collaborators

5 papers

cs.CL2026

Verbalizable Representations Form a Global Workspace in Language Models

Wes Gurnee, Nicholas Sofroniew, Adam Pearce +13

Out of everything the human brain processes, only a small fraction is consciously accessible, in the sense of being available for verbal report, deliberate control, and flexible re…

cs.AI2026

Introspection Adapters: Training LLMs to Report Their Learned Behaviors

Keshav Shenoy, Li Yang, Abhay Sheshadri +4

When model developers or users fine-tune an LLM, this can induce behaviors that are unexpected, deliberately harmful, or hard to detect. It would be far easier to audit LLMs if the…

cs.CL2026

AuditBench: Evaluating Alignment Auditing Techniques on Models with Hidden Behaviors

Abhay Sheshadri, Aidan Ewart, Kai Fronsdal +5

We introduce AuditBench, an alignment auditing benchmark. AuditBench consists of 56 language models with implanted hidden behaviors. Each model has one of 14 concerning behaviors--…

cs.LG2025

Eliciting Secret Knowledge from Language Models

Bartosz Cywiński, Emil Ryd, Rowan Wang +4

We study secret elicitation: discovering knowledge that an AI possesses but does not explicitly verbalize. As a testbed, we train three families of large language models (LLMs) to…

cs.CL2025

Believe It or Not: How Deeply do LLMs Believe Implanted Facts?

Stewart Slocum, Julian Minder, Clément Dumas +4

Knowledge editing techniques promise to implant new factual knowledge into large language models (LLMs). But do LLMs really believe these facts? We develop a framework to measure b…