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.
Gorilla: Large language model connected with massive apis
6 Pith papers cite this work. Polarity classification is still indexing.
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2026 6representative citing papers
A bidirectional semantic complementary tool retrieval method using planning-based query enhancement and dynamic tool dependency graphs with neighborhood aggregation improves retrieval accuracy on remote sensing and general tool tasks.
EVOREC integrates locate-then-edit model editing with FA-constrained decoding to improve LLM-based service recommendation under evolution, reporting 25.9% average relative gain in Recall@5 over baselines and 22.3% over fine-tuning in dynamic scenarios.
AgentGate decomposes routing into action decision and structural grounding stages, allowing small 3B-7B models to dispatch queries competitively on a curated benchmark after targeted fine-tuning.
A QoS-aware constrained packer restores 100% skill deliverability under token/risk/tool budgets at ~1.14 hit-rate points cost over unconstrained Top-5 on 35k skills.
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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Bidirectional Semantic Complementary Tool Retrieval for Remote Sensing Agents
A bidirectional semantic complementary tool retrieval method using planning-based query enhancement and dynamic tool dependency graphs with neighborhood aggregation improves retrieval accuracy on remote sensing and general tool tasks.
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When Model Editing Meets Service Evolution: A Knowledge-Update Perspective for Service Recommendation
EVOREC integrates locate-then-edit model editing with FA-constrained decoding to improve LLM-based service recommendation under evolution, reporting 25.9% average relative gain in Recall@5 over baselines and 22.3% over fine-tuning in dynamic scenarios.
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AgentGate: A Lightweight Structured Routing Engine for the Internet of Agents
AgentGate decomposes routing into action decision and structural grounding stages, allowing small 3B-7B models to dispatch queries competitively on a curated benchmark after targeted fine-tuning.
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SkillSelect-Serve: QoS-Aware Budgeted Skill Service Recommendation for LLM Agents
A QoS-aware constrained packer restores 100% skill deliverability under token/risk/tool budgets at ~1.14 hit-rate points cost over unconstrained Top-5 on 35k skills.
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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.