arXiv AI July 1, 2026 By Ankur Samanta, Akshayaa Magesh, Tal Lancewicki, Ayush Jain, Youliang Yu, Paul Sajda, Kaveh Hassani, Aditya Modi, Daniel R. Jiang, Yonathan Efroni

BayesBench: Evaluating LLM Belief Trajectories Under Multi-Turn Evidence Accumulation

Read the original on arXiv AI →

arXiv:2606. 30850v1 Announce Type: new Abstract: Large language models (LLMs) are typically deployed in multi-turn conversations, where each turn provides new evidence that should reduce epistemic uncertainty about their environment.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

llms

arXiv AI

Jul 14

Agentic Forecasting using Sequential Bayesian Updating of Linguistic Beliefs

arXiv:2604. 18576v4 Announce Type: replace Abstract: We present the Bayesian Linguistic Forecaster (BLF), an agentic system for binary forecasting that achieves state-of-the-art performance on the ForecastBench benchmark.

By Kevin Murphy

llms agents benchmarks

Hugging Face Trending Papers

Jul 9

What LLM Forecasters Know but Don't Say: Probing Internal Representations for Calibration and Faithfulness

Large language models fine-tuned for forecasting can be accurate yet poorly calibrated, and their chain-of-thought (CoT) reasoning may not faithfully reflect the evidence behind a forecast. We ask whether internal representations offer a more direct window into both.

llms fine-tuning benchmarks

arXiv AI

Jun 2

Hypothesis Generation and Inductive Inference in Children and Language Models

arXiv:2605. 24528v2 Announce Type: replace Abstract: Real world decision-making requires constructing mental models under uncertainty over evidence, over the underlying causal rules, and over the state of the world itself.

By Jeffrey Qin, Wasu Top Piriyakulkij, Zhuangfei Gao, Mia Radovanovic, Jessica Sommerville, Kevin Ellis, Marta Kryven

llms agents robotics

arXiv AI

Jun 6

A Model of Multi-turn Human Persuadability Using Probabilistic Belief Tracing

arXiv:2606. 05330v1 Announce Type: cross Abstract: Large language models can shift human beliefs across high-stakes domains, but most persuasion studies rely on pre/post belief change.

By Jared Moore, Noah Goodman, Nick Haber, Max Kleiman-Weiner

llms

arXiv AI

Sep 15

Before You Poll with LLMs: A Deliberative Diagnostic Framework

arXiv:2609.15849v1 Announce Type: cross Abstract: Can LLMs reason through new information like humans, or do they merely retrieve cached opinions? This is critical for silicon sampling, where LLM per...

By Ahmed Wali, Hassaan Tayyab

llms benchmarks

arXiv AI

Sep 2

HugAgent: A Human Simulation Benchmark for Individual-Level Reasoning

arXiv:2510.15144v4 Announce Type: replace Abstract: Simulating human reasoning in open-ended tasks has long been a central aspiration in AI and cognitive science. While large language models now appr...

By Chance Jiajie Li, Zhenze Mo, Yuhan Tang, Ao Qu, Jiayi Wu, Kaiya Ivy Zhao, Yulu Gan, Jie Fan, Jiangbo Yu, Hang Jiang, Paul Pu Liang, Jinhua Zhao, Luis Alberto Alonso Pastor, Kent Larson

llms agents benchmarks safety

One email a morning, machine-written

Related stories

Agentic Forecasting using Sequential Bayesian Updating of Linguistic Beliefs

What LLM Forecasters Know but Don't Say: Probing Internal Representations for Calibration and Faithfulness

Hypothesis Generation and Inductive Inference in Children and Language Models

A Model of Multi-turn Human Persuadability Using Probabilistic Belief Tracing

Before You Poll with LLMs: A Deliberative Diagnostic Framework

HugAgent: A Human Simulation Benchmark for Individual-Level Reasoning