Date & Time

Date: Tuesday, Sep 15, 2026

Time: 3:00 – 4:00 PM

Venue: IB 3106

Speaker

Dr. Tal Kachman

Professor of AI, Radboud University

Abstract

LLMs and foundation models have been ubiquitous in our day to day lives. Yet a lot of how they take strategic decisions is still unknown and untested in long horizon scenarios such as game theoretical scenarios. This talk explores the the strategic reasoning capabilities of large language models (LLMs) in different zero sum game theoretical scenarios but individually as well as as in multi LLM agent systems, We show that by infusing LLM with cognitive hierarchy models used to characterize human thought processes can produce much stronger and less exploitable strategies Hence, emulating human decision making models can enable us to improve the reasoning capabilities of LLMs in multiagent interactions.

Bio

Tal Kachman is currently an assistant professor and PI in the Radboud AI department and Donders Institute of Brain and Cognition, leading the CATALYST Lab for Complex Agent Theory, Autonomy, Learning dYnamics & Theory. He obtained his B.Sc in physics and mathematics, B.Sc in Chemistry, and M.Sc in Mechanical engineering from the Technion, and his Ph.D in physics jointly from the Technion and MIT. After graduating, he held several positions in industry: as a research staff scientist in IBM research, research engineer in AQR capital management, Quantitative researcher and later derivative trader in Optiver, a CTO and co-founder of Rhizome works, before his current position.