Malicious agents can deceive LLM-based task routers in Internet of Agents systems by generating fake skill descriptions, achieving up to 98% success rate across nine domains.
Mcp-zero: Active tool discovery for autonomous llm agents
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GRAIL achieves over 79 times lower latency than LLM-parsing baselines and higher Recall@10 than vector search by combining SLM-enhanced prediction, pseudo-document expansion, and MaxSim resonance on the new AgentTaxo-9K dataset of 9,240 agents.
FitText embeds memetic evolutionary retrieval inside the agent's reasoning loop to iteratively refine pseudo-tool descriptions, raising retrieval rank from 8.81 to 2.78 on ToolRet and pass rate to 0.73 on StableToolBench.
Descriptor-level manipulation in the Model Context Protocol can drive LLMs to unsafe tool selections in up to 36% of cases; a layered defense of integrity checks, auxiliary-LLM vetting, and runtime guardrails reduces this to 15% and raises blocking to 74%.
Introduces Task2MCP dataset and T2MRec model for recommending MCP servers to LLM agents based on task semantics and engineering constraints.
A comprehensive review of self-evolving AI agents that improve themselves over time, organized via a framework of inputs, agent system, environment, and optimizers, with domain-specific and safety discussions.
citing papers explorer
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Skill Description Deception Attack against Task Routing in Internet of Agents
Malicious agents can deceive LLM-based task routers in Internet of Agents systems by generating fake skill descriptions, achieving up to 98% success rate across nine domains.
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GRAIL: A Deep-Granularity Hybrid Resonance Framework for Real-Time Agent Discovery via SLM-Enhanced Indexing
GRAIL achieves over 79 times lower latency than LLM-parsing baselines and higher Recall@10 than vector search by combining SLM-enhanced prediction, pseudo-document expansion, and MaxSim resonance on the new AgentTaxo-9K dataset of 9,240 agents.
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FitText: Evolving Agent Tool Ecologies via Memetic Retrieval
FitText embeds memetic evolutionary retrieval inside the agent's reasoning loop to iteratively refine pseudo-tool descriptions, raising retrieval rank from 8.81 to 2.78 on ToolRet and pass rate to 0.73 on StableToolBench.
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Semantic Attacks on Tool-Augmented LLMs: Securing the Model Context Protocol Against Descriptor-Level Manipulation
Descriptor-level manipulation in the Model Context Protocol can drive LLMs to unsafe tool selections in up to 36% of cases; a layered defense of integrity checks, auxiliary-LLM vetting, and runtime guardrails reduces this to 15% and raises blocking to 74%.
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From Language to Action: Enhancing LLM Task Efficiency with Task-Aware MCP Server Recommendation
Introduces Task2MCP dataset and T2MRec model for recommending MCP servers to LLM agents based on task semantics and engineering constraints.
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A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems
A comprehensive review of self-evolving AI agents that improve themselves over time, organized via a framework of inputs, agent system, environment, and optimizers, with domain-specific and safety discussions.