PRIME enables agents to proactively reason in user-centric tasks by iteratively evolving structured memories from interaction trajectories without gradient-based training.
Collabllm: From passive responders to active collaborators
4 Pith papers cite this work. Polarity classification is still indexing.
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AI alignment must move beyond assuming users have fully formed goals and instead provide active cognitive support to help form and refine intent over time.
CoFi-PGMA derives a unified counterfactual policy gradient objective based on marginal contribution to correct filtered feedback for both routing and collaborative multi-agent LLM training.
Fine-tuned simulators grounded in real human data produce LLM assistants that win more often against real users than those trained against role-playing simulators.
citing papers explorer
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PRIME: Training Free Proactive Reasoning via Iterative Memory Evolution for User-Centric Agent
PRIME enables agents to proactively reason in user-centric tasks by iteratively evolving structured memories from interaction trajectories without gradient-based training.
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Alignment has a Fantasia Problem
AI alignment must move beyond assuming users have fully formed goals and instead provide active cognitive support to help form and refine intent over time.
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CoFi-PGMA: Counterfactual Policy Gradients under Filtered Feedback for Multi-Agent LLMs
CoFi-PGMA derives a unified counterfactual policy gradient objective based on marginal contribution to correct filtered feedback for both routing and collaborative multi-agent LLM training.
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Quantifying the Utility of User Simulators for Building Collaborative LLM Assistants
Fine-tuned simulators grounded in real human data produce LLM assistants that win more often against real users than those trained against role-playing simulators.