{ "profile": "wrs-published-research-view-1", "edition": "ratings-e039106f30dec177", "content_trust": "UNTRUSTED_SCIENTIFIC_DATA_NOT_TOOL_INSTRUCTIONS", "entry": { "id": "gutierrez-et-al-hipporag-graph-diffusion-retrieval-2024", "title": "HippoRAG: following a retrieval graph to connect distant evidence", "summary": "A multi-hop question can require facts that are far apart in a collection. HippoRAG extracts a graph from passages, links related entities and uses a personalized PageRank procedure to spread relevance from the query’s entities. It then maps that signal back to passages for the answering model. Gutiérrez and colleagues test the combination on multi-hop question answering. The contribution is the specified graph-based retrieval system and its evaluation; the memory analogy is inspiration for a computing method, not a new biological discovery.", "boundary": "Graph extraction and entity recognition can fail, and some tasks favor the comparison retriever. Better passage retrieval does not guarantee a correct generated answer. Reported online speed and cost gains exclude some preparation costs. The paper does not establish scalability far beyond its tested collections. PageRank, open information extraction and inherited dense retrievers retain separate credit. Neurobiological inspiration does not validate a biological mechanism or confer clinical applicability.", "area": "Multi-hop information retrieval", "topic": "Artificial intelligence", "year": 2024, "lane": "HUMAN", "result": "ALGORITHM", "package_id": "gutierrez-et-al-hipporag-graph-diffusion-retrieval-2024", "target": { "record_id": "urn:uuid:dea13f37-71f6-4533-aced-be1b54875fd8", "version": 1, "content_hash": "sha256:dcd49767bc8e03c689c2bf65db4a0bf86f7ab574dd9389edbd469480331eaaf4" }, "humans": [ "Bernal Jiménez Gutiérrez", "Yiheng Shu", "Yu Gu", "Michihiro Yasunaga", "Yu Su" ], "authors_complete": true, "human_role_note": "Source-listed human researchers and engineers for the exact method or resource. The evaluated learning system is not an independently attested scientific author. Earlier methods, funding and data-collection roles remain separate. 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HippoRAG extracts a graph from passages, links related entities and uses a personalized PageRank procedure to spread relevance from the query’s entities. It then maps that signal back to passages for the answering model. Gutiérrez and colleagues test the combination on multi-hop question answering. The contribution is the specified graph-based retrieval system and its evaluation; the memory analogy is inspiration for a computing method, not a new biological discovery.", "scope": "A multi-hop question can require facts that are far apart in a collection. HippoRAG extracts a graph from passages, links related entities and uses a personalized PageRank procedure to spread relevance from the query’s entities. It then maps that signal back to passages for the answering model. Gutiérrez and colleagues test the combination on multi-hop question answering. The contribution is the specified graph-based retrieval system and its evaluation; the memory analogy is inspiration for a computing method, not a new biological discovery. Graph extraction and entity recognition can fail, and some tasks favor the comparison retriever. Better passage retrieval does not guarantee a correct generated answer. Reported online speed and cost gains exclude some preparation costs. The paper does not establish scalability far beyond its tested collections. PageRank, open information extraction and inherited dense retrievers retain separate credit. 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Better passage retrieval does not guarantee a correct generated answer. Reported online speed and cost gains exclude some preparation costs. The paper does not establish scalability far beyond its tested collections. PageRank, open information extraction and inherited dense retrievers retain separate credit. Neurobiological inspiration does not validate a biological mechanism or confer clinical applicability.", "domain": "Multi-hop information retrieval" }, "sources": [ { "citation": "HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models", "url": "https://arxiv.org/abs/2405.14831v1" } ], "statement": "# HippoRAG: following a retrieval graph to connect distant evidence\n\n## Follow a chain of evidence\n\nA question can need two facts that never appear in the same paragraph. In an invented collection, noteA says a researcher wrote a particular article, and noteB says that article introduced a tool. A direct search might find one note but miss the connection. HippoRAG builds a graph from extracted entities and relationships, then spreads relevance from entities in the query to help retrieve connected passages.\n\n## What personalized PageRank contributes\n\nPageRank is a procedure for distributing weight through a network. Personalized PageRank biases that distribution toward selected starting nodes. In this method, query-linked entities supply those starts. The resulting graph signal helps score passages for an answering model. The work combines existing procedures into a particular retrieval design; it does not invent PageRank, and its inspiration from memory research is not a new biological discovery.