{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:UVSHEXUDRIMPZL2FG6UOFP57TF","short_pith_number":"pith:UVSHEXUD","schema_version":"1.0","canonical_sha256":"a564725e838a18fcaf4537a8e2bfbf994998bd6000962a8ba31571c80269cb7c","source":{"kind":"arxiv","id":"2304.11116","version":3},"attestation_state":"computed","paper":{"title":"Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPT","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Jiawei Zhang","submitted_at":"2023-04-10T05:25:54Z","abstract_excerpt":"In this paper, we aim to develop a large language model (LLM) with the reasoning ability on complex graph data. Currently, LLMs have achieved very impressive performance on various natural language learning tasks, extensions of which have also been applied to study the vision tasks with multi-modal data. However, when it comes to the graph learning tasks, existing LLMs present very serious flaws due to their several inherited weaknesses in performing {multi-step logic reasoning}, {precise mathematical calculation} and {perception about the spatial and temporal factors}.\n  To address such chall"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2304.11116","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2023-04-10T05:25:54Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"ed945e23c692cf6bd6ec2813ddca4034245cf066555801dd7bac416bdc892ce0","abstract_canon_sha256":"6934e4cd6f0abd4702c8c4f77e017afa4c196a5d94a26604c8b4e5af83e99bda"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:09:09.654858Z","signature_b64":"zCPh28iDJKdJBVkAW5syTysTBs1kQ8Xk2XJNZ6Jtunm5SjmuKFjf5iDrToR8QnBH6NlKyh4Q4gujVWvx+5T3Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a564725e838a18fcaf4537a8e2bfbf994998bd6000962a8ba31571c80269cb7c","last_reissued_at":"2026-07-05T06:09:09.654399Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:09:09.654399Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPT","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Jiawei Zhang","submitted_at":"2023-04-10T05:25:54Z","abstract_excerpt":"In this paper, we aim to develop a large language model (LLM) with the reasoning ability on complex graph data. Currently, LLMs have achieved very impressive performance on various natural language learning tasks, extensions of which have also been applied to study the vision tasks with multi-modal data. However, when it comes to the graph learning tasks, existing LLMs present very serious flaws due to their several inherited weaknesses in performing {multi-step logic reasoning}, {precise mathematical calculation} and {perception about the spatial and temporal factors}.\n  To address such chall"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.11116","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2304.11116/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2304.11116","created_at":"2026-07-05T06:09:09.654457+00:00"},{"alias_kind":"arxiv_version","alias_value":"2304.11116v3","created_at":"2026-07-05T06:09:09.654457+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.11116","created_at":"2026-07-05T06:09:09.654457+00:00"},{"alias_kind":"pith_short_12","alias_value":"UVSHEXUDRIMP","created_at":"2026-07-05T06:09:09.654457+00:00"},{"alias_kind":"pith_short_16","alias_value":"UVSHEXUDRIMPZL2F","created_at":"2026-07-05T06:09:09.654457+00:00"},{"alias_kind":"pith_short_8","alias_value":"UVSHEXUD","created_at":"2026-07-05T06:09:09.654457+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":6,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.06865","citing_title":"Are Large Language Models Suitable for Graph Computation? Progress and Prospects","ref_index":260,"is_internal_anchor":false},{"citing_arxiv_id":"2605.27204","citing_title":"GraphReview: Scientific Paper Evaluation via LLM-Based Graph Message Passing","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2312.10997","citing_title":"Retrieval-Augmented Generation for Large Language Models: A Survey","ref_index":109,"is_internal_anchor":false},{"citing_arxiv_id":"2402.13116","citing_title":"A Survey on Knowledge Distillation of Large Language Models","ref_index":30,"is_internal_anchor":false},{"citing_arxiv_id":"2605.10366","citing_title":"EGL-SCA: Structural Credit Assignment for Co-Evolving Instructions and Tools in Graph Reasoning Agents","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2404.16130","citing_title":"From Local to Global: A Graph RAG Approach to Query-Focused Summarization","ref_index":75,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UVSHEXUDRIMPZL2FG6UOFP57TF","json":"https://pith.science/pith/UVSHEXUDRIMPZL2FG6UOFP57TF.json","graph_json":"https://pith.science/api/pith-number/UVSHEXUDRIMPZL2FG6UOFP57TF/graph.json","events_json":"https://pith.science/api/pith-number/UVSHEXUDRIMPZL2FG6UOFP57TF/events.json","paper":"https://pith.science/paper/UVSHEXUD"},"agent_actions":{"view_html":"https://pith.science/pith/UVSHEXUDRIMPZL2FG6UOFP57TF","download_json":"https://pith.science/pith/UVSHEXUDRIMPZL2FG6UOFP57TF.json","view_paper":"https://pith.science/paper/UVSHEXUD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2304.11116&json=true","fetch_graph":"https://pith.science/api/pith-number/UVSHEXUDRIMPZL2FG6UOFP57TF/graph.json","fetch_events":"https://pith.science/api/pith-number/UVSHEXUDRIMPZL2FG6UOFP57TF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UVSHEXUDRIMPZL2FG6UOFP57TF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UVSHEXUDRIMPZL2FG6UOFP57TF/action/storage_attestation","attest_author":"https://pith.science/pith/UVSHEXUDRIMPZL2FG6UOFP57TF/action/author_attestation","sign_citation":"https://pith.science/pith/UVSHEXUDRIMPZL2FG6UOFP57TF/action/citation_signature","submit_replication":"https://pith.science/pith/UVSHEXUDRIMPZL2FG6UOFP57TF/action/replication_record"}},"created_at":"2026-07-05T06:09:09.654457+00:00","updated_at":"2026-07-05T06:09:09.654457+00:00"}