CollabSim is a new CSCW-grounded simulation framework that enables controlled multi-agent experiments to measure collaborative competence in LLM agents.
arXiv preprint arXiv:2502.00640(2025)
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PRIME enables agents to proactively reason in user-centric tasks by iteratively evolving structured memories from interaction trajectories without gradient-based training.
CogWM is an LLM-based user simulator and evaluator that jointly predicts users' BDI/E states and utterances, using the resulting cognitive trajectories to compare conversational agents.
Recon scores reasoning traces via action reconstruction fidelity, achieving 54.7% win rate over post-hoc baselines and up to 70% when used to train synthesis models across four domains.
MLReplicate benchmark evaluates six autonomous systems on 45 manuscripts from ICML 2025 papers, finding that automated reviews accept flawed outputs with fabricated claims while human review exposes methodological failures, and that the cheapest system outperforms the most expensive by a wide margin
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.
Centralized-critic actor-critic training (CoLLM-CC) improves sample efficiency and stability over Monte-Carlo multi-agent RL for training decentralized LLM collaboration.
citing papers explorer
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CollabSim: A CSCW-Grounded Methodology for Investigating Collaborative Competence of LLM Agents through Controlled Multi-Agent Experiments
CollabSim is a new CSCW-grounded simulation framework that enables controlled multi-agent experiments to measure collaborative competence in LLM agents.
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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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Cognitive World Model for Progressive BDI/E Trajectory Evaluation of Conversational Agents
CogWM is an LLM-based user simulator and evaluator that jointly predicts users' BDI/E states and utterances, using the resulting cognitive trajectories to compare conversational agents.
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Recon: Reconstruction-Guided Reasoning Synthesis for User Modeling
Recon scores reasoning traces via action reconstruction fidelity, achieving 54.7% win rate over post-hoc baselines and up to 70% when used to train synthesis models across four domains.
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MLReplicate: Benchmarking Autonomous Research Systems for Machine Learning Reproducibility
MLReplicate benchmark evaluates six autonomous systems on 45 manuscripts from ICML 2025 papers, finding that automated reviews accept flawed outputs with fabricated claims while human review exposes methodological failures, and that the cheapest system outperforms the most expensive by a wide margin
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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.
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Learning Decentralized LLM Collaboration with Multi-Agent Actor Critic
Centralized-critic actor-critic training (CoLLM-CC) improves sample efficiency and stability over Monte-Carlo multi-agent RL for training decentralized LLM collaboration.
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