The work creates NIABench and an LLM-plus-scoring-model framework that enables robots to deliver proactive assistance during human multi-step activities while avoiding interruptions and reducing human effort.
React: Synergizing reasoning and acting in language models
6 Pith papers cite this work. Polarity classification is still indexing.
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Behavior Forest decouples multi-constraint travel planning into parallel behavior trees with LLM nodes and global coordination, yielding 6.67% and 11.82% gains over prior methods on two benchmarks.
Tri-RAG turns external knowledge into Condition-Proof-Conclusion triplets and retrieves via the Condition anchor to improve efficiency and quality in LLM RAG.
OpsAgent presents a training-free multi-agent framework with dual self-evolution for automated incident management in microservices, claiming SOTA results on OPENRCA benchmark and successful production deployment at Lenovo.
A dual-LLM hierarchical framework for robotic task and motion planning, integrating object detection, achieves 86% success across 24 test scenarios ranging from simple spatial commands to infeasible requests.
A survey consolidating benchmarks, agent frameworks, real-world applications, and protocols for LLM-based autonomous agents into a proposed taxonomy with recommendations for future research.
citing papers explorer
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Assistance Without Interruption: A Benchmark and LLM-based Framework for Non-Intrusive Human-Robot Assistance
The work creates NIABench and an LLM-plus-scoring-model framework that enables robots to deliver proactive assistance during human multi-step activities while avoiding interruptions and reducing human effort.
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Decoupled Travel Planning with Behavior Forest
Behavior Forest decouples multi-constraint travel planning into parallel behavior trees with LLM nodes and global coordination, yielding 6.67% and 11.82% gains over prior methods on two benchmarks.
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Transforming External Knowledge into Triplets for Enhanced Retrieval in RAG of LLMs
Tri-RAG turns external knowledge into Condition-Proof-Conclusion triplets and retrieves via the Condition anchor to improve efficiency and quality in LLM RAG.
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OpsAgent: An Evolving Multi-agent System for Incident Management in Microservices
OpsAgent presents a training-free multi-agent framework with dual self-evolution for automated incident management in microservices, claiming SOTA results on OPENRCA benchmark and successful production deployment at Lenovo.
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Hierarchical Prompting with Dual LLM Modules for Robotic Task and Motion Planning
A dual-LLM hierarchical framework for robotic task and motion planning, integrating object detection, achieves 86% success across 24 test scenarios ranging from simple spatial commands to infeasible requests.
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From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review
A survey consolidating benchmarks, agent frameworks, real-world applications, and protocols for LLM-based autonomous agents into a proposed taxonomy with recommendations for future research.