Topic: interacting agents

Dimitris Papailiopoulos of Microsoft Research and the University of Wisconsin discusses how coding agents change scientific research. The conversation covers small arithmetic transformers, symbolic mathematical reasoning, interacting agents, and the role of human judgment and verification. It also examines diversity in machine-generated ideas, benchmark and harness overfitting, persistent agent errors, continual learning, world models, and where information theory helps explain AI. Hosted by Ravid Shwartz Ziv and Allen Roush; the full conversation is available on the publisher's YouTube channe

Social world models extend prediction beyond physical dynamics to the intentions, beliefs and strategies of interacting agents. This talk examines how such representations can be learned and used in socially capable AI. Examples include multimodal models of behaviour, benchmarks for social reasoning and agents that adapt over extended interactions.