LLM-built attribute graphs enable zero-shot entity ranking in e-commerce with over 5% average precision gains and 57% less token usage per product compared to raw-text baselines.
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Mujica-MyGo decomposes multi-turn RAG interactions via multi-agent workflows and applies minimalist policy gradient optimization to improve performance on QA benchmarks while avoiding long-context problems.
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From Unstructured to Structured: LLM-Guided Attribute Graphs for Entity Search and Ranking
LLM-built attribute graphs enable zero-shot entity ranking in e-commerce with over 5% average precision gains and 57% less token usage per product compared to raw-text baselines.
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Advancing Multi-Agent RAG Systems with Minimalist Reinforcement Learning
Mujica-MyGo decomposes multi-turn RAG interactions via multi-agent workflows and applies minimalist policy gradient optimization to improve performance on QA benchmarks while avoiding long-context problems.
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