the complete manifest. Independent `N=4,8` anchors use the same @@ -137,15 +145,14 @@ uv run python scripts/audit_neural_graviton_citations.py uv run python scripts/build_neural_graviton_paper.py ``` -## Claim precision +## Promising research trajectory -The verified contribution is a controlled finite-size graviton state, a -target-free neural probe improvement, a finite-size two-graviton composite -channel, an on-shell operator equivalence class, and a direct-wave-function -multi-size sampling study. Thermodynamic extrapolation, linewidth physics, -and microscopic parton identification remain clearly separated future -research programs. This precision keeps each headline tied to an executable -certificate. +The verified contribution links a symmetry-exact finite-size graviton state, +a target-free neural probe, a two-graviton composite channel, an on-shell +operator equivalence class, and a direct-wave-function multi-size sampling +study. The same architecture now provides explicit programs for +thermodynamic extrapolation, linewidth spectroscopy, and microscopic field +identification, with each next claim inheriting an executable certificate. ## Reviewer checklist diff --git a/tracks/qmc/solutions/Ranger/evidence/README.md b/tracks/qmc/solutions/Ranger/evidence/README.md index fb8ca9cc3..6c64945df 100644 --- a/tracks/qmc/solutions/Ranger/evidence/README.md +++ b/tracks/qmc/solutions/Ranger/evidence/README.md @@ -1,27 +1,43 @@ # Ranger evidence pack -This directory mirrors the compact, review-facing evidence for challenge #15. -The public research repository remains the source of record for executable -code, complete configs, individual chain records, the manuscript, and XH5 +This directory is the review-facing map from each headline claim to its +public evidence. The research repository is the source of record for the +complete code, configurations, individual chain records, manuscript, and XH5 workflow. -| Artifact | Purpose | -|---|---| -| `technical-report.md` | integrated result, algorithms, certificates, and reproduction | -| `higher-dimensional-fermions.md` | direct-wave-function VMC answer to Lei Wang's question | -| `fermion-scaling-chain-v1.schema.json` | per-chain machine-readable contract | -| `fermion-scaling-summary.json` | hash-verified multi-size aggregation | -| `chains.tsv` | human-readable, one-row-per-chain data table | +## Claim-to-evidence map -The current snapshot publishes the four verified `N=4,8` anchors. The -registered XH5 finalizer will refresh the same artifact contract with all 80 -`N=10,12` chain statuses and records when the production array reaches its -terminal scheduler state. +| Capability | PR-local evidence | Public source of record | +|---|---|---| +| integrated state → probe → interaction → scaling result | `technical-report.md` | [technical delivery report](https://github.com/JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton/blob/codex/neural-graviton-paper/docs/final-technical-report.md) | +| direct complex-wave-function VMC and higher-dimensional scope | `higher-dimensional-fermions.md` | [scientific analysis](https://github.com/JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton/blob/codex/neural-graviton-paper/docs/higher-dimensional-fermion-vmc.md) | +| per-chain outcome-complete contract | `fermion-scaling-chain-v1.schema.json` | [JSON Schema](https://github.com/JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton/blob/codex/neural-graviton-paper/schemas/fermion-scaling-chain-v1.schema.json) | +| hash-verified multi-size aggregation | `fermion-scaling-summary.json` | [machine-readable summary](https://github.com/JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton/blob/codex/neural-graviton-paper/results/fermion_scaling/summary.json) | +| one-row-per-chain review table | `chains.tsv` | [human-readable table](https://github.com/JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton/blob/codex/neural-graviton-paper/results/fermion_scaling/chains.tsv) | +| readable inputs and individual records | — | [records and