DOLORES, an agent using a formal language for meta-reasoning to construct adaptive scaffolds on the fly, outperforms prior scaffolding methods by 24.8% on average across four hard benchmarks and multiple model sizes.
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A unified bandit framework for general open multi-agent systems with global-UCB algorithms and regret bounds linear in entry uncertainty and dependent on system stability and agent patterns.
Meta-Team is a collaborative self-evolution framework that turns multi-agent execution experience into reusable improvements at agent, coordination, and team levels, outperforming baselines on six benchmarks.
citing papers explorer
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Deep Reasoning in General Purpose Agents via Structured Meta-Cognition
DOLORES, an agent using a formal language for meta-reasoning to construct adaptive scaffolds on the fly, outperforms prior scaffolding methods by 24.8% on average across four hard benchmarks and multiple model sizes.
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Bandit Learning in General Open Multi-agent Systems
A unified bandit framework for general open multi-agent systems with global-UCB algorithms and regret bounds linear in entry uncertainty and dependent on system stability and agent patterns.
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Evolve as a Team: Collaborative Self-Evolution for LLM-based Multi-Agent Systems
Meta-Team is a collaborative self-evolution framework that turns multi-agent execution experience into reusable improvements at agent, coordination, and team levels, outperforming baselines on six benchmarks.