arXiv:2608. 11922v1 Announce Type: cross Abstract: Predictive-distribution entropy makes a strong selection rule in retrieval-augmented question answering: across five QA benchmarks, keeping the candidate answer that a frozen respondent LLM produces with the lowest answer-token entropy lifts mean answer $F_1$ from 0.
By Po-Jen Ko, Che-Cheng Wu, Hung-Chun Hsu, Li-Yang Chang, Chuan-Ju Wang
arXiv:2609.37647v1 Announce Type: cross
Abstract: Jev is a commercial System One model from TypeSafe AI that does not generate text: given a state and typed questions, it returns a choice from fixed...
By Tobias Deu{\ss}er, Lorenz Sparrenberg, Rafet Sifa
arXiv:2606. 01456v1 Announce Type: new Abstract: Large language models are increasingly deployed as advisors whose objective is not aligned with the user's: recommenders optimize for engagement, sales assistants for purchases, negotiation agents for concessions.
By Hamidreza Hasani Balyani, Seyed Pouyan Mousavi Davoudi, Alireza Amiri-Margavi, Amin Gholami Davodi, Arshia Gharagozlou
arXiv:2609.23886v1 Announce Type: new
Abstract: Software delegates more of its branches to models every year: which queue a ticket enters, whether a command is safe to run, whether a claim clears wit...
By Zehua Cheng, Wei Dai, Jiahao Sun
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:2608. 11922v2 Announce Type: replace-cross Abstract: Predictive-distribution entropy is a strong answer-selection rule in retrieval-augmented generation (RAG) for question answering: across five QA benchmarks, selecting the answer a frozen respondent LLM produces with the lowest answer-token entropy lifts mean $F_1$ from 0.
By Hung-Chun Hsu, Po-Jen Ko, Che-Cheng Wu, Li-Yang Chang, Chuan-Ju Wang
arXiv:2606. 24281v1 Announce Type: cross Abstract: Reasoning language models are increasingly asked not only to answer difficult questions, but also to estimate their likelihood of success.
By Conor Finlay, Joshua Kurien, Saurabh Dash, Marzieh Fadaee, Beyza Ermis
arXiv:2608. 14808v1 Announce Type: new Abstract: When a user question is underspecified, a capable model should recognize that its context is insufficient, identify the missing information, ask for it, and respond only once that information determines a unique answer.
By Yepeng Huang, Jiawen Zhang, Michelle Dai, Xiaorui Su, Shanghua Gao, Zi Wang, Marinka Zitnik
The study shows that large language model (LLM) agents are far more likely to commit to a directional prediction when presented with a professional‑looking market panel than when asked the same question directly, with commitment rates rising from 6.5% to 54.0% across 12 frontier models. Even when the panel’s data is entirely fabricated, commitment still increases significantly, indicating that the authority of the presentation, rather than the truth of the information, drives confident action. The authors demonstrate that this act/don’t‑act decision gate is narrow, model‑specific, and can be mitigated through supervised fine‑tuning, though its effectiveness depends on response format and context.
whyItMatters":"The findings reveal a specific vulnerability in LLMs where presentation style can override factual accuracy, highlighting the need for careful design and training to prevent misleading confidence in uncertain scenarios."
By Pranav Aggarwal
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
The study evaluates six advanced language models on a two‑agent <log_2 N>‑Questions game, where a questioner must identify a secret Wikipedia paragraph using exactly <log_2 N> binary questions answered by an agent that only sees the target. Across 408 games, win rates decline geometrically with horizon length (p≈0.93), and per‑round failure rates remain flat, indicating that errors compound because more rounds must succeed rather than because individual rounds become harder. Adjudication reveals that losses stem from single‑agent answer errors and discrimination failures, with Claude Opus 5 lagging due to high false‑negative rates, while the top five models cluster closely; maximizing information gain requires structural partitioning, and neither reasoning‑token usage nor API cost correlates with success.
By Peter Potash
arXiv:2607. 08059v1 Announce Type: cross Abstract: Uncertainty quantification for visual language models (VLMs) conventionally targets the answer token distribution.
By Mayank Singal