Jul 2026· Annual International ACM SIGIR Conference on Research and Development in Information Retrieval· pp. 4409-4413· 0 citations· 16 references
Computer Science
TL;DR
GraphSynthQA, a knowledge-graph)—guided synthesis framework in an open-web setting, which iteratively retrieves and verifies evidence from the internet to expand a KG, then synthesizes complex, answer-verifiable queries grounded in multi-evidence dependencies.
Abstract
Web browsing—widely used for information retrieval and fact verification—has become a fundamental capability of recently emerged large language model (LLM) agents, which is often elicited by training on complex questions requiring web search. However, this task faces challenges with respect to data and training: existing QA datasets are mostly 1-3 hop over closed corpora (e.g., Wikipedia); meanwhile, outcome-based on-policy RL that used by recent works is inefficient and brittle in long-horizon, tool-heavy browsing environments. To address these challenges, we introduce GraphSynthQA, a knowledge-graph (KG)—guided synthesis framework in an open-web setting. Starting from Wikidata seed entities, GraphSynthQA iteratively retrieves and verifies evidence from the internet to expand a KG, then synthesizes complex, answer-verifiable queries grounded in multi-evidence dependencies. Building on the synthesized data, we train web-browsing agents with a compute-efficient two-stage recipe: (i) cold-start supervised fine-tuning on ReAct-style trajectories, and (ii) step-level Direct Preference Optimization (DPO), where preferences are constructed offline via single-step branched rollouts that contrast candidate actions by their downstream success rates, providing dense process supervision without expensive on-policy exploration. Experiments show that our approach consistently improves performance on challenging web-browsing benchmarks and remains competitive among models of similar size.
Search-on-Graph-R1 (\sogrone{}), which internalizes this navigation into a compact 8B model through supervised fine-tuning (SFT) followed by reinforcement learning (RL), which surpasses every frozen frontier-LLM system in their comparison and posts the strongest results on CWQ of any system the authors compare against.
J. Sun, Hao Yu, Fengran Mo et al.· arXiv.org· 1 citation· ⚡1
Integrating Knowledge Graphs (KGs) into Retrieval-Augmented Generation (RAG) can substantially improve LLM performance on complex question answering (QA) by reducing hallucinations and supplying structured context. However, building high-quality KGs over large corpora for edge scenarios is challenging: cloud-based processing introduces latency and dependency on remote services, while exhaustive on-device construction with LLMs is often computationally infeasible under limited hardware budgets. We observe that traditional non-LLM methods can efficiently capture explicit knowledge, and that real-world queries typically touch only a small, highly concentrated portion of the graph. As a result, static and exhaustive KG construction is redundant and inefficient. We propose Edge-AdaptiveKG, a resource-aware framework that combines an offline Seed KG (S-KG) with an online Query-driven KG (Q-KG). Lightweight non-LLM methods build the S-KG, while the LLM is invoked on demand during question answering to incrementally expand the Q-KG only when complex relations are needed. Experiments show that Edge-AdaptiveKG reduces computational overhead and inference latency, enabling KG-enhanced RAG on resource-constrained devices while maintaining competitive QA accuracy.
Yuyu Du, Juxin Niu, Chun Jason Xue et al.· IEEE International Conferenc...· 0 citations
We present EXYGEN (EXplore Your Graphs ENgine), a framework for knowledge graph (KG) understanding that enables conversational access to KGs at scale. We address two questions in sequence. First, how effectively can LLMs perform text-to-SPARQL generation given only automatically derived structured metadata and small graph samples, rather than task-specific fine-tuning? We integrate VoID descriptions and ShEx schemas into a retrieval-augmented generation (RAG) pipeline and ablate KG-derived context on the SciQA benchmark. Our best configuration -- combining ShEx schemas, retrieved triples, and example question-query pairs -- reaches an exact match of 0.419 on execution results without any LLM fine-tuning. We further find that lexical metrics such as F1 poorly predict query correctness, and that larger general-purpose LLMs can outperform smaller code-specialized ones once given sufficient context. Second, we ask how to generate the structured metadata that this method relies on from very large KGs, where KG metadata generation becomes computationally intractable. We introduce a predicate-coverage-aware parallel graph sampling strategy that preserves structural diversity while remaining computationally tractable. On OpenCitations Meta and GESIS, it retains high predicate coverage with minimal triple loss and reduces runtime by over 80x; on ORKG, sampling is not just faster but the only tractable path to obtain complete metadata. Together, these results show that structured schema context and lightweight prompting can substantially reduce reliance on fine-tuning for scalable conversational access to KGs, though closing the remaining gap to fully fine-tuned approaches will likely require reducing dependence on curated question-query exemplars -- whether through synthetic generation or an execution-feedback-driven approach -- and validating these findings beyond a single benchmark.
Harshdeep Singh, Yu-Rui Zhu, Giovanni Colavizza et al.· 0 citations
This work proposes MARS, a scalable knowledge graph question answering (KGQA) approach that requires no model fine-tuning, and performs a structured retrieval procedure that links question entities to the KG and iteratively retrieves relevant next-hop information.
Nikit Srivastava, Daniel Vollmers, René Speck et al.· arXiv.org· 0 citations
This work proposes KGCache, an in-memory cache for one-hop knowledge graph neighborhoods, which is designed to be compatible with both iterative traversal (ToG) and one shot planning (RoG) KGQA paradigms and shows substantial entity reuse among starting entities and entities reached during traversal.
Uros Stanic, Chang-He Yuan, Sabuj Laskar et al.· 0 citations
Experimental results demonstrate that the VDGR-RAG method significantly outperforms a variety of RAG baselines in terms of both knowledge retrieval recall and QA accuracy.
Wenqi Chen, Haofei Yang, Rui Yang et al.· 0 citations
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