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cs.AI2025
Terrarium: Revisiting the Blackboard for Multi-Agent Safety, Privacy, and Security Studies
Mason Nakamura, Abhinav Kumar, Saaduddin Mahmud +3
A multi-agent system (MAS) powered by large language models (LLMs) can automate tedious user tasks such as meeting scheduling that requires inter-agent collaboration. LLMs enable n…
cs.LG2025
Aligning LLMs on a Budget: Inference-Time Alignment with Heuristic Reward Models
Mason Nakamura, Saaduddin Mahmud, Kyle H. Wray +2
Aligning LLMs with user preferences is crucial for real-world use but often requires costly fine-tuning or expensive inference, forcing trade-offs between alignment quality and com…
cs.CL2025
Inference-Aware Prompt Optimization for Aligning Black-Box Large Language Models
Saaduddin Mahmud, Mason Nakamura, Kyle Hollins Wray +1
Prompt optimization methods have demonstrated significant effectiveness in aligning black-box large language models (LLMs). In parallel, inference scaling strategies such as Best-o…