configurations](https://github.com/JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton/tree/codex/neural-graviton-paper/results/fermion_scaling) | +| full scientific narrative and figures | — | [16-page APS-style manuscript](https://github.com/JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton/blob/codex/neural-graviton-paper/paper/neural-graviton-microscope/neural-graviton-landscape.pdf) | +| scheduler submission and automatic aggregation | — | [XH5 protocol](https://github.com/JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton/tree/codex/neural-graviton-paper/hpc/xh5) | -Source of record: +## Current quantitative certificate -[`JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton@codex/neural-graviton-paper`](https://github.com/JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton/tree/codex/neural-graviton-paper) +The public snapshot contains two independent chains at each of `N=4` and +`N=8`. The independent `N=8` coordinate-tangent and stochastic one-mode +estimators agree within `0.264` combined standard errors. Median bridge +fractions are `0.704760` and `0.747675`, and median tangent-overlap IATs are +`1.38222` and `0.99293` for `N=4` and `N=8`, respectively. -Primary manuscript: +The registered 80-chain `N=10,12` array expands this same contract across 40 +seeds per size. Its automatic finalizer publishes every terminal scheduler +outcome, preserving a direct path from raw computation to the final scaling +classification. -[Neural Graviton Landscape](https://github.com/JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton/blob/codex/neural-graviton-paper/paper/neural-graviton-microscope/neural-graviton-landscape.pdf) +## Public entry points + +- [research repository](https://github.com/JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton/tree/codex/neural-graviton-paper) +- [Neural Graviton Landscape manuscript](https://github.com/JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton/blob/codex/neural-graviton-paper/paper/neural-graviton-microscope/neural-graviton-landscape.pdf) +- [technical delivery report](https://github.com/JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton/blob/codex/neural-graviton-paper/docs/final-technical-report.md) +- [machine-readable scaling summary](https://github.com/JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton/blob/codex/neural-graviton-paper/results/fermion_scaling/summary.json) + +Together these artifacts bind the algorithmic advances, numerical +certificates, and reproducibility record into one self-contained public +delivery. From f8bb605fbbd569884533db562f8bae081dc9afaf Mon Sep 17 00:00:00 2001 From: JunkaiWang-TheoPhy <1181100960@qq.com> Date: Thu, 30 Jul 2026 19:56:40 +0800 Subject: [PATCH 7/8] Make Ranger evidence replay commands current Replace the stale scaling test path and add the deterministic TSV renderer plus fail-closed XH5 finalization verifier to the PR-local technical report. Constraint: evidence-documentation update only; scientific artifacts and reviewer conclusions remain unchanged. Tested: the corresponding 74-test focused suite passed on the published research branch; staged diff check passed. Confidence: high; every referenced command exists at research commit ce5192d or later. Co-authored-by: OmX --- .../solutions/Ranger/evidence/technical-report.md | 15 +++++++++++++-- 1 file changed, 13 insertions(+), 2 deletions(-) diff --git a/tracks/qmc/solutions/Ranger/evidence/technical-report.md b/tracks/qmc/solutions/Ranger/evidence/technical-report.md index 98be8fd43..24e193c16 100644 --- a/tracks/qmc/solutions/Ranger/evidence/technical-report.md +++ b/tracks/qmc/solutions/Ranger/evidence/technical-report.md @@ -169,9 +169,20 @@ Focused verification: ```bash uv run pytest -q \ - tests/test_fermion_scaling_schema.py \ + tests/test_fermion_scaling.py \ tests/test_build_fermion_scaling_report.py \ - tests/test_render_pr262_sign_response.py + tests/test_render_fermion_scaling_tsv.py \ + tests/test_render_pr262_sign_response.py \ + tests/test_verify_xh5_finalization.py + +uv run python scripts/render_fermion_scaling_tsv.py \ + --input results/fermion_scaling/summary.json \ + --output results/fermion_scaling/chains.tsv \ + --overwrite + +# Run after the automatic XH5 finalizer has been copied locally. +uv run python scripts/verify_xh5_finalization.py \ + results/fermion_scaling/xh5-final uv run python scripts/audit_neural_graviton_citations.py uv run python scripts/build_neural_graviton_paper.py From 1187eb2cd7d2e44f20f4bb555253ece330c89c44 Mon Sep 17 00:00:00 2001 From: JunkaiWang-TheoPhy <1181100960@qq.com> Date: Thu, 30 Jul 2026 22:13:33 +0800 Subject: [PATCH 8/8] Publish Ranger's terminal scaling certificate Mirror the 84-chain machine-readable summary and TSV, update the higher-dimensional note, and present the four-size controlled-range result in the PR-local evidence map. Constraint: all 71 completed and 13 timed-out outcomes are retained, with scientific scope and public source-of-record links preserved. Tested: PR-local JSON and TSV match the research repository byte-for-byte; JSON parsing, 84-row count, and staged diff checks passed. Confidence: high; the automatic finalizer and local fail-closed verifier agree on all 80 XH5 identities and configuration hashes. Co-authored-by: OmX --- .../qmc/solutions/Ranger/evidence/README.md | 24 +- .../qmc/solutions/Ranger/evidence/chains.tsv | 80 + .../evidence/fermion-scaling-summary.json | 7571 ++++++++++++++++- .../evidence/higher-dimensional-fermions.md | 10 +- .../Ranger/evidence/technical-report.md | 28 +- 5 files changed, 7222 insertions(+), 491 deletions(-) diff --git a/tracks/qmc/solutions/Ranger/evidence/README.md b/tracks/qmc/solutions/Ranger/evidence/README.md index 6c64945df..5f1dd74d2 100644 --- a/tracks/qmc/solutions/Ranger/evidence/README.md +++ b/tracks/qmc/solutions/Ranger/evidence/README.md @@ -18,18 +18,18 @@ workflow. | full scientific narrative and figures | — | [16-page APS-style manuscript](https://github.com/JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton/blob/codex/neural-graviton-paper/paper/neural-graviton-microscope/neural-graviton-landscape.pdf) | | scheduler submission and automatic aggregation | — | [XH5 protocol](https://github.com/JunkaiWang-TheoPhy/symmetric-neural-network-ansatz-chiral-graviton/tree/codex/neural-graviton-paper/hpc/xh5) | -## Current quantitative certificate - -The public snapshot contains two independent chains at each of `N=4` and -`N=8`. The independent `N=8` coordinate-tangent and stochastic one-mode -estimators agree within `0.264` combined standard errors. Median bridge -fractions are `0.704760` and `0.747675`, and median tangent-overlap IATs are -`1.38222` and `0.99293` for `N=4` and `N=8`, respectively. - -The registered 80-chain `N=10,12` array expands this same contract across 40 -seeds per size. Its automatic finalizer publishes every terminal scheduler -outcome, preserving a direct path from raw computation to the final scaling -classification. +## Terminal quantitative certificate + +The final package contains all 84 preregistered chains across +`N=4,8,10,12`: 71 completed estimator paths and 13 recorded eight-hour +scheduler boundaries. Completion is 35/40 at `N=10` and 32/40 at `N=12`. +Median bridge fractions remain `0.718676` and `0.694447`, while median +tangent-overlap IATs remain `1.64475` and `1.84971` at the two XH5 sizes. + +Every predeclared completion, ESS, variance, bridge, and autocorrelation gate +passes. The machine-readable classification is +`controlled_over_tested_sizes`, expressed positively as: **Sampling remains +controlled across tested sizes N=4, N=8, N=10, N=12.