Oracle Gap and Signal Fidelity: A Fixed-Pool Diagnostic for Test-Time Collaboration

A new arXiv paper (2607.17531v1) introduces a fixed-pool diagnostic framework for test-time collaboration in LLMs, decomposing net gains into recoverable mass, verification-signal coverage, conditional selection quality, and harm. On LiveCodeBench, a public-test verifier (MCC 0.825) gained +8.14 percentage points over a first-sample baseline, while a generated-test verifier (MCC 0.248) improved by +2.70pp with near-zero harm. The framework provides a practical pre-deployment diagnostic: estimate the oracle gap, then measure coverage, signal fidelity, and harm before investing in collaboration.

arXiv:2607.17531v1 Announce Type: new Abstract: Test-time collaboration, including self-consistency, best-of-N selection, critic models, and verifier pipelines, is often credited with broadly improving LLM reasoning, yet its gains are uneven and sometimes negative. We ask when training-free collaboration should be expected to help. For a fixed candidate pool, we decompose a selector or verifier's net gain into measurable factors: recoverable mass, verification-signal coverage, conditional selection quality, and harm to already-correct outputs. This reframes collaboration as a candidate-selection problem rather than as an intrinsic property of a multi-agent topology. Across LiveCodeBench, MATH Level-5 hard subjects, and GPQA-Diamond, gains are bounded first by the oracle gap and then by signal fidelity, which we measure directly as candidate-level agreement between verifier verdicts and official labels. On LiveCodeBench, a public-test verifier MCC 0.825 gains +8.14 percentage points pp over a first-sample baseline; a generated-test verifier MCC 0.248 improves by +2.70pp and is not statistically distinguishable from an LLM selector, but operates at near-zero harm versus the selector's 4.69% harm rate. On MATH, a symbolic answer-equivalence selector beats self-consistency by +4.67pp, while LLM selectors are negative. On GPQA-Diamond, recoverable mass is only 3.03% and 87.54% of candidate pools are answer-identical; a weaker model's pools shrink both further, suggesting that oracle gap is a joint property of task, model, and sampling configuration. Our framework yields a practical pre-deployment diagnostic: estimate the oracle gap, then measure coverage, signal fidelity, and harm before investing in collaboration.