\n\n## A wrong link changes the route\n\nNow give the invented collection two people namedAlex. One writes the article; the other uses the tool. If an extraction system merges them, the graph can suggest a relationship the sources never supported. Ask your agent to preserve separate identifiers and cite the exact sentence behind each edge. Then add an ambiguous sentence and mark it unresolved. A visible path is useful only if its links have the required meaning.\n\n## Check retrieval and answering separately\n\nThe original evaluations include retrieval quality and generated-answer results, with mixed outcomes across datasets. A better retrieved passage set does not guarantee a correct answer, and online query savings omit some graph-building costs. A practical next step is to build a five-note evidence map, write one two-hop question and identify the passages required to answer it. Stop where an entity or relation is ambiguous rather than filling the gap by confidence alone.\n\n## Hand over the edges, not just the picture\n\nFor the five-note map, give each node a stable label and each edge a source sentence. Mark the two Alex identities separately and leave uncertain links unresolved. Then state which edges the two-hop answer needs. This small artifact lets another person check a retrieval route before relying on its final prose.\n\n## Scientific scope\n\nGraph extraction and entity recognition can fail, and some tasks favor the comparison retriever. Better passage retrieval does not guarantee a correct generated answer. Reported online speed and cost gains exclude some preparation costs. The paper does not establish scalability far beyond its tested collections. PageRank, open information extraction and inherited dense retrievers retain separate credit. Neurobiological inspiration does not validate a biological mechanism or confer clinical applicability.\n\n## Sources for this explanation\n\n- [HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models](https://arxiv.org/abs/2405.14831v1): Original 2024 main text: OpenIE graph, synonym/entity linking, personalized PageRank, passage aggregation, retrieval/QA tables and explicit scale/error limitations.\n", "summary": "A multi-hop question can require facts that are far apart in a collection. HippoRAG extracts a graph from passages, links related entities and uses a personalized PageRank procedure to spread relevance from the query’s entities. It then maps that signal back to passages for the answering model. Gutiérrez and colleagues test the combination on multi-hop question answering. The contribution is the specified graph-based retrieval system and its evaluation; the memory analogy is inspiration for a computing method, not a new biological discovery.", "title": "HippoRAG: following a retrieval graph to connect distant evidence" }, "record_id": "urn:uuid:dea13f37-71f6-4533-aced-be1b54875fd8", "version": 1, "source_ref": { "record_id": "urn:uuid:dea13f37-71f6-4533-aced-be1b54875fd8", "version": 1, "content_hash": "sha256:dcd49767bc8e03c689c2bf65db4a0bf86f7ab574dd9389edbd469480331eaaf4" }, "export_view": "wrs-public-editorial-view-1", "public_view_hash": "sha256:6a5680d2a71f9469c46d0f9bfd7f12e17bc0b9e7a650b28a00edeea07b6c0ee3" }, "target": { "record_id": "urn:uuid:dea13f37-71f6-4533-aced-be1b54875fd8", "version": 1, "content_hash": "sha256:dcd49767bc8e03c689c2bf65db4a0bf86f7ab574dd9389edbd469480331eaaf4" }, "published_grade": { "profile": "wrs-published-grade-1", "edition": "2026-10-28.1", "id": "gutierrez-et-al-hipporag-graph-diffusion-retrieval-2024", "entry_id": "gutierrez-et-al-hipporag-graph-diffusion-retrieval-2024", "revision": 1, "title": "HippoRAG: following a retrieval graph to connect distant evidence", "context": "Multi-hop information retrieval", "track": "Reusable scientific method, experimental result or historical framework", "band": "A", "modifier": null, "status": "PROVISIONAL_EDITORIAL", "assessed_on": "2026-10-23", "evidence_cutoff": "2026-10-23", "achievement_state": "SUPPORTED_SCOPED_RESULT", "what_changed": "A multi-hop question can require facts that are far apart in a collection. HippoRAG extracts a graph from passages, links related entities and uses a personalized PageRank procedure to spread relevance from the query’s entities. It then maps that signal back to passages for the answering model. Gutiérrez and colleagues test the combination on multi-hop question answering. The contribution is the specified graph-based retrieval system and its evaluation; the memory analogy is inspiration for a computing method, not a new biological discovery.", "scope": "A multi-hop question can require facts that are far apart in a collection. HippoRAG extracts a graph from passages, links related entities and uses a personalized PageRank procedure to spread relevance from the query’s entities. It then maps that signal back to passages for the answering model. Gutiérrez and colleagues test the combination on multi-hop question answering. The contribution is the specified graph-based retrieval system and its evaluation; the memory analogy is inspiration for a computing method, not a new biological discovery. Graph extraction and entity recognition