{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:ZYZJCU5OXM455IHG66OQBXNSKP","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"424ea3a180da8a21a60f487a43c0ac9ecec437d87fd5d733da11357849d8659c","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-17T09:45:24Z","title_canon_sha256":"9f4a253d770d3b3b4a80874b6513cb2bc2ea0bb05ff6d7c90aa7ac7c0de06d46"},"schema_version":"1.0","source":{"id":"2605.17352","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.17352","created_at":"2026-05-20T00:03:53Z"},{"alias_kind":"arxiv_version","alias_value":"2605.17352v1","created_at":"2026-05-20T00:03:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.17352","created_at":"2026-05-20T00:03:53Z"},{"alias_kind":"pith_short_12","alias_value":"ZYZJCU5OXM45","created_at":"2026-05-20T00:03:53Z"},{"alias_kind":"pith_short_16","alias_value":"ZYZJCU5OXM455IHG","created_at":"2026-05-20T00:03:53Z"},{"alias_kind":"pith_short_8","alias_value":"ZYZJCU5O","created_at":"2026-05-20T00:03:53Z"}],"graph_snapshots":[{"event_id":"sha256:b0ef3f63e49c6168a5a00a6117d90ea6c302fa821db9874b41b681ef45a4b10c","target":"graph","created_at":"2026-05-20T00:03:53Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T21:41:57.793107Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T21:33:23.725646Z","status":"skipped","version":"1.0.0"}],"endpoint":"/pith/2605.17352/integrity.json","findings":[],"snapshot_sha256":"aaa5bbe50280e25ebf9f359ece76bad67dbd6e0333ffef295c679be6cb45a8a6","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Despite substantial advances in large language models (LLMs), generating factually consistent responses for knowledge-intensive question answering remains challenging. These difficulties are primarily due to hallucinations and the limitations of LLMs in bridging long-tail knowledge gaps. To address this, we propose AMATA, an Adaptive Multi-Agent Trajectory Alignment framework that dynamically integrates external knowledge to improve response interpretability and factual grounding. Our architecture leverages six specialized agents that collaboratively perform structured actions for complex ques","authors_text":"Chen Chen, Chengyu Wang, Dongyang Li, Jiuheng Wan, Qizhou Chen, Richang Hong, Taolin Zhang, Xiaofeng He","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-17T09:45:24Z","title":"AMATA: Adaptive Multi-Agent Trajectory Alignment for Knowledge-Intensive Question Answering"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.17352","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:3e1ed8810341fd4927d04acdffde206ffbc768737febb07d320b27a2c67e8073","target":"record","created_at":"2026-05-20T00:03:53Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"424ea3a180da8a21a60f487a43c0ac9ecec437d87fd5d733da11357849d8659c","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-05-17T09:45:24Z","title_canon_sha256":"9f4a253d770d3b3b4a80874b6513cb2bc2ea0bb05ff6d7c90aa7ac7c0de06d46"},"schema_version":"1.0","source":{"id":"2605.17352","kind":"arxiv","version":1}},"canonical_sha256":"ce329153aebb39dea0e6f79d00ddb253cbd318f243e406d3e2484111ff1520a3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ce329153aebb39dea0e6f79d00ddb253cbd318f243e406d3e2484111ff1520a3","first_computed_at":"2026-05-20T00:03:53.717702Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:03:53.717702Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"qBQg7OeA1EynwZhIjmWgqPeiW7cc7pIqMDUBD9/RkN+qqNpu43LCZIIhWdIYVKdKi//EZ/JI7xjAvMJmJQ4FDw==","signature_status":"signed_v1","signed_at":"2026-05-20T00:03:53.718586Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.17352","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3e1ed8810341fd4927d04acdffde206ffbc768737febb07d320b27a2c67e8073","sha256:b0ef3f63e49c6168a5a00a6117d90ea6c302fa821db9874b41b681ef45a4b10c"],"state_sha256":"7075ac281b2cbadbad12dcaa0084709c4d710b12187c0bc22cdbbe65819da981"}