{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:4WXRIHBUWLTQK4IPUZDWDIC3MV","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":"96ea7930755e25495aa03aa36165812b168acfb537f575c8bbbacb3cc39e4628","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-27T03:29:49Z","title_canon_sha256":"8f93ac9f44acd2df10259ad7800b426900b813708ed95c53ce45ac42f259fce9"},"schema_version":"1.0","source":{"id":"2606.28708","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.28708","created_at":"2026-06-30T01:16:48Z"},{"alias_kind":"arxiv_version","alias_value":"2606.28708v1","created_at":"2026-06-30T01:16:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.28708","created_at":"2026-06-30T01:16:48Z"},{"alias_kind":"pith_short_12","alias_value":"4WXRIHBUWLTQ","created_at":"2026-06-30T01:16:48Z"},{"alias_kind":"pith_short_16","alias_value":"4WXRIHBUWLTQK4IP","created_at":"2026-06-30T01:16:48Z"},{"alias_kind":"pith_short_8","alias_value":"4WXRIHBU","created_at":"2026-06-30T01:16:48Z"}],"graph_snapshots":[{"event_id":"sha256:00d6d847b889d4ce088e829f095d29bbda2225a8ac2f83970e03ebeaf65d721c","target":"graph","created_at":"2026-06-30T01:16:48Z","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":[],"endpoint":"/pith/2606.28708/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Accurately explaining hidden patterns in multi-aspect data has typically been done by leveraging labels and/or accompanying auxiliary metadata. However, labels and auxiliary data may be inaccurate (e.g. nonstandard, inconsistent), insufficient (e.g. static tabular metadata for time-dependent recordings), or unavailable. %\nWe propose \\fullmethod (\\method), which leverages the knowledge of large language models (LLMs) to explain the hidden patterns in human narratives. \\method uses task-agnostic and task-specific prompts to explain extracted co-clustered latent patterns from tensor decomposition","authors_text":"Auder Der, Dawon Ahn, Evangelos E. Papalexakis","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-27T03:29:49Z","title":"AnTenA: Actionable and Explainable Tensor Analysis System with Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.28708","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":"sha256:4172c1db352c3b164988cb24fd4a3e7845c53c16ef064ed6012580dfca05db42","target":"record","created_at":"2026-06-30T01:16:48Z","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":"96ea7930755e25495aa03aa36165812b168acfb537f575c8bbbacb3cc39e4628","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-27T03:29:49Z","title_canon_sha256":"8f93ac9f44acd2df10259ad7800b426900b813708ed95c53ce45ac42f259fce9"},"schema_version":"1.0","source":{"id":"2606.28708","kind":"arxiv","version":1}},"canonical_sha256":"e5af141c34b2e705710fa64761a05b65710149ba5562c302fe62857691653264","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e5af141c34b2e705710fa64761a05b65710149ba5562c302fe62857691653264","first_computed_at":"2026-06-30T01:16:48.770844Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-30T01:16:48.770844Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"w4jTYDKaI6qOdO4umt6hf76MYMAiQsJF5zmxCGMv2PCdGSoHrACDH3xrK0gOuQ+t4wUVMkKDa+ddMaGkn/lSDA==","signature_status":"signed_v1","signed_at":"2026-06-30T01:16:48.771265Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.28708","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4172c1db352c3b164988cb24fd4a3e7845c53c16ef064ed6012580dfca05db42","sha256:00d6d847b889d4ce088e829f095d29bbda2225a8ac2f83970e03ebeaf65d721c"],"state_sha256":"1bbd1ebf661b7abeda5040992568edff16d0dacc9c3b773d68bc1b658f0084e0"}