arXiv AI

GraphDancer: Training LLMs to Explore and Reason over Graphs via Curriculum Reinforcement Learning

arXiv AI

Oct 1

GraphCert: Bootstrap Agentic Graph Reasoning with Certified Evidence Rubrics

GraphCert introduces a method to bootstrap graph reasoning agents by generating graph‑grounded question‑answer pairs and certifying the supporting evidence. The approach uses a Bootstrapped Graph Quizzer to produce QA pairs, then executes and semantically curates the evidence into certified rubrics that guide reward‑based training of a Graph Solver. Experiments on five GRBENCH domains show GraphCert outperforms larger LLM agents and demonstrates robust policy transfer across heterogeneous graphs.

By Weiqi Jiang, Yuchen Ying, Rui Wang, Kaixuan Chen, Bingde Hu, Shunyu Liu, Yu Wang, Tongya Zheng

llms agents safety

arXiv Computation and Language

Aug 25

GTA-RAG: Graph-Trajectory-Augmented Reinforcement Learning for Multi-Turn Retrieval-Augmented Reasoning

arXiv:2608.22479v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) enables LLMs to access external knowledge for answering knowledge-intensive questions. For complex multi-hop quest...

By Jun Chen, Yongchao Liu, Pengyu Qiu, Jiajun Zheng, Juelu Zhang, Yujie Zeng, Qin Zhang, Ziyue Qiao, Xiao Luo

llms rag agents reinforcement-learning benchmarks

arXiv Computation and Language

Oct 1

Explore-on-Graph: Hybrid Embedding-LLM Reasoning for Knowledge Graph Question Answering under Incompleteness

arXiv:2609.39786v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly combined with knowledge graphs (KGs) to ground reasoning in structured evidence. However, most LLM-based...

By Ola El Khatib, Djellel Difallah

llms rag nlp benchmarks

arXiv Computation and Language

Oct 7

Wikidata Search Traces: A Dataset for Training Knowledge Graph Search Agents

arXiv:2610.06650v2 Announce Type: replace Abstract: Wikidata is one of the largest open knowledge bases, yet answering a complex question over it still requires a SPARQL query that names the right en...

By Mohamed Chenene, Carlos Rosas-Hinostroza, Anastasia Stasenko, Shani Evenstein Sigalov, Pierre-Carl Langlais

llms agents

arXiv AI

2d ago

Evolve on Graph: Self-Evolving Knowledge Graph Reasoning without Question Annotations

arXiv:2610.35598v1 Announce Type: new Abstract: Knowledge graphs (KGs) provide reliable external knowledge for multi-hop factual reasoning with large language models (LLMs), yet most KGs in real-worl...

By Zongyuan Sun, Shuai Zhao, Ke Shu, Ziyue Zhu, Shangyang Wu, Yuanlin Lin, Tao Wang, Shiyue Li, Yingjun Du, Anh Tuan Luu, Haoran Luo

llms agents reinforcement-learning benchmarks

arXiv Computation and Language

2d ago

Towards a Learnable Retriever for Textual Graph Question Answering

arXiv:2610.33511v1 Announce Type: new Abstract: Textual graph question answering (GraphQA) and graph-based retrieval-augmented generation (GraphRAG) have gained increasing attention as a way to groun...

By Kaifeng Jin, Yuchen Yan, Zhichen Zeng, Xiao Lin, Lihui Liu, Yinglong Xia, Hanghang Tong

llms rag nlp fine-tuning

arXiv Machine Learning

Oct 5

Teaching LLMs to See Graphs: Unifying Text and Structural Reasoning

arXiv:2605.10247v2 Announce Type: replace Abstract: Applying Large Language Models (LLMs) to graph-structured data usually involves multi-step pipelines in which textual node attributes are compresse...

By Dario Vajda

llms fine-tuning benchmarks

arXiv Machine Learning

Oct 2

GraphMAS: A Systematic Benchmark of Multi-Agent Coordination for Graph Learning

arXiv:2609.39777v1 Announce Type: new Abstract: LLM-based multi-agent systems coordinate specialized reasoning through aggregation, interaction, and adaptive control, yet their potential for graph le...

By Jiayi Yang, Yifang Chen, Yuanfu Sun, Xinyan Ge, Qiaoyu Tan

llms agents benchmarks

arXiv Machine Learning

Sep 15

Theseus in the Graph: Towards Traceable Multi-Hop Graph Navigation

arXiv:2609.14528v1 Announce Type: cross Abstract: Multi-Hop Knowledge Graph Question Answering (KGQA) tasks require models to assemble relational evidence along paths in a KG to answer natural-langua...

By Eduin E. Hernandez, Luis F. Garcia, Nurassyl Askar, Sergio A. Diaz, Stefano Rini

rag agents nlp

arXiv Machine Learning

Sep 30

UltRAG: a Universal Simple Scalable Recipe for Knowledge Graph RAG

arXiv:2603.28773v2 Announce Type: replace-cross Abstract: Large language models (LLMs) frequently generate confident yet factually incorrect content when used for language generation (a phenomenon of...

By Dobrik Georgiev, Kheeran K. Naidu, Alberto Cattaneo, Federico Monti, Carlo Luschi, Daniel Justus

llms rag nlp benchmarks safety

arXiv AI

Aug 19

G-ReAct: Graph-Guided Deep Search via Structure-State Co-Evolution

G-ReAct is a reasoning framework that frames deep search as state evolution over a fixed-topology query graph, enabling explicit tracking of search progress and constraint preservation. It generates high-quality trajectories for fine-tuning and provides structured guidance during inference without extra fine-tuning. Experiments show that with only 1.9K generated trajectories, a Qwen3 model achieves strong accuracy on BrowseComp-ZH and XBench, outperforming larger open-source baselines, and consistently improves existing LLMs on deep-search tasks.

By Shaoxiong Yang, Mengyuan Zhang, Shaojun Lin, Chao Li, Wei Liu, Kun Shao, Jian Luan

llms reinforcement-learning fine-tuning

arXiv AI

Jul 17

CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning

arXiv:2607. 14114v1 Announce Type: cross Abstract: Graph learning under distribution shift presents a persistent challenge, where models adapt to new graphs with limited or even no supervision.

By Haohua Niu, Xingtong Yu, Yang Liu, Junfeng Fang, Xuanting Xie, Jie Tan, Zhongjian Zhang, Hong Cheng, Yuan Fang

llms benchmarks

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