A new arXiv paper proposes CREW, a collaborative multi-agent reinforcement learning framework for generating the related work section of research papers. The authors aim to reduce the time and effort researchers spend writing these sections, addressing limitations they identify in earlier multi-agent LLM approaches. The work appears in the cs.LG category on arXiv.
A new arXiv paper examines multi-agent LLM pipelines by treating two design choices as independent variables: how agent calls are wired together and where the differences between agents come from. The authors evaluate this on multilingual, low-resource emotion detection, where labeled data is scarce. The goal is to clarify which gains come from the structure of the agent network versus from the diversity introduced between agents.
A new arXiv paper introduces HypoEvolve, a system that combines multi-agent large language models with evolutionary search to generate scientific hypotheses. The approach uses critique, comparison and revision cycles to refine candidate explanations, though the abstract notes limitations in current agent-based discovery systems. It sits within a broader trend of pairing LLM agents with evolutionary optimization for research tasks.
Researchers present a multi-agent large language model system designed to support clinical interview training, aiming to ease the resource burden of standardized patient sessions. The scaffolding-based approach is meant to help medical students practice safe and coherent patient interviews amid uncertainty. It is described as an evaluation of that system rather than a deployed product.