Accuracy Hides How Language Models Fail: Measuring Failure States Under Matched Output Budgets

Zongyou Yang; Yinghan Hou

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arXiv · 2607.24268

Accuracy Hides How Language Models Fail: Measuring Failure States Under Matched Output Budgets

Zongyou Yang·Yinghan Hou

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Abstract

Language-model benchmarks collapse two distinct measurement questions into a single accuracy score: whether a response reached an evaluable state, and whether its answer was judged correct. We introduce a two-layer evaluation framework that separates scorer-independent execution evidence, including termination, answer exposure, parseability, and completion length, from scorer-dependent correctness. Across 2,550 outputs from five fixed Qwen and DeepSeek configurations on MATH and ARC-Challenge, matched 2,048-token limits produce sharply different execution mixtures: 49 of 450 Qwen MATH outputs terminate without a final answer, compared with 5 of 300 DeepSeek MATH outputs and none of the 750 ARC outputs. Among the same 300 DeepSeek MATH question-model pairs, no missing-final length termination is observed at 8,192 tokens. A coverage-audited targeted verification study further shows that candidate-selection and aggregation policies can substantially alter comparative accuracy estimates. These results demonstrate that accuracy conflates execution case mix with verification policy. Evaluations of test-time methods should therefore report pre-intervention execution states, verification coverage, and scorer provenance alongside accuracy.

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Zongyou Yang, Yinghan Hou. 2026-07-30. Accuracy Hides How Language Models Fail: Measuring Failure States Under Matched Output Budgets. https://arxiv.org/abs/2607.24268

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