4 citations · 10 across the 12 of their papers we have counts for
6 papers · 1 filter
Auditing Games for Sandbagging
Jordan Taylor, Sid Black, Dillon Bowen +10
Future AI systems could conceal their capabilities ('sandbagging') during evaluations, potentially misleading developers and auditors. We stress-tested sandbagging detection techni…
ABBEL: Learning Natural-Language Belief States for Memory-Efficient Interaction
Aly Lidayan, Jakob Bjorner, Satvik Golechha +2
As the time horizons of sequential decision-making tasks grow, keeping full interaction histories in model context becomes increasingly costly. Recent work reduces context lengths…
Who's the Evil Twin? Differential Auditing for Undesired Behavior
Ishwar Balappanawar, Venkata Hasith Vattikuti, Greta Kintzley +2
Detecting hidden behaviors in neural networks poses a significant challenge due to minimal prior knowledge and potential adversarial obfuscation. We explore this problem by framing…
Among Us: A Sandbox for Measuring and Detecting Agentic Deception
Satvik Golechha, Adrià Garriga-Alonso
Prior studies on deception in language-based AI agents typically assess whether the agent produces a false statement about a topic, or makes a binary choice prompted by a goal, rat…
Auditing language models for hidden objectives
Samuel Marks, Johannes Treutlein, Trenton Bricken +32
We study the feasibility of conducting alignment audits: investigations into whether models have undesired objectives. As a testbed, we train a language model with a hidden objecti…
Studying Cross-cluster Modularity in Neural Networks
Satvik Golechha, Maheep Chaudhary, Joan Velja +2
An approach to improve neural network interpretability is via clusterability, i.e., splitting a model into disjoint clusters that can be studied independently. We define a measure…