The study investigates whether a second review by a different large language model (LLM) improves error detection in code, documentation, and analyses. Using 30 artifacts with 150 planted errors and 900 review sessions across three reviewer models, the authors find that a top-tier cross-model reviewer performs similarly to same-model reviews in fresh sessions, identifies partly different errors, and that a combination of a fresh same-model review and a cross-model review detects more errors than two fresh same-model reviews. However, the results do not conclusively separate the effects of model difference from reviewer capability, and a lightweight cross-model reviewer does not outperform same-model review.
whyItMatters":"The findings suggest that while cross-model reviews can enhance error detection, the benefit depends on reviewer skill and the specific review strategy, informing how teams might structure LLM-based code review processes."
By Tae-Eun Song
arXiv:2606. 09046v1 Announce Type: new Abstract: Useful audits reveal not only how often a model fails, but also where its failures concentrate.
By Vyzantinos Repantis, Ameya Gawde, Harshvardhan Singh
The study investigates how varying the capability of a reviewer model in a large‑language‑model (LLM) execute‑review‑revise pipeline affects rejection decisions on 100 olympiad mathematics problems. A mid‑tier reviewer improves final accuracy by 12 percentage points (from 52 % to 64 %) without damaging answers, while a self‑reviewer detects errors best (85 % recall) but rejects too often and harms correct solutions. Below a certain capability threshold the reviewer becomes inert, changing none of the answers and doubling token cost.
By Faizan Tanveer
The paper introduces TRACE, a protocol designed to diagnose whether changes in verifier scores for large language model agents reflect actual changes in agent behavior or merely alterations in the evaluation process. TRACE works by applying targeted changes to evaluation components, running paired experiments, and re‑scoring unchanged trajectories to isolate the source of score variation. Experiments on synthetic tasks and public benchmark tasks demonstrate that seemingly significant score shifts can often be attributed to evaluation artifacts, while TRACE can also detect genuine behavioral changes.
By Radhika Gaonkar
The paper argues that accuracy is an inadequate metric for certifying a coordinator’s theory‑of‑mind in multi‑agent systems, because the required accuracy depends on deployment‑specific costs and errors are often dependent. Experiments on task assignment, Level‑Based Foraging, Hanabi, and human judgments show that accuracy can both approve harmful language‑model deployments and reject beneficial ones. The authors propose a Blind‑Spot Gate that evaluates error costs, pairwise interactions, and dependence without deployment, achieving better safety outcomes than accuracy alone.
By Will Beaumaster, Paul Schrater
The paper introduces Janus, a method for validating error patterns in language models by comparing error rates across predefined yes/no properties and using shuffled decoy labels to set significance thresholds. Janus requires that a pattern’s error difference surpasses the decoy-derived threshold and is replicated on held‑out data before reporting. Experiments on a controlled code‑finding task confirm several meaningful error patterns, while on MuSiQue and LongBench v2 Janus reports no confirmed patterns for the tested properties, contrasting with standard shuffling tests that sometimes confirm patterns.
By Vyzantinos Repantis, Ameya Gawde, Harshvardhan Singh
VeriHarness is a method that enhances verification for large language model agents tackling long‑horizon tasks without needing reference answers at test time. It transforms the base LLM into an agentic verifier by providing a workspace, evidence tools, and reusable verification skills, using disagreement resolution and consensus challenge to evaluate competing claims. Across five benchmarks and two frontier models, VeriHarness outperforms baselines, achieving significant performance gains and demonstrating self‑improvement of verification skills from failure feedback.
By Caiqi Zhang, Rujun Han, Zifeng Wang, Zoey CuiZhu, Nigel Collier, Tomas Pfister, Chen-Yu Lee
The paper introduces SPLIT, a method that optimally allocates test‑time compute among sampling, model selection, and verification for language models and agents. Using an exact probability formula that accounts for task difficulty, shared blind spots, and verifier correlations, SPLIT is fitted via empirical Bayes on a small labeled pilot and then used to plan budgets for unseen tasks. Across seven diverse benchmarks, 26 models, and six verifier types, SPLIT achieves near‑optimal performance, outperforming rule‑based baselines by up to 2.5 percentage points and providing insights into when additional LLM judges reduce precision.
By Srinivasan Iyengar
arXiv:2603. 28590v3 Announce Type: replace Abstract: Large language models (LLMs) can generate chains of thought (CoTs) that are not always causally responsible for their final outputs.
By Han Wang, Yifan Sun, Brian Ko, Mann Talati, Jiawen Gong, Zimeng Li, Naicheng Yu, Xucheng Yu, Wei Shen, Vedant Jolly, Huan Zhang
arXiv:2607. 08065v1 Announce Type: new Abstract: LLM-as-judge (Zheng et al.
By Kaihua Ding
arXiv:2610.17747v1 Announce Type: cross
Abstract: Failure-attribution benchmarks ask an LLM judge which step or agent caused a failure. A high score should mean the judge checked the faulty step agai...
By Tianyang Xu, Jing Gao
Coordinators that act on what they infer about teammates are usually certified by the accuracy of those inferences. We argue that accuracy is the wrong certificate, for two reasons. First, the accurac...