1 citations · 1 across the 3 of their papers we have counts for
5 papers
CAT-alyzing Honesty: Continuous Adversarial Training for Transferable LLM Honesty
Yuan-Chen Chang, Niklas Herbster, Gauthier Gidel +1
Large language models (LLMs) are becoming prevalent in scenarios where honesty is critical. However, despite alignment training, these models still exhibit dishonest behaviour, suc…
Quantifying Overclaiming Propensity in Frontier LLM Agents
Nolan Smyth, Yorguin-Jose Mantilla-Ramos, Pascal Jr Tikeng Notsawo +6
Frontier coding agents are increasingly trusted to work autonomously for long periods of time, yet what they actually did is often hard to tell from their final response. We quanti…
Position: Collusion Risks Among AI Reasoning Agents Justify Certification Requirements for Making Market Decisions
Matthew Riemer, Tommaso Tosato, Amin Memarian +4
This position paper argues that AI agents with chain-of-thought reasoning capabilities are predisposed to exhibit collusive behavior and should be required to obtain behavioral cer…
Activation Steering for Aligned Open-ended Generation without Sacrificing Coherence
Niklas Herbster, Martin Zborowski, Alberto Tosato +2
Alignment in LLMs is more brittle than commonly assumed: misalignment can be induced by adversarial prompts, benign fine-tuning, emergent misalignment, and goal misgeneralization.…
Persistent Instability in LLM's Personality Measurements: Effects of Scale, Reasoning, and Conversation History
Tommaso Tosato, Saskia Helbling, Yorguin-Jose Mantilla-Ramos +5
Large language models require consistent behavioral patterns for safe deployment, yet there are indications of large variability that may lead to an instable expression of personal…