MAP-Law dynamically controls retrieval depth in legal AI by computing element coverage, evidence coverage, and marginal gain on a joint node graph, reaching 0.86 element coverage with 58% fewer rounds than fixed baselines on 50 labor-law cases.
Lawluo: A chinese law firm co-run by llm agents
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LLM Orchestration integrates modality experts via an LLM controller, cross-modal memory, and interaction layer to enable multimodal input-output without gradient-based training.
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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MAP-Law: Coverage-Driven Retrieval Control for Multi-Turn Legal Consultation
MAP-Law dynamically controls retrieval depth in legal AI by computing element coverage, evidence coverage, and marginal gain on a joint node graph, reaching 0.86 element coverage with 58% fewer rounds than fixed baselines on 50 labor-law cases.
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Training-Free Multimodal Large Language Model Orchestration
LLM Orchestration integrates modality experts via an LLM controller, cross-modal memory, and interaction layer to enable multimodal input-output without gradient-based training.
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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.