aiqbench analysis
This research proposes using output entropy derived from standard inference logs as a low-cost proxy for monitoring Large Language Model performance drift. The study demonstrates that these signals can effectively estimate domain-level quality on both verifiable benchmarks and subjective tasks, potentially reducing reliance on expensive external judges. By enabling cheaper continuous evaluation of model degradation, this approach offers practical utility for operationalizing LLMs at scale without requiring specialized hardware or extensive retraining.
Entropy Sentinel: Probing Entropy Traces for LLM Monitoring
arXiv:2601.09001v5 Announce Type: replace Abstract: Deploying LLMs raises two coupled challenges: (1) monitoring---estimating where a model underperforms as traffic drifts---and (2) prioritization---deciding where to intervene to close the largest performance gaps. We explore whether top-$k$ logprobs---cheap, consumer-accessible signals from standard inference---can serve as reliable proxies for domain-level quality of both verifiable and subjective tasks. We summarize each response's output-entropy profile into a compact vector, predict instance quality with a lightweight classifier, and then average predictions to yield a domain-level estimate. On verifiable tasks (ten STEM benchmarks, nine LLMs, exhaustive train/test compositions), estimates often track held-out accuracy, with several models showing near monotonic calibration. On subjective tasks, trained on LLM-judge scores over categorized real user conversations, several models track the judge's slice scores remarkably closely ($r$ up to $0.91$) and detect the worst-performing categories near-perfectly, though this is not the case for all LLMs. Where coupling holds, results suggest that entropy signals can support monitoring at a fraction of the cost of judge-based evaluation.
Abstract:Deploying LLMs raises two coupled challenges: (1) monitoring---estimating where a model underperforms as traffic drifts---and (2) prioritization---deciding where to intervene to close the largest performance gaps. We explore whether top-$k$ logprobs---cheap, consumer-accessible signals from standard inference---can serve as reliable proxies for domain-level quality of both verifiable and subjective tasks. We summarize each response's output-entropy profile into a compact vector, predict instance quality with a lightweight classifier, and then average predictions to yield a domain-level estimate. On verifiable tasks (ten STEM benchmarks, nine LLMs, exhaustive train/test compositions), estimates often track held-out accuracy, with several models showing near monotonic calibration. On subjective tasks, trained on LLM-judge scores over categorized real user conversations, several models track the judge's slice scores remarkably closely ($r$ up to $0.91$) and detect the worst-performing categories near-perfectly, though this is not the case for all LLMs. Where coupling holds, results suggest that entropy signals can support monitoring at a fraction of the cost of judge-based evaluation.
Computation and Language (cs.CL)
arXiv:2601.09001 [cs.CL]
(or arXiv:2601.09001v5 [cs.CL] for this version)
Full text under the publisher's open licence · 170 words
Related coverage
News
Quantum Computing Report · Jun 7, 2026
Spurious probes as a black-box alternative to activation probing
LessWrong · Sep 25, 2026
Autonomous Topology Mutation: Safe Runtime Restructuring for Multi-Agent LLM Systems with Capability, State, and Shadow Invariants
arXiv · Jul 24, 2026
Quantifying mixed-state entanglement via partial transpose and realignment moments
Quantum Journal · Aug 19, 2026