AgentTether repairs 69% of initially failed LLM agent tasks on τ-bench by combining graph-guided root-cause diagnosis, cross-iteration repair memory, and guarded runtime intervention, improving over blind retry by 26 percentage points.
The rise and potential of large language model based agents: A survey
4 Pith papers cite this work. Polarity classification is still indexing.
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AgentDID is a W3C-compliant decentralized identity system for AI agents enabling self-managed authentication and state verification via challenge-response.
CyberOps-Bots is a hierarchical LLM-empowered multi-agent RL framework that reports 68.5% higher network availability and 34.7% better jumpstart performance in new scenarios without retraining on real cloud datasets.
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.
citing papers explorer
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AgentTether: Graph-Guided Diagnosis and Runtime Intervention for Reliable LLM Agent Operation
AgentTether repairs 69% of initially failed LLM agent tasks on τ-bench by combining graph-guided root-cause diagnosis, cross-iteration repair memory, and guarded runtime intervention, improving over blind retry by 26 percentage points.
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AgentDID: Trustless Identity Authentication for AI Agents
AgentDID is a W3C-compliant decentralized identity system for AI agents enabling self-managed authentication and state verification via challenge-response.
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Enhancing Cloud Network Resilience via a Robust LLM-Empowered Multi-Agent Reinforcement Learning Framework
CyberOps-Bots is a hierarchical LLM-empowered multi-agent RL framework that reports 68.5% higher network availability and 34.7% better jumpstart performance in new scenarios without retraining on real cloud datasets.
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Multi-Agent Systems: From Classical Paradigms to Large Foundation Model-Enabled Futures
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.