The study examines how 105 language models choose a single word from a large set of valid options, finding that 46% of the time they all pick the same word, "serendipity." Using 96 prompts that ask for one-word answers across various categories, the researchers measure convergence and each model’s share of the consensus. They observe that post‑training and persona‑tuned models diverge more, while heavily post‑trained assistants are more conformist, and that the field’s answer choices are more concentrated than human norms in most categories.
By Tapan Parikh
Prism-1 introduces Position- and Rival-Independent Scoring for System One models, ensuring that option ordering and set changes do not affect decision probabilities. It achieves this by reading each option in its own block at shared positions, preserving softmax independence across options. Experiments on Qwen3-8B and Gemma 4 12B show Prism-1 maintains baseline accuracy while eliminating sensitivity to shuffling and irrelevant options.
By Yao Long Teng, Jianguo Huang, Yanchen Deng, Jiayi Cai, Bo An
arXiv:2608. 15428v1 Announce Type: cross Abstract: Multiple-choice benchmarks are graded on whether a model picks the right option, not on whether it needed the question.
By Volodymyr Ovcharov
Given a state and a set of options, such as intents, tools or interface elements, System One decision models return a distribution over the options in one forward pass, without generated reasoning. We...
The paper investigates the reliability of LLM judges in the Arena-Hard benchmark, showing that aggregate scores can mask significant variability in individual verdicts. By running GPT‑4.1 across 500 prompts under eight different judge configurations and repeating each setup, the authors find that small changes—whether in temperature, criteria, or instruction framing—can shift win/tie/loss outcomes on up to 28% of prompts and alter aggregate scores by several points. The study concludes that stable aggregate scores do not guarantee repeatable individual judgments, and recommends that evaluations report both aggregate results and prompt‑level agreement.
By Louise Yorke
arXiv:2607. 10202v1 Announce Type: new Abstract: Cross-model comparisons read divergence in value dispositions as evidence that language models hold individuated values.
By Hong-In Won, Jinseok Jang, Hyoseop Kim
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
Large Language Model judges are commonly used to rank texts via pairwise comparison, with reliability traditionally measured by position bias, transitivity, and pairwise agreement. This paper argues that these proxies are misleading because they are dominated by close‑rank‑gap pairs, which contribute little to the overall ranking, while far‑gap pairs carry the true ranking signal. Experiments on simulations and human‑rated corpora show weak correlation between the proxies and actual ranking accuracy, suggesting judges should be evaluated using rank‑gap‑conditional metrics against human rankings.
By Bruno Brocai, Maria Becker
The study evaluates six frontier language models on a two‑agent <log(N)>‑Questions game using Wikipedia lead paragraphs. In each game a questioner must identify a target paragraph with exactly <log2 N> yes/no questions, while an answerer only sees the target and the question and replies with a single word. Across 408 games, the models perform similarly, with Claude Opus 5 winning 28 of 68 games and the top five models showing only marginal differences; win rates decline sharply with larger document sets, following a reliability parameter of 0.928 per question.
"whyItMatters":"The results reveal how well language models can communicate under information asymmetry, highlighting that even top models struggle to extract a full bit per question and that reasoning token usage does not strongly predict success."
By Peter Potash
arXiv:2609.08475v1 Announce Type: cross
Abstract: Large language models have collapsed the cost of producing lexically elaborate prose, and whether peer reviewers still reward it is a question about...
By Jiabin Zheng (School of Computer Science, Peking University)
arXiv:2607. 12796v1 Announce Type: cross Abstract: When a language model must pick one answer from a large space of equally valid options, which does it pick -- and how often is it the same answer every other model picks?
By Tapan Parikh
arXiv:2608.21382v1 Announce Type: new
Abstract: Multiple-choice benchmarks fix the questions and the correct answers, but not the harness: the order of the options, the wording of the prompt, and whe...
By V. S. Raghu Parupudi