arXiv AI July 14, 2026 By Xuankun Rong, Wenke Huang, Bo Du, Dacheng Tao, Mang Ye

Behavioural Signatures of Risk-Sensitive Decision-Making in Large Language Models

arXiv:2607. 10251v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly used in decision support, it is important to understand whether their choices under uncertainty exhibit stable and interpretable behavioural regularities.

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.

arXiv AI

Jul 21

Some Large Language Models Exhibit Consistent Risk Attitudes

arXiv:2607. 16197v1 Announce Type: new Abstract: As artificial intelligence systems are deployed in open-ended, high-stakes settings, a critical dimension remains unmeasured: how perceived risk is translated into action.

By Bowen Sun, Rui Min, Yuxi Wang, Brian Odegaard, Qi Wang, Jing Du

llms agents safety

arXiv Machine Learning

Sep 10

Risk-Conditioned Fine-Tuning of Large Language Models

arXiv:2609.08064v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed in settings where rare but severe harmful generations can have significant consequences. Existin...

By Zixuan Liu, Fangzheng Wu, Brian Summa, Zizhan zheng

llms reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

Aug 4

Humans Are More Diverse: Frontier LLMs Show Extreme Policies in Idealised AI Development Races

arXiv:2608. 01193v1 Announce Type: cross Abstract: An AI development race creates a multi-agent safety dilemma.

By Phu Hoa Pham, Duy Minh Dao Sy, Trung Kiet Huynh, Phu Quy Nguyen Lam, Chi Nguyen Tran, Minh Trung Le, Phong Hao Le, Dinh Nam Nguyen, Thien Ky Nguyen Dong, Elias Fernandez Domingos, Le Hong Trang, The Anh Han

llms agents benchmarks safety

arXiv AI

Jul 7

Out-of-Distribution Generalization of Risk Aversion in Language Models

arXiv:2607. 02755v1 Announce Type: cross Abstract: Training AIs to be risk-averse in resources could offer a failsafe in the event that AIs turn out misaligned.

By Kristina Zhang, Junior Chinomso Okoroafor, Benjamin Maltbie, Andrew Lin, Abhitej Bokka, Elliott Thornley

llms reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 10

Superficial Beliefs in LLM Decision-Making

arXiv:2606. 11016v1 Announce Type: new Abstract: We ask whether large language models (LLMs) merely imitate rationales when choosing between two options, or whether their choices reflect a systematic underlying decision structure.

By Gabriel Freedman, Francesca Toni

llms

arXiv AI

Sep 25

Evaluation of Multi-Turn Consistency in LLM Agents: Survival Analysis and Failure-Rationale Taxonomy

The study evaluates how large language model agents maintain consistency over extended interactions by simulating a 20‑step delayed‑gratification task. Researchers ran 84,540 trajectories across eight model families, using survival analysis to track when agents first claim a reward and discrete‑time hazard regression to assess how factors like social visibility, persona stressors, and deliberation policy affect failure risk. They also developed a seven‑category taxonomy from 13,780 deliberation traces, revealing that early failures are impulse‑driven, later ones are fatigue‑ or cost‑benefit‑framed, and public settings elicit norm‑oriented justifications; longer deliberation correlates with higher intra‑rationale contradictions, challenging assumptions about reasoning depth and consistency.

By Igor Bogdanov, Olga Manakina, Chung-Horng Lung

llms agents robotics

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Related stories

Some Large Language Models Exhibit Consistent Risk Attitudes

Risk-Conditioned Fine-Tuning of Large Language Models

Humans Are More Diverse: Frontier LLMs Show Extreme Policies in Idealised AI Development Races

Out-of-Distribution Generalization of Risk Aversion in Language Models

Superficial Beliefs in LLM Decision-Making

Evaluation of Multi-Turn Consistency in LLM Agents: Survival Analysis and Failure-Rationale Taxonomy