The paper introduces a method to mitigate reviewer lottery error in scientific peer review by generating checklists of criticisms from historical review data. These checklists are organized into a tree of coarsenings, and the optimal coarsening is chosen to maximize correlated agreement among reviewers. Experiments on NeurIPS 2021 and ICLR 2022 data show that the learned checklists significantly reduce mean squared error in simulated and human review settings.
By Xuanyou Liu, Ziyang Guo, Jason Hartline, Jessica Hullman
arXiv:2606. 15887v1 Announce Type: cross Abstract: Large language model (LLM) systems are increasingly proposed to assist peer review, yet most evaluations judge the prose of machine-generated review text, not the validity of the numeric score a system assigns.
By Costa Georgantas
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
The study analyzes 373,019 judgments from LLM‑scored benchmarks, decomposing variance into system, item, judge, and interaction components via generalizability theory. It finds that with a single judge, generalizability converges to a ceiling determined by the system‑by‑judge variance, which is substantially lower in pairwise preference settings, allowing one judge to suffice. The research also reveals significant biases in presentation order and highlights that many published win‑rate claims fall below the measured floor of the benchmarks.
By Atul Anand
arXiv:2606. 05104v1 Announce Type: new Abstract: Knowledge benchmarks for LLMs face three issues: scaling-driven designs that do not operationalize disciplinary representativeness; flat-payment annotation that permits lazy consensus; and unaudited ranking instability under bounded test budgets.
By Sheng Jin, Minghao Liu, Yunze Xiao, Zeqi Zhou, Heli Qi, Yifan Yao, Meishu Song, Kaijing Ma, Xuan Zhang, Sicong Jiang, Yizhe Li, Ningshan Ma, Jie Wei, Ziniu Li, Minglai Yang, Bangya Liu, Yiming Liang, Xiao Fang, Qingcheng Zeng, Jiarui Liu, Rui Yang, Shen Yan, Wenhao Huang, Jiaheng Liu, Zihan Wang, Weihao Xuan, Ge Zhang
arXiv:2608. 15046v1 Announce Type: new Abstract: A fraction of a point of benchmark accuracy is the usual evidence that a compressed model is equivalent to its original.
By Amogh Singh
The paper investigates the reliability of ranking tables produced by small-sample evaluations of large language models (LLMs). Using LLM‑inferred prompt structure across eight model variants, the authors find that prompt‑structure recovery is highly unstable, with only the bottom of the ranking consistently reproducible. They demonstrate that standard evaluation practices can misrepresent model performance and propose reporting practices to improve transparency.
By Dipankar Sarkar
arXiv:2606. 23767v1 Announce Type: new Abstract: Headline accuracies on the Tuebingen cause-effect pairs are routinely compared across papers even though each is measured under its authors' own protocol -- different pair subsets, weightings, model-selection, and decision rates.
By Wietse Stienstra
The paper presents a calibrated evidence inference model fine‑tuned from Qwen3‑4B‑Base using low‑rank adapters and a classification head to classify trial outcomes as significant increase, significant decrease, or no significant difference. Trained on 1,431 open‑access PubMed Central articles, the released model (Apache 2.0) achieves an expected calibration error of 0.017, a log loss lower than Gemma 4 31B‑it in zero‑shot settings, and a macro‑F1 of 0.925 on the test split. Accepting its top‑80 % most confident predictions yields only a 2.6 % error rate, and ablation studies show that article context influences answers while the title alone still provides useful signal.
By Johann Emmanuel Li
arXiv:2607. 27209v1 Announce Type: cross Abstract: Peer review at ML conferences increasingly relies on reviewer scores as the primary decision instrument.
By Binyan Xu, Fan Yang, Xilin Dai, Kehuan Zhang
arXiv:2610.07177v1 Announce Type: cross
Abstract: An open contrastive decision model is near chance as a judge on the hard public benchmarks: Contrastive-LM/CLM-v0.1-8B scores between 0.351 (best- of...
By Gowthamkumar Nandakishore
arXiv:2606. 13221v2 Announce Type: replace Abstract: Evaluating new large language models typically requires costly human annotation campaigns at scale.
By Bora Kargi, David Salinas