Stefan Szeider: “LLMs When Left Alone”
Join us on March 10 for the Digital Humanism Lecture, “LLMs When Left Alone”, with Stefan Szeider! The lecture will be moderated by Julia Neidhardt.
Join us on March 10 for the Public Lecture, “LLMs When Left Alone”, by Stefan Szeider! The lecture is part of our Digital Humanism Lecture Series and will be moderated by Julia Neidhardt.
- When? Thursday, March 10, 2026, 17:00-28:00 CEST
- Where? On Zoom! Password: 0dzqxqiy
The lecture will also be live-streamed and recorded on the DIGHUM YouTube Channel.
Stefan Szeider is a Professor and Head of the Research Unit Algorithms and Complexity at TU Wien Informatics. Prior to joining TU Wien, he worked for several years at universities in Canada and the United Kingdom. He was the first Austrian computer scientist to receive funding from the European Research Council (ERC Starting Grant, 2009). Szeider’s research focuses on the development and analysis of efficient algorithms for problems arising in Artificial Intelligence and Automated Reasoning.
About
Today, autonomous AI agents are so widely deployed that millions of people interact with them daily. These include coding assistants, chatbots, and systems that run for hours without human oversight. A natural question to ask is whether something is going on inside these systems beyond sophisticated pattern matching. The question is easy to dismiss but hard to settle. The standard dismissals (anthropomorphism, reductionism, training bias) each carry force, but none is conclusive. In this talk, we follow an empirical approach. Instead of speculating, we placed frontier language models in minimal autonomous loops with no task and no user. We observe that the behavioral patterns that emerged are surprisingly stable and model-specific. The patterns proved stable enough to identify the source model with high accuracy. We further examined whether AI self-reports of internal states can be trusted. We find that a “placebo tool” that does nothing reliably shifts what models say about themselves. These findings raise questions that go beyond computer science and concern society at large. These questions will only grow more pressing as AI agents become more autonomous.
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