{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:6G764Q6CNOG4XEOO2SNL3ZDJWO","short_pith_number":"pith:6G764Q6C","canonical_record":{"source":{"id":"2310.00835","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-02T00:59:07Z","cross_cats_sorted":[],"title_canon_sha256":"771b00439f6f1e399439fdd945f9c00e09953f62e9d664c7192996d5675712ff","abstract_canon_sha256":"3e2d61e18b24a471e1f7db273e8f2520a2e0b9027f1514081347bd9ba1c9fef3"},"schema_version":"1.0"},"canonical_sha256":"f1bfee43c26b8dcb91ced49abde469b3bf5c4c948ceb043399af580f9b06f865","source":{"kind":"arxiv","id":"2310.00835","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.00835","created_at":"2026-07-05T08:25:28Z"},{"alias_kind":"arxiv_version","alias_value":"2310.00835v3","created_at":"2026-07-05T08:25:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.00835","created_at":"2026-07-05T08:25:28Z"},{"alias_kind":"pith_short_12","alias_value":"6G764Q6CNOG4","created_at":"2026-07-05T08:25:28Z"},{"alias_kind":"pith_short_16","alias_value":"6G764Q6CNOG4XEOO","created_at":"2026-07-05T08:25:28Z"},{"alias_kind":"pith_short_8","alias_value":"6G764Q6C","created_at":"2026-07-05T08:25:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:6G764Q6CNOG4XEOO2SNL3ZDJWO","target":"record","payload":{"canonical_record":{"source":{"id":"2310.00835","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-02T00:59:07Z","cross_cats_sorted":[],"title_canon_sha256":"771b00439f6f1e399439fdd945f9c00e09953f62e9d664c7192996d5675712ff","abstract_canon_sha256":"3e2d61e18b24a471e1f7db273e8f2520a2e0b9027f1514081347bd9ba1c9fef3"},"schema_version":"1.0"},"canonical_sha256":"f1bfee43c26b8dcb91ced49abde469b3bf5c4c948ceb043399af580f9b06f865","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:25:28.922356Z","signature_b64":"6Y0s31/R6SG7ptj0fF/L33XHKfQ6USictvlAGJqRqdH6/qHHRMOhxa3gXrDD7rTLV+0gNqCYFAVAIHMzQoETBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f1bfee43c26b8dcb91ced49abde469b3bf5c4c948ceb043399af580f9b06f865","last_reissued_at":"2026-07-05T08:25:28.921915Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:25:28.921915Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2310.00835","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:25:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7lET9u7DjkidhoNoXd+Hb+TkyQMZb+QBPNRRpLqOS2+9vA5/uVaYATVLulGNg7UE9i67F/rNHX7ZHJ85YRjZCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T13:07:02.529094Z"},"content_sha256":"edbf693f3f11466d5a15822e7ca260bc46f8fee9f7791afc4811a6c639d63a20","schema_version":"1.0","event_id":"sha256:edbf693f3f11466d5a15822e7ca260bc46f8fee9f7791afc4811a6c639d63a20"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:6G764Q6CNOG4XEOO2SNL3ZDJWO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"TRAM: Benchmarking Temporal Reasoning for Large Language Models","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Yun Zhao, Yuqing Wang","submitted_at":"2023-10-02T00:59:07Z","abstract_excerpt":"Reasoning about time is essential for understanding the nuances of events described in natural language. Previous research on this topic has been limited in scope, characterized by a lack of standardized benchmarks that would allow for consistent evaluations across different studies. In this paper, we introduce TRAM, a temporal reasoning benchmark composed of ten datasets, encompassing various temporal aspects of events such as order, arithmetic, frequency, and duration, designed to facilitate a comprehensive evaluation of the TeR capabilities of large language models (LLMs). We evaluate popul"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.00835","kind":"arxiv","version":3},"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/2310.00835/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:25:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Fr6DyscNGrwIpEcIgDNGJxv2sO0DJZXwdOPfHQsJabAct+Utsg5CzMbP7gI9rEzoXvqdrKDOig5GYtwT0nrdDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T13:07:02.530106Z"},"content_sha256":"2e4d9d05d9d72554ea40118fe45203df51e61d57ec3cc1f71feb10c5cb84cbae","schema_version":"1.0","event_id":"sha256:2e4d9d05d9d72554ea40118fe45203df51e61d57ec3cc1f71feb10c5cb84cbae"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6G764Q6CNOG4XEOO2SNL3ZDJWO/bundle.json","state_url":"https://pith