{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:OGRCAYOBI6JWWPZGOVZLTFIFK2","short_pith_number":"pith:OGRCAYOB","schema_version":"1.0","canonical_sha256":"71a22061c147936b3f267572b9950556a79b7aec5a814161fb19b9cf1c59616f","source":{"kind":"arxiv","id":"2501.03040","version":2},"attestation_state":"computed","paper":{"title":"ChronoSense: Exploring Temporal Understanding in Large Language Models with Time Intervals of Events","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.LG","authors_text":"Duygu Sezen Islakoglu, Jan-Christoph Kalo","submitted_at":"2025-01-06T14:27:41Z","abstract_excerpt":"Large Language Models (LLMs) have achieved remarkable success in various NLP tasks, yet they still face significant challenges in reasoning and arithmetic. Temporal reasoning, a critical component of natural language understanding, has raised increasing research attention. However, comprehensive testing of Allen's interval relations (e.g., before, after, during) -- a fundamental framework for temporal relationships -- remains underexplored. To fill this gap, we present ChronoSense, a new benchmark for evaluating LLMs' temporal understanding. It includes 16 tasks, focusing on identifying the Al"},"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":"2501.03040","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-01-06T14:27:41Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"79c21209ea10ef9354058b32e45a2a981fe77133a5cb7a071922af3de7209afb","abstract_canon_sha256":"e48307019b2cdbb579535b6bf3b45c462555278f2bbdfc252d93f4a158731178"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:39:46.929550Z","signature_b64":"zJjK5lP4WGgpM7RDpLsu/V4cSnPRieEz9Fwa9BivBs2IE98+0ifmy2BTeEkBNtr3aF8+IDOZ/zMhQnCEe1XhAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"71a22061c147936b3f267572b9950556a79b7aec5a814161fb19b9cf1c59616f","last_reissued_at":"2026-07-05T11:39:46.928826Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:39:46.928826Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ChronoSense: Exploring Temporal Understanding in Large Language Models with Time Intervals of Events","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.LG","authors_text":"Duygu Sezen Islakoglu, Jan-Christoph Kalo","submitted_at":"2025-01-06T14:27:41Z","abstract_excerpt":"Large Language Models (LLMs) have achieved remarkable success in various NLP tasks, yet they still face significant challenges in reasoning and arithmetic. Temporal reasoning, a critical component of natural language understanding, has raised increasing research attention. However, comprehensive testing of Allen's interval relations (e.g., before, after, during) -- a fundamental framework for temporal relationships -- remains underexplored. To fill this gap, we present ChronoSense, a new benchmark for evaluating LLMs' temporal understanding. It includes 16 tasks, focusing on identifying the Al"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.03040","kind":"arxiv","version":2},"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/2501.03040/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":"2501.03040","created_at":"2026-07-05T11:39:46.928908+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.03040v2","created_at":"2026-07-05T11:39:46.928908+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.03040","created_at":"2026-07-05T11:39:46.928908+00:00"},{"alias_kind":"pith_short_12","alias_value":"OGRCAYOBI6JW","created_at":"2026-07-05T11:39:46.928908+00:00"},{"alias_kind":"pith_short_16","alias_value":"OGRCAYOBI6JWWPZG","created_at":"2026-07-05T11:39:46.928908+00:00"},{"alias_kind":"pith_short_8","alias_value":"OGRCAYOB","created_at":"2026-07-05T11:39:46.928908+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.16808","citing_title":"Rethinking LLM-Based RTL Code Optimization Via Timing Logic Metamorphosis","ref_index":32,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/OGRCAYOBI6JWWPZGOVZLTFIFK2","json":"https://pith.science/pith/OGRCAYOBI6JWWPZGOVZLTFIFK2.json","graph_json":"https://pith.science/api/pith-number/OGRCAYOBI6JWWPZGOVZLTFIFK2/graph.json","events_json":"https://pith.science/api/pith-number/OGRCAYOBI6JWWPZGOVZLTFIFK2/events.json","paper":"https://pith.science/paper/OGRCAYOB"},"agent_actions":{"view_html":"https://pith.science/pith/OGRCAYOBI6JWWPZGOVZLTFIFK2","download_json":"https://pith.science/pith/OGRCAYOBI6JWWPZGOVZLTFIFK2.json","view_paper":"https://pith.science/paper/OGRCAYOB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.03040&json=true","fetch_graph":"https://pith.science/api/pith-number/OGRCAYOBI6JWWPZGOVZLTFIFK2/graph.json","fetch_events":"https://pith.science/api/pith-number/OGRCAYOBI6JWWPZGOVZLTFIFK2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OGRCAYOBI6JWWPZGOVZLTFIFK2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OGRCAYOBI6JWWPZGOVZLTFIFK2/action/storage_attestation","attest_author":"https://pith.science/pith/OGRCAYOBI6JWWPZGOVZLTFIFK2/action/author_attestation","sign_citation":"https://pith.science/pith/OGRCAYOBI6JWWPZGOVZLTFIFK2/action/citation_signature","submit_replication":"https://pith.science/pith/OGRCAYOBI6JWWPZGOVZLTFIFK2/action/replication_record"}},"created_at":"2026-07-05T11:39:46.928908+00:00","updated_at":"2026-07-05T11:39:46.928908+00:00"}