Code-on-Graph lets LLMs turn retrieved KG facts into Python class instances and generate executable code for reasoning, outperforming prior LLM-KG methods by up to 10.5% on WebQSP, CWQ, and GrailQA.
InProceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
9 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 9verdicts
UNVERDICTED 9representative citing papers
KG-Guard augments knowledge graphs with a virtual question node and uses a graph encoder plus MLP to classify LLM-proposed answers as hallucinations or not, reporting superior F1 scores and downstream improvements on three benchmarks.
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.
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.
ARLtR is a framework for jointly constructing knowledge graphs, embeddings, and grounded QA pairs from text, released as a Roman Empire dataset with over 19,000 entities and 8,400 QA pairs.
OCC-RAG develops task-specialized SLMs (0.6B and 1.7B) via a new synthetic data pipeline for multi-hop reasoning and context faithfulness, claiming to match or exceed 2-6x larger general models on HotpotQA, MuSiQue, TAT-QA, ConFiQA, and MuSiQue-Un.
SPADER is an RL method for multi-answer QA that claims better recall and F1 via peer-aligned step-level advantages and diversity rewards on four benchmarks.
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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Code-on-Graph: Iterative Programmatic Reasoning via Large Language Models on Knowledge Graphs
Code-on-Graph lets LLMs turn retrieved KG facts into Python class instances and generate executable code for reasoning, outperforming prior LLM-KG methods by up to 10.5% on WebQSP, CWQ, and GrailQA.
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KG-Guard: Graph-Based Hallucination Detection for Knowledge Base Question Answering
KG-Guard augments knowledge graphs with a virtual question node and uses a graph encoder plus MLP to classify LLM-proposed answers as hallucinations or not, reporting superior F1 scores and downstream improvements on three benchmarks.
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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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All Relations Lead to Rome: Automated Knowledge Graph Creation and Question Generation
ARLtR is a framework for jointly constructing knowledge graphs, embeddings, and grounded QA pairs from text, released as a Roman Empire dataset with over 19,000 entities and 8,400 QA pairs.
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OCC-RAG: Optimal Cognitive Core for Faithful Question Answering
OCC-RAG develops task-specialized SLMs (0.6B and 1.7B) via a new synthetic data pipeline for multi-hop reasoning and context faithfulness, claiming to match or exceed 2-6x larger general models on HotpotQA, MuSiQue, TAT-QA, ConFiQA, and MuSiQue-Un.
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SPADER: Step-wise Peer Advantage with Diversity-Aware Exploration Rewards for Multi-Answer Question Answering
SPADER is an RL method for multi-answer QA that claims better recall and F1 via peer-aligned step-level advantages and diversity rewards on four benchmarks.
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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.