.science/pith/6G764Q6CNOG4XEOO2SNL3ZDJWO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6G764Q6CNOG4XEOO2SNL3ZDJWO/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-08T13:07:02Z","links":{"resolver":"https://pith.science/pith/6G764Q6CNOG4XEOO2SNL3ZDJWO","bundle":"https://pith.science/pith/6G764Q6CNOG4XEOO2SNL3ZDJWO/bundle.json","state":"https://pith.science/pith/6G764Q6CNOG4XEOO2SNL3ZDJWO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6G764Q6CNOG4XEOO2SNL3ZDJWO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:6G764Q6CNOG4XEOO2SNL3ZDJWO","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":"3e2d61e18b24a471e1f7db273e8f2520a2e0b9027f1514081347bd9ba1c9fef3","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-02T00:59:07Z","title_canon_sha256":"771b00439f6f1e399439fdd945f9c00e09953f62e9d664c7192996d5675712ff"},"schema_version":"1.0","source":{"id":"2310.00835","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.00835","created_at":"2026-07-05T08:25:28Z"},{"alias_kind":"arxiv_version","alias_value":"2310.00835v3","created_at":"2026-07-05T08:25:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.00835","created_at":"2026-07-05T08:25:28Z"},{"alias_kind":"pith_short_12","alias_value":"6G764Q6CNOG4","created_at":"2026-07-05T08:25:28Z"},{"alias_kind":"pith_short_16","alias_value":"6G764Q6CNOG4XEOO","created_at":"2026-07-05T08:25:28Z"},{"alias_kind":"pith_short_8","alias_value":"6G764Q6C","created_at":"2026-07-05T08:25:28Z"}],"graph_snapshots":[{"event_id":"sha256:2e4d9d05d9d72554ea40118fe45203df51e61d57ec3cc1f71feb10c5cb84cbae","target":"graph","created_at":"2026-07-05T08:25:28Z","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/2310.00835/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Reasoning about time is essential for understanding the nuances of events described in natural language. Previous research on this topic has been limited in scope, characterized by a lack of standardized benchmarks that would allow for consistent evaluations across different studies. In this paper, we introduce TRAM, a temporal reasoning benchmark composed of ten datasets, encompassing various temporal aspects of events such as order, arithmetic, frequency, and duration, designed to facilitate a comprehensive evaluation of the TeR capabilities of large language models (LLMs). We evaluate popul","authors_text":"Yun Zhao, Yuqing Wang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-02T00:59:07Z","title":"TRAM: Benchmarking Temporal Reasoning for Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.00835","kind":"arxiv","version":3},"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:edbf693f3f11466d5a15822e7ca260bc46f8fee9f7791afc4811a6c639d63a20","target":"record","created_at":"2026-07-05T08:25:28Z","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":"3e2d61e18b24a471e1f7db273e8f2520a2e0b9027f1514081347bd9ba1c9fef3","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-02T00:59:07Z","title_canon_sha256":"771b00439f6f1e399439fdd945f9c00e09953f62e9d664c7192996d5675712ff"},"schema_version":"1.0","source":{"id":"2310.00835","kind":"arxiv","version":3}},"canonical_sha256":"f1bfee43c26b8dcb91ced49abde469b3bf5c4c948ceb043399af580f9b06f865","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f1bfee43c26b8dcb91ced49abde469b3bf5c4c948ceb043399af580f9b06f865","first_computed_at":"2026-07-05T08:25:28.921915Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:25:28.921915Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"6Y0s31/R6SG7ptj0fF/L33XHKfQ6USictvlAGJqRqdH6/qHHRMOhxa3gXrDD7rTLV+0gNqCYFAVAIHMzQoETBw==","signature_status":"signed_v1","signed_at":"2026-07-05T08:25:28.922356Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.00835","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:edbf693f3f11466d5a15822e7ca260bc46f8fee9f7791afc4811a6c639d63a20","sha256:2e4d9d05d9d72554ea40118fe45203df51e61d57ec3cc1f71feb10c5cb84cbae"],"state_sha256":"773e82cfbee4478724c298d3ac042d231d9b2e81b2bd0ee9783d2552def6f155"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"M0YFzT3xpy3Jwfr0CCkxgc01ySTQPjYYm2dWRBuEewve/So9Iz1T91WTmNCW7FwhWp9ywtBmRqcsq1SyxemxBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T13:07:02.555173Z","bundle_sha256":"e7d51b04708b569bed8aee6df6cfd7746be69b37f467788642a10f1f5b61812b"}}