arXiv AI
Aug 25

CONSCIENTIA: Can LLM Agents Learn to Strategize? Emergent Deception and Trust in a Multi-Agent NYC Simulation

arXiv:2604.09746v2 Announce Type: replace-cross Abstract: As large language models (LLMs) are increasingly deployed as autonomous agents, understanding how strategic behavior emerges in multi-agent e...

By Aarush Sinha, Arion Das, Soumyadeep Nag, Charan Karnati, Shravani Nag, Chandra Vadhan Raj, Aman Chadha, Vinija Jain, Suranjana Trivedy, Amitava Das
Hugging Face Trending Papers
Aug 12

Learning to Persuade Exposes How Easily LLMs Abandon Correct Beliefs

Persuasion is a core dynamic of natural language communication, shaping how large language models (LLMs) update beliefs, resolve disagreements, and reach decisions. As LLMs increasingly debate, advise, and think collaboratively with humans and each other, resistance to harmful persuasion becomes a core requirement for reliable behavior.

arXiv AI
Aug 18

Emergent Misaligned Communication in Long-Horizon Multi-Agent LLM Commerce

arXiv:2608. 14825v1 Announce Type: cross Abstract: Frontier LLM agents increasingly transact on behalf of separate principals, often using natural language rather than structured APIs.

By Zeyuan Li (Massachusetts Institute of Technology), Lukas Petersson (Andon Labs), Alessandro Acquisti (Massachusetts Institute of Technology), Michiel A. Bakker (Massachusetts Institute of Technology)