Agentic search narrows the gap between dense RAG and GraphRAG but does not remove GraphRAG's advantage on complex multi-hop reasoning.
arXiv preprint arXiv:2501.18922 , year=
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GAPD adds dense token-level guidance from gold actions to outcome-based RL for KBQA via mid-anchor matching and outperforms SOTA on WebQSP, GrailQA, and GraphQ.
AlphaMemo equips LLM alpha-mining agents with AST-diff motif memory, residual learning, and asymmetric veto control to improve out-of-sample factor discovery on CSI 500 and S&P 500.
citing papers explorer
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Do We Still Need GraphRAG? Benchmarking RAG and GraphRAG for Agentic Search Systems
Agentic search narrows the gap between dense RAG and GraphRAG but does not remove GraphRAG's advantage on complex multi-hop reasoning.
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GAPD: Gold-Action Policy Distillation for Agentic Reinforcement Learning in Knowledge Base Question Answering
GAPD adds dense token-level guidance from gold actions to outcome-based RL for KBQA via mid-anchor matching and outperforms SOTA on WebQSP, GrailQA, and GraphQ.
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AlphaMemo: Structured Search-Process Memory for Self-Evolving Alpha Mining Agents
AlphaMemo equips LLM alpha-mining agents with AST-diff motif memory, residual learning, and asymmetric veto control to improve out-of-sample factor discovery on CSI 500 and S&P 500.