The paper reports a preregistered audit of language‑model judges used as measurement instruments, revealing that the assumption that a model’s responses remain stable over time is invalid. Across nearly 53,000 audited requests, repeat rankings and byte‑identical replays fell far below required reliability thresholds, with three identified mechanisms—label‑to‑meaning bias, candidate gaps below the noise floor, and input permutation noise—explaining the discrepancy. The study proposes a three‑level snapshot‑identity framework, eight design rules, and a reporting checklist to prevent such reliability failures in future evaluations.
By Haoyaun Zhu, Jie Zhang
arXiv:2608. 20290v1 Announce Type: new Abstract: Whether a language model has improved itself is increasingly judged not by mean accuracy but by which individual problems it gains and loses.
By Cheng Xu, Nan Yan, Liming Chen, M-Tahar Kechadi
arXiv:2606. 15474v1 Announce Type: new Abstract: Continuous evaluation of LLM products relies on a strong LLM judge treated as ground truth: a cheap monitor scores every interaction and a team is paged when the score drifts down.
By Yitao Li
The study investigates why small language model agents tend to repeat a tool call that just failed. By recording the failed call and its error message in the transcript, the authors measure a negative corrective gain—agents are more likely to repeat the failed action, with a drop of about 1.03 nats per token. The problem is traced to the harness design rather than the model’s understanding of errors, and the authors show that replacing the verbatim call with a runtime-generated description of the failure can reduce this backfiring effect by 76%.
By Esmail Gumaan
AutoTuneBench introduces a trustworthy measurement protocol for evaluating how large language model agents auto‑tune GPU kernels and serving engines. The benchmark addresses four failure modes—strawman baselines, machine‑dependent timing, saturated tasks, and infrastructure defects—by enforcing code‑frozen protocols, database validation, anti‑cheat checks, pre‑registered comparisons, and external result anchoring. Using this protocol, the authors demonstrate that previously reported speedups are inflated, revealing more modest improvements across different engines and machines.
By Li Chen
arXiv:2608. 08239v1 Announce Type: new Abstract: LLM routers promise efficiency by matching each request to the cheapest adequate model, and are increasingly applied per step inside multi-step agents.
By Ashritha Gonuguntla