{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:C5MU3A2KSWFL5VXUVLMCLYJZ5O","short_pith_number":"pith:C5MU3A2K","schema_version":"1.0","canonical_sha256":"17594d834a958abed6f4aad825e139eba8c2afbb00d0046e22a1749e612320db","source":{"kind":"arxiv","id":"2606.30247","version":1},"attestation_state":"computed","paper":{"title":"Grounding LLM Reasoning under Incomplete Graph Evidence","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Fanghui Song, Jiaqi Li","submitted_at":"2026-06-29T12:56:21Z","abstract_excerpt":"Knowledge graphs can guide large language models (LLMs) reasoning, but the graph seen by a system is usually a retrieved, linked, temporally scoped, and incomplete evidence state rather than a complete account of truth. We develop a theoretical perspective on grounding observable LLM trajectories under such incomplete graph evidence.The evidence state induces entity anchors, typed relation residuals, path energies, and support regions, while the language model supplies a prior over candidate trajectories. We show that, under open-world incompleteness, no hard rule based only on the observed st"},"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":"2606.30247","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2026-06-29T12:56:21Z","cross_cats_sorted":[],"title_canon_sha256":"b51d6468fd84940c0abb8c72dce4058f26eecf470789bd122629045a161f2a44","abstract_canon_sha256":"6be434858957565919c10d590c73d96f334218a7ba379fbdad24108800aa833b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-30T02:17:56.028192Z","signature_b64":"4Y/m6gH9NF6o5l3jUIYbad5+3xjFPgArt0YA3kInwrylGcW+eh8QUMsBBAzgqXruR7XJOgH3ll3EYpmB67GBDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"17594d834a958abed6f4aad825e139eba8c2afbb00d0046e22a1749e612320db","last_reissued_at":"2026-06-30T02:17:56.027748Z","signature_status":"signed_v1","first_computed_at":"2026-06-30T02:17:56.027748Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Grounding LLM Reasoning under Incomplete Graph Evidence","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Fanghui Song, Jiaqi Li","submitted_at":"2026-06-29T12:56:21Z","abstract_excerpt":"Knowledge graphs can guide large language models (LLMs) reasoning, but the graph seen by a system is usually a retrieved, linked, temporally scoped, and incomplete evidence state rather than a complete account of truth. We develop a theoretical perspective on grounding observable LLM trajectories under such incomplete graph evidence.The evidence state induces entity anchors, typed relation residuals, path energies, and support regions, while the language model supplies a prior over candidate trajectories. We show that, under open-world incompleteness, no hard rule based only on the observed st"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.30247","kind":"arxiv","version":1},"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/2606.30247/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":"2606.30247","created_at":"2026-06-30T02:17:56.027809+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.30247v1","created_at":"2026-06-30T02:17:56.027809+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.30247","created_at":"2026-06-30T02:17:56.027809+00:00"},{"alias_kind":"pith_short_12","alias_value":"C5MU3A2KSWFL","created_at":"2026-06-30T02:17:56.027809+00:00"},{"alias_kind":"pith_short_16","alias_value":"C5MU3A2KSWFL5VXU","created_at":"2026-06-30T02:17:56.027809+00:00"},{"alias_kind":"pith_short_8","alias_value":"C5MU3A2K","created_at":"2026-06-30T02:17:56.027809+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/C5MU3A2KSWFL5VXUVLMCLYJZ5O","json":"https://pith.science/pith/C5MU3A2KSWFL5VXUVLMCLYJZ5O.json","graph_json":"https://pith.science/api/pith-number/C5MU3A2KSWFL5VXUVLMCLYJZ5O/graph.json","events_json":"https://pith.science/api/pith-number/C5MU3A2KSWFL5VXUVLMCLYJZ5O/events.json","paper":"https://pith.science/paper/C5MU3A2K"},"agent_actions":{"view_html":"https://pith.science/pith/C5MU3A2KSWFL5VXUVLMCLYJZ5O","download_json":"https://pith.science/pith/C5MU3A2KSWFL5VXUVLMCLYJZ5O.json","view_paper":"https://pith.science/paper/C5MU3A2K","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.30247&json=true","fetch_graph":"https://pith.science/api/pith-number/C5MU3A2KSWFL5VXUVLMCLYJZ5O/graph.json","fetch_events":"https://pith.science/api/pith-number/C5MU3A2KSWFL5VXUVLMCLYJZ5O/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/C5MU3A2KSWFL5VXUVLMCLYJZ5O/action/timestamp_anchor","attest_storage":"https://pith.science/pith/C5MU3A2KSWFL5VXUVLMCLYJZ5O/action/storage_attestation","attest_author":"https://pith.science/pith/C5MU3A2KSWFL5VXUVLMCLYJZ5O/action/author_attestation","sign_citation":"https://pith.science/pith/C5MU3A2KSWFL5VXUVLMCLYJZ5O/action/citation_signature","submit_replication":"https://pith.science/pith/C5MU3A2KSWFL5VXUVLMCLYJZ5O/action/replication_record"}},"created_at":"2026-06-30T02:17:56.027809+00:00","updated_at":"2026-06-30T02:17:56.027809+00:00"}