7 papers
Towards Understanding Specification Gaming in Reasoning Models
Kei Nishimura-Gasparian, Robert McCarthy, David Lindner
Specification gaming is a critical failure mode of LLM agents. Despite this, there has been little systematic research into when it arises and what drives it. To address this, we b…
Reasoning Models Struggle to Control their Chains of Thought
Chen Yueh-Han, Robert McCarthy, Bruce W. Lee +5
Chain-of-thought (CoT) monitoring is a promising tool for detecting misbehaviors and understanding the motivations of modern reasoning models. However, if models can control what t…
Split Personality Training: Revealing Latent Knowledge Through Alternate Personalities
Florian Dietz, William Wale, Oscar Gilg +5
Detecting misalignment in large language models is challenging because models may learn to conceal misbehavior during training. Standard auditing techniques fall short: black-box m…
Large language models can learn and generalize steganographic chain-of-thought under process supervision
Joey Skaf, Luis Ibanez-Lissen, Robert McCarthy +8
Chain-of-thought (CoT) reasoning not only enhances large language model performance but also provides critical insights into decision-making processes, marking it as a useful tool…
Hidden in Plain Text: Emergence & Mitigation of Steganographic Collusion in LLMs
Yohan Mathew, Ollie Matthews, Robert McCarthy +4
The rapid proliferation of frontier model agents promises significant societal advances but also raises concerns about systemic risks arising from unsafe interactions. Collusion to…
Early Signs of Steganographic Capabilities in Frontier LLMs
Artur Zolkowski, Kei Nishimura-Gasparian, Robert McCarthy +2
Monitoring Large Language Model (LLM) outputs is crucial for mitigating risks from misuse and misalignment. However, LLMs could evade monitoring through steganography: Encoding hid…