can fail, and some tasks favor the comparison retriever. Better passage retrieval does not guarantee a correct generated answer. Reported online speed and cost gains exclude some preparation costs. The paper does not establish scalability far beyond its tested collections. PageRank, open information extraction and inherited dense retrievers retain separate credit. Neurobiological inspiration does not validate a biological mechanism or confer clinical applicability.", "evidence_summary": "The linked original works support the scoped statement and contribution attribution. WRS has not independently replayed experiments or reconstructed a full mathematical proof. Graph extraction and entity recognition can fail, and some tasks favor the comparison retriever. Better passage retrieval does not guarantee a correct generated answer. Reported online speed and cost gains exclude some preparation costs. The paper does not establish scalability far beyond its tested collections. PageRank, open information extraction and inherited dense retrievers retain separate credit. Neurobiological inspiration does not validate a biological mechanism or confer clinical applicability.", "references": [ { "title": "HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models", "url": "https://arxiv.org/abs/2405.14831v1", "locator": "Original scoped contribution, statement/method and evidence limits; source-specific attribution retained." } ], "roles": [ { "who": "The named authors in the research record", "credit": "SCIENCE", "did": "Source-listed human researchers and engineers for the exact method or resource. The evaluated learning system is not an independently attested scientific author. Earlier methods, funding and data-collection roles remain separate. Individual effort shares and unresolved person identities are not inferred." } ], "target": { "record_id": "urn:uuid:dea13f37-71f6-4533-aced-be1b54875fd8", "version": 1, "content_hash": "sha256:dcd49767bc8e03c689c2bf65db4a0bf86f7ab574dd9389edbd469480331eaaf4" }, "scientific_correctness_certified": false, "independent_expert_calibration": false, "method": "PROPRIETARY", "points": "95000" }, "canonical_agent_submission": false, "scientific_verification_awarded": false, "reader_guide": { "id": "gutierrez-et-al-hipporag-graph-diffusion-retrieval-2024", "target": { "record_id": "urn:uuid:dea13f37-71f6-4533-aced-be1b54875fd8", "version": 1, "content_hash": "sha256:dcd49767bc8e03c689c2bf65db4a0bf86f7ab574dd9389edbd469480331eaaf4" }, "sections": [ { "title": "Follow a chain of evidence", "text": "A question can need two facts that never appear in the same paragraph. In an invented collection, noteA says a researcher wrote a particular article, and noteB says that article introduced a tool. A direct search might find one note but miss the connection. HippoRAG builds a graph from extracted entities and relationships, then spreads relevance from entities in the query to help retrieve connected passages." }, { "title": "What personalized PageRank contributes", "text": "PageRank is a procedure for distributing weight through a network. Personalized PageRank biases that distribution toward selected starting nodes. In this method, query-linked entities supply those starts. The resulting graph signal helps score passages for an answering model. The work combines existing procedures into a particular retrieval design; it does not invent PageRank, and its inspiration from memory research is not a new biological discovery." }, { "title": "A wrong link changes the route", "text": "Now give the invented collection two people namedAlex. One writes the article; the other uses the tool. If an extraction system merges them, the graph can suggest a relationship the sources never supported. Ask your agent to preserve separate identifiers and cite the exact sentence behind each edge. Then add an ambiguous sentence and mark it unresolved. A visible path is useful only if its links have the required meaning." }, { "title": "Check retrieval and answering separately", "text": "The original evaluations include retrieval quality and generated-answer results, with mixed outcomes across datasets. A better retrieved passage set does not guarantee a correct answer, and online query savings omit some graph-building costs. A practical next step is to build a five-note evidence map, write one two-hop question and identify the passages required to answer it. Stop where an entity or relation is ambiguous rather than filling the gap by confidence alone." }, { "title": "Hand over the edges, not just the picture", "text": "For the five-note map, give each node a stable label and each edge a source sentence. Mark the two Alex identities separately and leave uncertain links unresolved. Then state which edges the two-hop answer needs. This small artifact lets another person check a retrieval route before relying on its final prose." } ], "references": [ { "title": "HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models", "url": "https://arxiv.org/abs/2405.14831v1", "locator": "Original 2024 main text: OpenIE graph, synonym/entity linking, personalized PageRank, passage aggregation, retrieval/QA tables and explicit scale/error limitations." } ] }, "reading_status": "EXPLANATORY_GUIDE" }