ReCrit frames critic interaction as a correctness-transition problem and uses quadrant-based RL rewards to improve LLM performance on scientific reasoning benchmarks by rewarding corrections and robustness while penalizing sycophancy.
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Llm-based multi-agent reinforcement learning: Current and future directions
12 Pith papers cite this work. Polarity classification is still indexing.
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MACA frames multi-agent coordination as posterior inference, learns a structural prior to guide orchestration, and reports 8.42% higher performance with 43.19% fewer tokens than adaptive baselines on benchmarks.
LLM-generated coordination graph priors improve multi-agent reinforcement learning performance on MPE benchmarks, with models as small as 1.5B parameters proving effective.
CoMAM jointly optimizes agents in multi-agent LLM memory systems via end-to-end RL and adaptive credit assignment to improve collaboration and performance.
AgeMem unifies long-term and short-term memory management in LLM agents by exposing memory operations as learnable tool actions trained via three-stage progressive reinforcement learning, outperforming baselines on long-horizon tasks.
WebSailor trains open-source web agents to match proprietary performance on complex information-seeking tasks by generating high-uncertainty scenarios and using a new RL method called DUPO.
CoEvolve improves LLM agent performance by 15-19% on AppWorld and BFCL benchmarks through mutual evolution of the agent and data distribution using feedback-driven task synthesis.
OATH combines adaptive Halton sampling, obstacle-aware clustering with auctions, and LLM-based instruction interpretation to improve task assignment and planning for heterogeneous robot teams in obstacle-rich environments.
A survey comparing classical multi-agent systems with large foundation model-enabled multi-agent systems, showing how the latter enables semantic-level collaboration and greater adaptability.
The paper delivers the first systematic review of self-evolving agents, structured around what components evolve, when adaptation occurs, and how it is implemented.
The survey organizes LLM-based multi-agent collaboration mechanisms into a framework with dimensions of actors, types, structures, strategies, and coordination protocols, reviews applications across domains, and identifies challenges for future research.
A survey consolidating frameworks, data practices, large action models, benchmarks, applications, and research gaps in LLM-brained GUI agents.
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Large Language Model-Brained GUI Agents: A Survey
A survey consolidating frameworks, data practices, large action models, benchmarks, applications, and research gaps in LLM-brained GUI agents.