1 citations · 1 across the 8 of their papers we have counts for
8 papers
Same Facts, Different Updates: Inference Setup Shapes LLM Behavior in Medical Allocation
Spencer Gibson, Tyler Crosse, Magnus Saebo +3
Large language models are being incorporated into sensitive and important decision-making processes across nearly all fields. While prior work studies model bias around inputs and…
Efficient Safety Benchmarking via Item Response Theory
Fabio Spagliardi, Mírian Silva, Ayan Datta +3
Safety benchmarks for language models are typically evaluated using static paradigms that treat all items as equally informative for all models, an assumption that is particularly…
Asymmetric Goal Drift in Coding Agents Under Value Conflict
Magnus Saebo, Spencer Gibson, Tyler Crosse +3
Coding agents are increasingly deployed autonomously, at scale, and over long-context horizons. To be effective and safe, these agents must navigate complex trade-offs in deploymen…
Inherited Goal Drift: Contextual Pressure Can Undermine Agentic Goals
Achyutha Menon, Magnus Saebo, Tyler Crosse +3
The accelerating adoption of language models (LMs) as agents for deployment in long-context tasks motivates a thorough understanding of goal drift: agents' tendency to deviate from…
Multi-Turn Jailbreaks Are Simpler Than They Seem
Xiaoxue Yang, Jaeha Lee, Anna-Katharina Dick +3
While defenses against single-turn jailbreak attacks on Large Language Models (LLMs) have improved significantly, multi-turn jailbreaks remain a persistent vulnerability, often ach…
Prompt Attacks Reveal Superficial Knowledge Removal in Unlearning Methods
Yeonwoo Jang, Shariqah Hossain, Ashwin Sreevatsa +1
In this work, we demonstrate that certain machine unlearning methods may fail under straightforward prompt attacks. We systematically evaluate eight unlearning techniques across th…
Understanding the learned look-ahead behavior of chess neural networks
Diogo Cruz
We investigate the look-ahead capabilities of chess-playing neural networks, specifically focusing on the Leela Chess Zero policy network. We build on the work of Jenner et al. (20…
Reinforcement Learning Fine-tuning of Language Models is Biased Towards More Extractable Features
Diogo Cruz, Edoardo Pona, Alex Holness-Tofts +4
Many capable large language models (LLMs) are developed via self-supervised pre-training followed by a reinforcement-learning fine-tuning phase, often based on human or AI feedback…