Fix gpt-5/reasoning-model accuracy: model-aware max_tokens default

[liana313]

Fixes #255.

Root cause

Reasoning models (gpt-5, o-series) spend hidden reasoning tokens from the same max_completion_tokens budget as the visible answer. Lotus's flat max_tokens=512 default starves them:

1. On non-trivial rows, gpt-5 burns the entire 512-token budget on internal reasoning (finish_reason='length')
2. The completion comes back with content='' (or OpenAI 400s with "Could not finish the message because max_tokens or model output limit was reached")
3. filter_postprocess finds neither output token in the empty string and silently coerces the row to default=True — bad accuracy, zero errors

Empirical confirmation (debug branch debug-gpt5-repro, runs in Actions)

Scenario	reasoning tokens	result
trivial sentiment claim @512	64	Answer: True ✓ (8/8 — easy tasks unaffected)
hard arithmetic claim @512	448–512	one row empty → defaulted → 5/6
same, @8000 budget	~512	6/6
hard ZS_COT @512	—	truncated/empty rows → 5/6
forced 64-token budget	64	finish_reason=length, content='', sometimes OpenAI 400
gpt-4o-mini @512 (control)	0	6/6

On real workloads (longer docs, harder predicates) most rows blow the 512 budget, which is why gpt-5 looked uniformly broken.

Fix

• LM default max_tokens is now model-aware: 8192 for reasoning models (detected via litellm.supports_reasoning), 512 otherwise. An explicit max_tokens always wins. max_completion_tokens is a cap, not a purchase — this only affects worst-case per-row cost on reasoning models.
• _get_top_choice now warns on finish_reason='length' with a reasoning-model-specific hint, so this failure mode is never silent again.
• Users can still pass reasoning_effort="minimal" (forwarded via kwargs) to trade reasoning depth for cost — verified working in the probes.

Tests

• New offline tests/test_lm_defaults.py (7 tests, no API calls) pinning the model-aware defaults; wired into the settings CI suite.
• End-to-end verification run on the debug branch: gpt-5 with pure library defaults on the previously-failing hard set.

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