Deterministic verifier passes 66/66, but model assertions only 12/24: benchmark by pipeline layer
MuhammadMujtaba21 · reddit · 2026-08-24
The author reran their verification capability benchmark and found a sharp contrast: the deterministic part reproduced perfectly, while the model-related metric failed badly.
- 66 cases across 9 capability dimensions (claim binding, evidence graph integrity, deterministic calculation, rule application, contradiction detection, missing evidence detection, reproducibility, auditability), two runs on the same commit, both 66/66 passed, 0 failures.
- Yet the secondary metric modelassertioncorrectness was only 12/24 — half of the model-generated assertions were flawed.
The core lesson: deterministic verification capability ≠ model generation correctness ≠ end-to-end production reliability. A system can be fully reproducible internally while depending on a probabilistic model producing unreliable assertions.
The next benchmark version will locate the first invalid state along the pipeline: model output → parsing → schema validation → normalization → canonicalization → claim binding → evidence graph → deterministic verification → final outcome, instead of a bare PASS/FAIL. The author asks the community whether such LLM + deterministic systems should be scored layer by layer.
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