SEGRA: Structured Experience-Guided Graph Reasoning Agent for Gremlin Based Question Answering

• URL: http://arxiv.org/abs/2607.22713v1
• Date: Tue, 21 Jul 2026 03:22:40 GMT
• Title: SEGRA: Structured Experience-Guided Graph Reasoning Agent for Gremlin Based Question Answering
• Abstract summary: We introduce SEGRA, an experience-guided agent for text-to-Gremlin question answering.<n>On an enterprise IT support benchmark, SEGRA achieves a $7.0times$ higher mean judge score than backbone-only chain-of-thought prompting.
• Score: 1.4924423820656074
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Enterprise IT support knowledge graphs capture rich relationships among cases, users, devices, symptoms, taxonomic categories, root causes, and historical resolutions. Yet querying them in Gremlin requires knowledge of graph schemas, traversal semantics, edge directionality, and property-graph-specific constraints, making them difficult for non-expert operators to use. We introduce SEGRA, an experience-guided agent for enterprise text-to-Gremlin question answering. SEGRA integrates intent routing, schema- and taxonomy-grounded query generation, multi-shot decomposition, execution-aware verification, and a curriculum-bootstrapped skill library that reuses verified query patterns. On an enterprise IT support benchmark, SEGRA achieves a $7.0\times$ higher mean judge score than backbone-only chain-of-thought prompting. Its skill library further reduces LLM calls by $20\%$ and dollar cost by $18\%$ relative to SEGRA without skills, while preserving answer quality. These results show that schema-grounded agent design and reusable execution experience improve both accuracy and efficiency for enterprise graph QA.

