6 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…
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…
Duel-Evolve: Reward-Free Test-Time Scaling via LLM Self-Preferences
Sweta Karlekar, Carolina Zheng, Magnus Saebo +5
Many applications seek to optimize LLM outputs at test time by iteratively proposing, scoring, and refining candidates over a discrete output space. Existing methods use a calibrat…
SWE-Spot: Building Small Repo-Experts with Repository-Centric Learning
Jinjun Peng, Magnus Saebo, Tianjun Zhong +5
The deployment of coding agents in privacy-sensitive and resource-constrained environments drives the demand for capable open-weight Small Language Models (SLMs). However, they suf…
BlindFL: Segmented Federated Learning with Fully Homomorphic Encryption
Evan Gronberg, Liv d'Aliberti, Magnus Saebo +1
Federated learning (FL) is a popular privacy-preserving edge-to-cloud technique used for training and deploying artificial intelligence (AI) models on edge devices. FL aims to secu…