KoRe encodes 1-hop knowledge graph subgraphs as compact discrete tokens for injection into LLMs, achieving competitive benchmark performance with up to 10x token reduction.
InProceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
4 Pith papers cite this work. Polarity classification is still indexing.
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2026 4verdicts
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GraphWalker achieves state-of-the-art results on CWQ and WebQSP by training KGQA agents via synthetic random-walk trajectories in stage-wise SFT plus RL, with improved out-of-distribution generalization.
Full-horizon planning with on-demand replanning achieves accuracy parity with single-step planning in tool-calling agents for knowledge base and multi-hop question answering while consuming 2-3 times fewer tokens.
STAR is a semantic-tuned and tail-adaptive retriever for GraphRAG that uses cross-attention interaction learning and path-weighted contrastive learning to mitigate Semantic Shortcut Bias and Long-Tail Path Bias, reporting 1.8% retrieval and 2.2% QA gains.
citing papers explorer
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KoRe: Compact Knowledge Representations for Large Language Models
KoRe encodes 1-hop knowledge graph subgraphs as compact discrete tokens for injection into LLMs, achieving competitive benchmark performance with up to 10x token reduction.
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GraphWalker: Agentic Knowledge Graph Question Answering via Synthetic Trajectory Curriculum
GraphWalker achieves state-of-the-art results on CWQ and WebQSP by training KGQA agents via synthetic random-walk trajectories in stage-wise SFT plus RL, with improved out-of-distribution generalization.
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Do Agents Need to Plan Step-by-Step? Rethinking Planning Horizon in Data-Centric Tool Calling
Full-horizon planning with on-demand replanning achieves accuracy parity with single-step planning in tool-calling agents for knowledge base and multi-hop question answering while consuming 2-3 times fewer tokens.
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STAR: Semantic-Tuned and Tail-Adaptive Retriever for Graph-Augmented Generation
STAR is a semantic-tuned and tail-adaptive retriever for GraphRAG that uses cross-attention interaction learning and path-weighted contrastive learning to mitigate Semantic Shortcut Bias and Long-Tail Path Bias, reporting 1.8% retrieval and 2.2% QA gains.