SPIRE is a multi-agent framework drawing on scholarly primitives to perform evidence-grounded humanities scholarship, outperforming Naive LLM, Text RAG, and GraphRAG on a benchmark of classical Chinese and Greco-Roman Latin papers.
Internet of agents: Weaving a web of heterogeneous agents for collaborative intelligence
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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 wireless agent network framework achieves superior energy efficiency via hierarchical optimization combining semantic compression, resource allocation, and potential field-based topology evolution for agentic AI collaboration.
A survey consolidating frameworks, data practices, large action models, benchmarks, applications, and research gaps in LLM-brained GUI agents.
A survey synthesizing challenges, system architectures, model optimizations, deployment methods, and resource management techniques for large language model inference at the network edge.
A survey that organizes LLMs-as-judges research into functionality, methodology, applications, meta-evaluation, and limitations.
citing papers explorer
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Extending AI for Research to the Humanities: A Multi-Agent Framework for Evidence-Grounded Scholarship
SPIRE is a multi-agent framework drawing on scholarly primitives to perform evidence-grounded humanities scholarship, outperforming Naive LLM, Text RAG, and GraphRAG on a benchmark of classical Chinese and Greco-Roman Latin papers.
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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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Agentic AI-Empowered Wireless Agent Networks With Semantic-Aware Collaboration via ILAC
A wireless agent network framework achieves superior energy efficiency via hierarchical optimization combining semantic compression, resource allocation, and potential field-based topology evolution for agentic AI collaboration.
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Large Language Model-Brained GUI Agents: A Survey
A survey consolidating frameworks, data practices, large action models, benchmarks, applications, and research gaps in LLM-brained GUI agents.
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Network Edge Inference for Large Language Models: Principles, Techniques, and Opportunities
A survey synthesizing challenges, system architectures, model optimizations, deployment methods, and resource management techniques for large language model inference at the network edge.
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LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods
A survey that organizes LLMs-as-judges research into functionality, methodology, applications, meta-evaluation, and limitations.