Related papers

• Inference-Time Graph Engineering for Multi-Agent LLM Workflows [51.56038180639833]
  We introduce ReActNet, a training-free framework that compiles a query and a set of role-specialized agents into a sequence of directed communication graphs.<n>We show that ReActNet consistently improves over fixed-topology and learned-topology baselines while maintaining competitive inference cost.
  arXiv  Detail & Related papers  (2026-09-04T23:32:11Z)
• SelfGraphRAG: Bridging the Supervision Gap in Graph-Based RAG with Synthetic QA Generation [10.08021023788401]
  SelfGraphRAG is a framework that generates question-answer pairs directly from knowledge graph structure and uses them to train a query-conditioned graph retriever.<n>We show that SelfGraphRAG improves retrieval precision and downstream reasoning performance over embedding-based baselines.
  arXiv  Detail & Related papers  (2026-08-25T20:18:05Z)
• Schema-Agnostic Graph Reasoning Agent for Hybrid Knowledge Graphs [42.698418800007865]
  We present GRA, a Graph Reasoning Agent that explores hybrid knowledge graphs.<n>On UFK-M, an industrial benchmark of 258 analytical questions, GRA beats a full-context agent by 5.1 pp.<n>A graph-free control shows the gain comes chiefly from selective agentic access rather than graph topology.
  arXiv  Detail & Related papers  (2026-08-16T16:10:32Z)
• Topology-Aware Reasoning over Incomplete Knowledge Graph with Graph-Based Soft Prompting [11.222318740825388]
  Large Language Models (LLMs) have shown remarkable capabilities across various tasks but remain prone to hallucinations in knowledge-intensive scenarios.<n>In this paper, we propose a novel graph-based soft prompting framework that shifts the reasoning paradigm to subgraph-level reasoning.<n> Experiments on four multi-hop KBQA benchmarks show that our approach achieves state-of-the-art performance on three of them, demonstrating its effectiveness.
  arXiv  Detail & Related papers  (2026-04-14T09:27:52Z)
• GraphWalk: Enabling Reasoning in Large Language Models through Tool-Based Graph Navigation [0.2999888908665658]
  We present GraphWalk, a problem-agnostic, training-free, tool-based framework that allows off-the-shelf LLMs to reason through sequential graph navigation.<n>We evaluate whether models equipped with GraphWalk can compose these operations into correct multi-step reasoning chains.
  arXiv  Detail & Related papers  (2026-04-02T04:43:01Z)
• Graph-Anchored Knowledge Indexing for Retrieval-Augmented Generation [53.42323544075114]
  We propose GraphAnchor, a novel Graph-Anchored Knowledge Indexing approach.<n> Experiments on four multi-hop question answering benchmarks demonstrate the effectiveness of GraphAnchor.
  arXiv  Detail & Related papers  (2026-01-23T05:41:05Z)
• Search-on-Graph: Iterative Informed Navigation for Large Language Model Reasoning on Knowledge Graphs [26.0585592684229]
  Large language models (LLMs) have demonstrated impressive reasoning abilities yet remain unreliable on knowledge-intensive, multi-hop questions.<n>We propose Search-on-Graph (SoG), a simple yet effective framework that enables LLMs to perform iterative informed graph navigation.<n>We demonstrate particularly strong gains on Wikidata benchmarks (+16% improvement over previous best methods) alongside consistent improvements on Freebase benchmarks.
  arXiv  Detail & Related papers  (2025-10-09T21:20:16Z)
• Query-Centric Graph Retrieval Augmented Generation [15.423162448800134]
  QCG-RAG is a query-centric graph RAG framework that enables query-granular indexing and multi-hop chunk retrieval.<n> Experiments on LiHuaWorld and MultiHop-RAG show that QCG-RAG consistently outperforms prior chunk-based and graph-based RAG methods in question answering accuracy.
  arXiv  Detail & Related papers  (2025-09-25T14:35:44Z)
• Enrich-on-Graph: Query-Graph Alignment for Complex Reasoning with LLM Enriching [61.824094419641575]
  Large Language Models (LLMs) struggle with hallucinations and factual errors in knowledge-intensive scenarios like knowledge graph question answering (KGQA)<n>We attribute this to the semantic gap between structured knowledge graphs (KGs) and unstructured queries, caused by inherent differences in their focuses and structures.<n>Existing methods usually employ resource-intensive, non-scalable reasoning on vanilla KGs, but overlook this gap.<n>We propose a flexible framework, Enrich-on-Graph (EoG), which leverages LLMs' prior knowledge to enrich KGs, bridge the semantic gap between graphs and queries.
  arXiv  Detail & Related papers  (2025-09-25T06:48:52Z)
• Align-GRAG: Reasoning-Guided Dual Alignment for Graph Retrieval-Augmented Generation [79.75818239774952]
  Large language models (LLMs) have demonstrated remarkable capabilities, but still struggle with issues like hallucinations and outdated information.<n>Retrieval-augmented generation (RAG) addresses these issues by grounding LLM outputs in external knowledge with an Information Retrieval (IR) system.<n>We propose Align-GRAG, a novel reasoning-guided dual alignment framework in post-retrieval phrase.
  arXiv  Detail & Related papers  (2025-05-22T05:15:27Z)
• Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning [62.640169289390535]
  SPLIT-RAG is a multi-agent RAG framework that addresses the limitations with question-driven semantic graph partitioning and collaborative subgraph retrieval.<n>The innovative framework first create Semantic Partitioning of Linked Information, then use the Type-Specialized knowledge base to achieve Multi-Agent RAG.<n>The attribute-aware graph segmentation manages to divide knowledge graphs into semantically coherent subgraphs, ensuring subgraphs align with different query types.<n>A hierarchical merging module resolves inconsistencies across subgraph-derived answers through logical verifications.
  arXiv  Detail & Related papers  (2025-05-20T06:44:34Z)
• G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering [61.93058781222079]
  We develop a flexible question-answering framework targeting real-world textual graphs. We introduce the first retrieval-augmented generation (RAG) approach for general textual graphs. G-Retriever performs RAG over a graph by formulating this task as a Prize-Collecting Steiner Tree optimization problem.
  arXiv  Detail & Related papers  (2024-02-12T13:13:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.