{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:2F25WHBAQL7D4S4CHZVFLRJZGP","short_pith_number":"pith:2F25WHBA","schema_version":"1.0","canonical_sha256":"d175db1c2082fe3e4b823e6a55c53933e72af7955b29d75c4c5ffaaf9d9cd03d","source":{"kind":"arxiv","id":"2607.28638","version":1},"attestation_state":"computed","paper":{"title":"Learning Stateful Predictive Knowledge From Experience","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Bo Liu, Cheng Deng, Haotian Fu, Jian Zhao, Jun Wang, Mengyue Yang, Xidong Feng, Xinyu Cui, Yan Song, Zichen Liu","submitted_at":"2026-05-19T17:09:32Z","abstract_excerpt":"As large language model (LLM) agents increasingly learn from experience, they primarily rely on trajectory-level reflection to extract insights. Viewed through the lens of predictive knowledge, we argue that this approach operates on episodic hindsight rather than predictive foresight, yielding brittle, path-dependent heuristics. To address this, we propose Stateful Knowledge Learning (SKL). SKL shifts the agent's focus from trajectory-level summarization to maintaining Stateful Knowledge: explicit, declarative predictive assessments anchored to state. We first demonstrate a motivating example"},"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":"2607.28638","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2026-05-19T17:09:32Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"b298e280ebaabdb10416d4dfce087b59dbd286321670d18acc3a00ac8b04212b","abstract_canon_sha256":"236b48622c6ce13a7a2098d417ff53d1b0736ead1039ff3395498b8dbc8a76e5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-03T00:11:52.535167Z","signature_b64":"7QUvIXEeMFe6cobn6laPX8ZAY6VVIAwh6YmHXEaNV0ogodO51hL1u7SHZ1nZ2mpiG6qggw+jtO8S5as4Ld3VAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d175db1c2082fe3e4b823e6a55c53933e72af7955b29d75c4c5ffaaf9d9cd03d","last_reissued_at":"2026-08-03T00:11:52.532882Z","signature_status":"signed_v1","first_computed_at":"2026-08-03T00:11:52.532882Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Stateful Predictive Knowledge From Experience","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Bo Liu, Cheng Deng, Haotian Fu, Jian Zhao, Jun Wang, Mengyue Yang, Xidong Feng, Xinyu Cui, Yan Song, Zichen Liu","submitted_at":"2026-05-19T17:09:32Z","abstract_excerpt":"As large language model (LLM) agents increasingly learn from experience, they primarily rely on trajectory-level reflection to extract insights. Viewed through the lens of predictive knowledge, we argue that this approach operates on episodic hindsight rather than predictive foresight, yielding brittle, path-dependent heuristics. To address this, we propose Stateful Knowledge Learning (SKL). SKL shifts the agent's focus from trajectory-level summarization to maintaining Stateful Knowledge: explicit, declarative predictive assessments anchored to state. We first demonstrate a motivating example"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.28638","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/2607.28638/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":"2607.28638","created_at":"2026-08-03T00:11:52.534348+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.28638v1","created_at":"2026-08-03T00:11:52.534348+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.28638","created_at":"2026-08-03T00:11:52.534348+00:00"},{"alias_kind":"pith_short_12","alias_value":"2F25WHBAQL7D","created_at":"2026-08-03T00:11:52.534348+00:00"},{"alias_kind":"pith_short_16","alias_value":"2F25WHBAQL7D4S4C","created_at":"2026-08-03T00:11:52.534348+00:00"},{"alias_kind":"pith_short_8","alias_value":"2F25WHBA","created_at":"2026-08-03T00:11:52.534348+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/2F25WHBAQL7D4S4CHZVFLRJZGP","json":"https://pith.science/pith/2F25WHBAQL7D4S4CHZVFLRJZGP.json","graph_json":"https://pith.science/api/pith-number/2F25WHBAQL7D4S4CHZVFLRJZGP/graph.json","events_json":"https://pith.science/api/pith-number/2F25WHBAQL7D4S4CHZVFLRJZGP/events.json","paper":"https://pith.science/paper/2F25WHBA"},"agent_actions":{"view_html":"https://pith.science/pith/2F25WHBAQL7D4S4CHZVFLRJZGP","download_json":"https://pith.science/pith/2F25WHBAQL7D4S4CHZVFLRJZGP.json","view_paper":"https://pith.science/paper/2F25WHBA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.28638&json=true","fetch_graph":"https://pith.science/api/pith-number/2F25WHBAQL7D4S4CHZVFLRJZGP/graph.json","fetch_events":"https://pith.science/api/pith-number/2F25WHBAQL7D4S4CHZVFLRJZGP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2F25WHBAQL7D4S4CHZVFLRJZGP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2F25WHBAQL7D4S4CHZVFLRJZGP/action/storage_attestation","attest_author":"https://pith.science/pith/2F25WHBAQL7D4S4CHZVFLRJZGP/action/author_attestation","sign_citation":"https://pith.science/pith/2F25WHBAQL7D4S4CHZVFLRJZGP/action/citation_signature","submit_replication":"https://pith.science/pith/2F25WHBAQL7D4S4CHZVFLRJZGP/action/replication_record"}},"created_at":"2026-08-03T00:11:52.534348+00:00","updated_at":"2026-08-03T00:11:52.534348+00:00"}