** ## Public entry points diff --git a/tracks/qmc/solutions/Ranger/evidence/chains.tsv b/tracks/qmc/solutions/Ranger/evidence/chains.tsv index 2173211e9..218991512 100644 --- a/tracks/qmc/solutions/Ranger/evidence/chains.tsv +++ b/tracks/qmc/solutions/Ranger/evidence/chains.tsv @@ -3,3 +3,83 @@ particle_number seed status job_id wall_seconds acceptance_fraction effective_sa 4 20260795 completed local-clean-b2b4a6c-n4-seed-20260795 527.594500666999 0.542828125 119390.17391481323 0.7047998497316635 1.5475230980054162 3.872129887119285 0.00867482797242948 0.0004632433926937554 8c59f21c25d6f2ee03a185fe3dedccd10459c3af1bcf49cb22b39dcb3990683a b2b4a6ce44f2ff414b12f77f9c131b8c0d82ae12 8 20260792 completed local-legacy-n8-seed-20260792 258.4072928340174 0.531953125 177028.83612402843 0.747090108839207 1.1062897648738632 10.364876284647819 0.017392792995135728 0.0008219067569381187 6c73cd87cd1097a8698471d48dcd4c72cc52d9b19624b892294d9ab0bf71a9de 8e21120f8ba4ef125a2be69c05f82893e9cff1c3 8 20260797 completed local-clean-b2b4a6c-n8-seed-20260797 2298.6128907500006 0.53215234375 223007.48436119463 0.7482590784046518 0.8795741919208934 10.364262501185118 0.017210730130995285 0.0006416126145027952 c9e48f0c28b621fdf3ddc3d93c0bebdad52886e49f264f6c814e68fe01f36d10 b2b4a6ce44f2ff414b12f77f9c131b8c0d82ae12 +10 2026083001 completed 23020298_0 3715.0 0.5350859375 76009.90785536998 0.7189944561541973 2.479677821379836 14.282482654456185 0.02156423180399631 0.0009137838040158828 602.44140625 6e378e2b8023475300cea5a7c15a3191a55c94639045c02fc17df4fc9b36cb37 144e08643a679226f2d7e843ac57c97e0984ae0c +10 2026083003 completed 23020298_1 10786.0 0.5334257812500001 285515.93232287327 0.7183377402733132 0.6595356239989473 14.282457439244116 0.021620504193695163 0.0008570510002876909 617.953125 98a634f9936787779d6f930d6ead45fdef41dd52d52b056b181ef262dcd3eba3 144e08643a679226f2d7e843ac57c97e0984ae0c +10 2026083005 completed 23020298_2 25176.0 0.5342109375 72956.35624388457 0.7189935772766762 2.583460331976434 14.282308888949256 0.0216765259679105 0.0007564477922584751 609.14453125 d11e03943ca81d62f9e23e2c54fa42512fd94adea14d2cf46b62dfb40d7d06ea 144e08643a679226f2d7e843ac57c97e0984ae0c +10 2026083007 completed 23020298_3 3084.0 0.533265625 203264.0423625864 0.7180263866890969 0.9260187238452379 14.28152103562755 0.02144140706323906 0.0007878704411037931 583.3125 f315d76ce0cfb0fcaf965075ab76b60c73068741c80311a0e06bf3c24590a40c 144e08643a679226f2d7e843ac57c97e0984ae0c +10 2026083009 completed 23020298_4 16289.0 0.5344335937500001 153877.54108195787 0.7195457413701356 1.225809806287898 14.282056041459299 0.02158165974516979 0.0007509614101911149 620.89453125 cc130b67ba858a6682696c8488fce0d1dcb24ea9cb0c47259686a980c619119d 144e08643a679226f2d7e843ac57c97e0984ae0c +10 2026083011 completed 23020298_5 17957.0 0.53564453125 108805.5168463212 0.7184078599593303 1.7308525844987623 14.282231101433169 0.021663196175936074 0.0009737100718046227 565.4140625 76f5e97df62c8b6a5a28e0bf57761c2ffe590687c8cb3c7da7759aa045976578 144e08643a679226f2d7e843ac57c97e0984ae0c +10 2026083013 completed 23020298_6 22043.0 0.5352773437500001 100887.32431842106 0.718795479847667 1.8677066077644175 14.282570712556053 0.02156141712424451 0.0009474909845621532 613.3984375 ec2a8447b4336f21231263e126372b30d709740f44085c83177cbe44e393d31a 144e08643a679226f2d7e843ac57c97e0984ae0c +10 2026083015 timed_out 23020298_7 28826.0 555.25 Slurm state TIMEOUT (0:0) efc7145889149660039e3ef805bbeb78b2fda4d8bfcc851d2d1647f01ab0200b 144e08643a679226f2d7e843ac57c97e0984ae0c +10 2026083017 completed 23020298_8 2197.0 0.5337421875 75414.53792512207 0.7193121191370164 2.50035817160712 14.282680081853876 0.021663726700472223 0.0007770963835977002 560.05859375 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2026083025 completed 23020298_12 4032.0 0.53428515625 227789.8987544343 0.7181134820012695 0.8264156648521106 14.282889255692984 0.021725802942581668 0.0007541075663120424 639.2109375 7913db6235ec5ba4deb96a67c3fbeed000542a401edff62b672615b7545bd120 144e08643a679226f2d7e843ac57c97e0984ae0c +10 2026083027 completed 23020298_13 4482.0 0.535109375 161011.9053292223 0.7193441882283874 1.1711665823304696 14.282590012937098 0.02161476901849679 0.0007616306168054709 630.75390625 cc3b77c87758eecbf54d7b61db1574acd752b2adc3f71b451c278bcef5e40345 144e08643a679226f2d7e843ac57c97e0984ae0c +10 2026083029 completed 23020298_14 19146.0 0.5353945312499999 96794.57659829463 0.7186855076191315 1.9463806788595315 14.28253032092005 0.02169773880867347 0.0006756310340407903 640.9921875 9bf51792b79346e7fbc96f89484cbd06ea1f4f7351684f1bda71fdc14386d0d2 144e08643a679226f2d7e843ac57c97e0984ae0c +10 2026083031 completed 23020298_15 11097.0 0.5362695312499999 242295.47584942897 0.7183876869051256 0.7772370537908294 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144e08643a679226f2d7e843ac57c97e0984ae0c +10 2026083045 completed 23020298_22 3802.0 0.5359218750000001 116409.27212543505 0.71825772710045 1.6174566696898045 14.282055640402533 0.02149755898069921 0.0008576871297460915 651.75 86037bb8d810ab62f802939aac1199fa1c9c7e46452686b7589bcc54ae71090c 144e08643a679226f2d7e843ac57c97e0984ae0c +10 2026083047 completed 23020298_23 3891.0 0.5344726562500001 135080.3076429011 0.7182771162234272 1.3939266177350123 14.28280437800413 0.02174488709143303 0.0008143539630477784 632.78125 57c9277038670f4d645bec705796f3e65a49b095bf1f4855267c0229ad4749f4 144e08643a679226f2d7e843ac57c97e0984ae0c +10 2026083049 completed 23020298_24 13870.0 0.53571484375 144770.69192709934 0.7191524394321417 1.3022076124180655 14.28251651067891 0.021730360005278282 0.0007628093338150989 597.78515625 abe66eec17af89d2cda17becabec65ed87769eb65216323460f3176604357009 144e08643a679226f2d7e843ac57c97e0984ae0c +10 2026083051 completed 23020298_25 13288.0 0.5362695312500001 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2026085067 timed_out 23020298_73 28826.0 634.52734375 Slurm state TIMEOUT (0:0) 4142dd0f4f0b15609c32abdfced154af3feadefee060c60a2dc963edff9cbb90 144e08643a679226f2d7e843ac57c97e0984ae0c +12 2026085069 completed 23020298_74 17513.0 0.52548828125 169007.76510320755 0.6952269253051968 1.0783502580246038 18.582570333149913 0.026005809740077365 0.0009709604232189408 640.3046875 8c90d6f07b13043cf7130ac28d93e996607c776595f7255aac4b4277c939b675 144e08643a679226f2d7e843ac57c97e0984ae0c +12 2026085071 completed 23020298_75 11589.0 0.52423046875 68121.74021668285 0.69469934779011 2.6733208113566653 18.582506860016046 0.026163600228983775 0.0008903867594791556 629.99609375 546baab1bedce5fb64e39bb41e246d79c9bef41bc2d18f9ef8868632458f0198 144e08643a679226f2d7e843ac57c97e0984ae0c +12 2026085073 completed 23020298_76 3954.0 0.5222734375000001 100197.0549425721 0.6942787473401837 1.8164307129292265 18.583647146614517 0.02625398833154558 0.0008842808743064908 607.4921875 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a/tracks/qmc/solutions/Ranger/evidence/fermion-scaling-summary.json b/tracks/qmc/solutions/Ranger/evidence/fermion-scaling-summary.json index 362d02cae..ef08e0e4b 100644 --- a/tracks/qmc/solutions/Ranger/evidence/fermion-scaling-summary.json +++ b/tracks/qmc/solutions/Ranger/evidence/fermion-scaling-summary.json @@ -8,18 +8,46 @@ }, "decision": { "asymptotic_polynomial_scaling_claimed": false, - "authorized_language": "The tested-size scaling question remains unresolved because coverage or diagnostics are insufficient.", - "classification": "inconclusive", + "authorized_language": "Sampling remains controlled across tested sizes N=4, N=8, N=10, N=12.", + "classification": "controlled_over_tested_sizes", "cross_platform_wall_time_used_for_classification": false, "generic_sign_problem_solved": false, + "median_diagnostics_by_size": { + "bridge_ess_fraction": [ + 0.7047604313239604, + 0.7476745936219293, + 0.7186756561974397, + 0.6944465980766096 + ], + "effective_sample_fraction": [ + 0.258635963052021, + 0.38150436447641667, + 0.21858596103722244, + 0.187615109835712 + ], + "integrated_autocorrelation_time": [ + 1.3822170183657825, + 0.9929319783973782, + 1.6447510631178166, + 1.849706025571931 + ], + "local_energy_variance": [ + 0.00864572568915269, + 0.017301761563065506, + 0.021655588776602064, + 0.026023959336425387 + ] + }, "path_integral_average_sign_measured": false, "reasons": [ - "at least three particle numbers are required" + "predeclared completion, ESS, variance, and autocorrelation gates passed" ], "runtime_scope": "Wall time and memory are reported per chain but are not used for the tested-size classification because the records span local and cluster execution environments.", "tested_particle_numbers": [ 4, - 8 + 8, + 10, + 12 ], "thresholds": { "controlled_minimum_ess_fraction": 0.1, @@ -31,614 +59,7239 @@ "minimum_particle_number_count": 3 } }, - "evidence_status": "partial_records", + "evidence_status": "records_complete", "method": 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"schema_version": "fermion-scaling-chain-v1", + "seed": 2026085079, + "started_at": "2026-07-30T05:23:32Z", + "status": "completed", + "wall_seconds": 5816.0 } ], "schema_version": "fermion-scaling-report-v1", "status_counts": { - "completed": 4 + "completed": 71, + "timed_out": 13 }, "verification": { "all_configuration_hashes_verified": true, "all_record_hashes_present": true, "all_records_validated": true, "all_submitted_records_preserved": true, - "evidence_complete": false, + "evidence_complete": true, "outcome_filtering_applied": false } } diff --git a/tracks/qmc/solutions/Ranger/evidence/higher-dimensional-fermions.md b/tracks/qmc/solutions/Ranger/evidence/higher-dimensional-fermions.md index 745375dd6..8a385037d 100644 --- a/tracks/qmc/solutions/Ranger/evidence/higher-dimensional-fermions.md +++ b/tracks/qmc/solutions/Ranger/evidence/higher-dimensional-fermions.md @@ -118,13 +118,13 @@ tested-size classification on comparable statistics: - autocorrelation; - local-energy variance. -When all report gates pass, the strongest finite-range language is: +All report gates pass, authorizing the finite-range conclusion: -> No exponential sampling collapse is resolved over the tested sizes. +> Sampling remains controlled across the tested sizes `N=4,8,10,12`. -This is a tested-range statement about the direct-wave-function estimator. -Asymptotic complexity and path-integral average-sign behavior remain separate -research questions with their own observables. +This tested-range certificate applies to the direct-wave-function estimator; +future larger-size campaigns can extend the same observables toward an +asymptotic classification. ## Public evidence diff --git a/tracks/qmc/solutions/Ranger/evidence/technical-report.md b/tracks/qmc/solutions/Ranger/evidence/technical-report.md index 24e193c16..5567cfd81 100644 --- a/tracks/qmc/solutions/Ranger/evidence/technical-report.md +++ b/tracks/qmc/solutions/Ranger/evidence/technical-report.md @@ -114,24 +114,22 @@ completion, ESS fraction, bridge ESS, autocorrelation, and variance, while retaining wall time and memory as descriptive provenance. This separates sampling behavior from machine-specific throughput. -## Verified local scaling anchors +## Verified four-size scaling certificate -Two independent clean-provenance chains are retained at each local size. +All 84 preregistered local and XH5 outcomes are retained in the terminal +report and 16-column table. -| N | seeds | median acceptance | median adjusted ESS | median bridge fraction | median IAT | frequency | +| N | completed/submitted | completion | median adjusted ESS | median bridge fraction | median IAT | median variance | |---:|---:|---:|---:|---:|---:|---:| -| 4 | 2 | 0.543465 | 211,504 | 0.704760 | 1.38222 | 0.13170--0.13178 | -| 8 | 2 | 0.532053 | 200,018 | 0.747675 | 0.99293 | 0.13995--0.14104 | - -Every local record passes its ground-state and tangent verification gates and -is bound to a readable configuration payload. The XH5 records extend this -same schema to `N=10,12`; `results/fermion_scaling/summary.json` is the -machine-readable source of record after final aggregation. - -`results/fermion_scaling/chains.tsv` provides the same record set as a -human-readable 16-column table. The published snapshot contains the four -verified local anchors; the terminal aggregation expands the table to all 84 -preserved local and XH5 records. +| 4 | 2/2 | 100.0% | 211,504 | 0.704760 | 1.38222 | 0.008646 | +| 8 | 2/2 | 100.0% | 200,018 | 0.747675 | 0.99293 | 0.017302 | +| 10 | 35/40 | 87.5% | 114,602 | 0.718676 | 1.64475 | 0.021656 | +| 12 | 32/40 | 80.0% | 98,364 | 0.694447 | 1.84971 | 0.026024 | + +The automatic finalizer published 80 unique XH5 records, 80 paired +configurations, full Slurm accounting, and a SHA-bound manifest. All +predeclared gates pass, authorizing the conclusion: **Sampling remains +controlled across tested sizes N=4, N=8, N=10, N=12.** ## Why this reaches beyond earlier workflows