{"as_of":"2026-08-11T07:44:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:87f19526e7a42c810a1a08e0e1d673a9799cb9e17a3c2e505777047da5ab6fc5","coverage":[{"denominator":36,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":36,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T14:51:28.659622Z","state":"measured"},{"denominator":36,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":36,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2508.20828/citation-record","integrity":"/paper/2508.20828/integrity","json":"/paper/2508.20828/citation-record.json","paper":"/paper/2508.20828"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:51:28.321738Z","title":"online\" 'onlinestring :=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.20828","last_updated":"2025-08-28T14:23:39Z","snapshot_observed_at":"2026-08-10T04:05:47.588066Z","submitted_at":"2025-08-28T14:23:39Z","title":"GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-05T14:51:28.321738Z"},"links":{"citing_paper":"/paper/2508.20828"},"observation_digest":"sha256:3f639e4141ff141b3b576c661c55a1935a6a3342f3b3df56b1d77a9def90dce0","observation_id":"65fb7e73-971b-4f66-8404-04932e4034d1","resolution":{"observed_at":"2026-08-05T14:51:28.321738Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:51:28.331044Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.20828","last_updated":"2025-08-28T14:23:39Z","snapshot_observed_at":"2026-08-10T04:05:47.588066Z","submitted_at":"2025-08-28T14:23:39Z","title":"GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-05T14:51:28.331044Z"},"links":{"citing_paper":"/paper/2508.20828"},"observation_digest":"sha256:6040221bd8d7801a9eb6fb31921dc7fc8ca919873ab6ae9608fe054d17a314d2","observation_id":"c2da5ea8-2d1a-4daf-b6d4-b959977cb7d9","resolution":{"observed_at":"2026-08-05T14:51:28.331044Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:51:29.886391Z","title":null,"venue":null,"work_id":"8f07b357-7304-4b1b-b789-4589aa54f144","year":2020},"citing_paper":{"arxiv_id":"2508.20828","last_updated":"2025-08-28T14:23:39Z","snapshot_observed_at":"2026-08-10T04:05:47.588066Z","submitted_at":"2025-08-28T14:23:39Z","title":"GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-05T14:51:28.338417Z"},"links":{"citing_paper":"/paper/2508.20828"},"observation_digest":"sha256:a612dde1c5e9fdec0380911726287e3172998d464401e9a2d9a322a40cde14d2","observation_id":"bf7dd6a2-7426-4dd0-80f0-da45045183d0","resolution":{"observed_at":"2026-08-05T14:51:29.894717Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:51:29.851017Z","title":null,"venue":null,"work_id":"1591f19c-5e21-40f6-93bd-4ee1f55fb9c6","year":2014},"citing_paper":{"arxiv_id":"2508.20828","last_updated":"2025-08-28T14:23:39Z","snapshot_observed_at":"2026-08-10T04:05:47.588066Z","submitted_at":"2025-08-28T14:23:39Z","title":"GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-05T14:51:28.351838Z"},"links":{"citing_paper":"/paper/2508.20828"},"observation_digest":"sha256:f8ba74c0dcfb3495090579875a9e2686925232fab8564aa2f8dbaf82bda13d92","observation_id":"d611ce71-7dba-4b4e-9568-81a57f47ec51","resolution":{"observed_at":"2026-08-05T14:51:29.859890Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.21187","last_updated":"2025-02-01T07:57:37Z","snapshot_observed_at":"2026-08-01T16:43:44.704797Z","submitted_at":"2024-12-30T18:55:12Z","title":"Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.21187","snapshot_observed_at":"2026-08-05T14:51:28.358865Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.20828","last_updated":"2025-08-28T14:23:39Z","snapshot_observed_at":"2026-08-10T04:05:47.588066Z","submitted_at":"2025-08-28T14:23:39Z","title":"GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-05T14:51:28.358865Z"},"links":{"cited_paper":"/paper/2412.21187","citing_paper":"/paper/2508.20828"},"observation_digest":"sha256:7164113eff879102a3adb3c374996ce53193ca01abf860487dccbf3969d5f7fb","observation_id":"91158fbd-ea7e-4f8c-9730-e9202363e0d1","resolution":{"observed_at":"2026-08-05T14:51:28.358865Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:51:29.810353Z","title":null,"venue":null,"work_id":"b8d71c1c-c828-4dcc-ad8d-18a6a4e0cfbd","year":2020},"citing_paper":{"arxiv_id":"2508.20828","last_updated":"2025-08-28T14:23:39Z","snapshot_observed_at":"2026-08-10T04:05:47.588066Z","submitted_at":"2025-08-28T14:23:39Z","title":"GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-05T14:51:28.369955Z"},"links":{"citing_paper":"/paper/2508.20828"},"observation_digest":"sha256:3e42e5666a5b53d19f27cfa5c2d6ec7304baf6828c8cd014d651bf30833fa872","observation_id":"f5463ca4-1410-448d-b1b4-393f9bd3bec0","resolution":{"observed_at":"2026-08-05T14:51:29.827332Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.06020","last_updated":"2025-02-09T20:26:30Z","snapshot_observed_at":"2026-08-10T04:05:08.157444Z","submitted_at":"2025-02-09T20:26:30Z","title":"Temporal Working Memory: Query-Guided Segment Refinement for Enhanced Multimodal Understanding","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.06020","snapshot_observed_at":"2026-08-05T14:51:28.377811Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.20828","last_updated":"2025-08-28T14:23:39Z","snapshot_observed_at":"2026-08-10T04:05:47.588066Z","submitted_at":"2025-08-28T14:23:39Z","title":"GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-05T14:51:28.377811Z"},"links":{"cited_paper":"/paper/2502.06020","citing_paper":"/paper/2508.20828"},"observation_digest":"sha256:d248339b9e4defa95a6223700da3eb7a23f635bab221204a3fd7c92f2c6419a7","observation_id":"b8fddfc9-11a4-477f-9b31-f006d6e697db","resolution":{"observed_at":"2026-08-05T14:51:28.377811Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:51:29.756219Z","title":null,"venue":null,"work_id":"d99327b7-8d79-4a0f-94ef-1eb3b47b820b","year":2021},"citing_paper":{"arxiv_id":"2508.20828","last_updated":"2025-08-28T14:23:39Z","snapshot_observed_at":"2026-08-10T04:05:47.588066Z","submitted_at":"2025-08-28T14:23:39Z","title":"GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-05T14:51:28.384091Z"},"links":{"citing_paper":"/paper/2508.20828"},"observation_digest":"sha256:21a217c3afc4745e9a831cb9ca203effa94006c9603e12eeaf241293d298df21","observation_id":"ff921cd6-314f-4e15-ac3e-8695920d913b","resolution":{"observed_at":"2026-08-05T14:51:29.768243Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:51:29.730620Z","title":null,"venue":null,"work_id":"aa4cb3d7-b10d-4dbb-b7f3-bc2e7ca0484f","year":2019},"citing_paper":{"arxiv_id":"2508.20828","last_updated":"2025-08-28T14:23:39Z","snapshot_observed_at":"2026-08-10T04:05:47.588066Z","submitted_at":"2025-08-28T14:23:39Z","title":"GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-05T14:51:28.392056Z"},"links":{"citing_paper":"/paper/2508.20828"},"observation_digest":"sha256:e6b2245fc7da157117fd754381168643c860d32de19fa6a9ea9ca5b87f184ef9","observation_id":"84190cea-5624-4446-8c76-6e5f99f8264e","resolution":{"observed_at":"2026-08-05T14:51:29.737707Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:51:29.702410Z","title":null,"venue":null,"work_id":"50003ecb-ef77-42ab-9916-b5935ab3e68a","year":2020},"citing_paper":{"arxiv_id":"2508.20828","last_updated":"2025-08-28T14:23:39Z","snapshot_observed_at":"2026-08-10T04:05:47.588066Z","submitted_at":"2025-08-28T14:23:39Z","title":"GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-05T14:51:28.399407Z"},"links":{"citing_paper":"/paper/2508.20828"},"observation_digest":"sha256:96cb71f94aa20df27f0c132dd4c52b8325121be4ce8f61996df19fe8cb195cdc","observation_id":"dbb7262f-bef7-4408-bf9f-a91a28be0628","resolution":{"observed_at":"2026-08-05T14:51:29.708512Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:51:28.413793Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.20828","last_updated":"2025-08-28T14:23:39Z","snapshot_observed_at":"2026-08-10T04:05:47.588066Z","submitted_at":"2025-08-28T14:23:39Z","title":"GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-05T14:51:28.413793Z"},"links":{"citing_paper":"/paper/2508.20828"},"observation_digest":"sha256:2f97dfeef96a473aa676bb1b1d05aa9161133452fda1f138999f5bcaf1a0c416","observation_id":"ef902aec-635e-434a-929a-76d689994ba0","resolution":{"observed_at":"2026-08-05T14:51:28.413793Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:51:29.638649Z","title":null,"venue":null,"work_id":"50d4e4ac-2d12-4d29-8ccc-0d5b3de85178","year":2025},"citing_paper":{"arxiv_id":"2508.20828","last_updated":"2025-08-28T14:23:39Z","snapshot_observed_at":"2026-08-10T04:05:47.588066Z","submitted_at":"2025-08-28T14:23:39Z","title":"GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-05T14:51:28.428063Z"},"links":{"citing_paper":"/paper/2508.20828"},"observation_digest":"sha256:a641ba1dcfeb5012a60403fa1dbac09f545d9de2b90ed6499b9a5912fcc620b8","observation_id":"b8fb53fc-4274-415d-a8d3-adb9d1d483c7","resolution":{"observed_at":"2026-08-05T14:51:29.646579Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:51:29.594661Z","title":null,"venue":null,"work_id":"28396070-c221-4fe8-95c6-c9d9e993cb45","year":2023},"citing_paper":{"arxiv_id":"2508.20828","last_updated":"2025-08-28T14:23:39Z","snapshot_observed_at":"2026-08-10T04:05:47.588066Z","submitted_at":"2025-08-28T14:23:39Z","title":"GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-05T14:51:28.435998Z"},"links":{"citing_paper":"/paper/2508.20828"},"observation_digest":"sha256:9b6dbdc3724388da3f3686e1b197b2e568c8a136c3b0157c0f1914928cda3125","observation_id":"8c45af3a-1b36-4e2b-a06d-d414f0980ff2","resolution":{"observed_at":"2026-08-05T14:51:29.616905Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.21276","last_updated":"2024-10-25T17:43:01Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-10-25T17:43:01Z","title":"GPT-4o System Card","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.21276","snapshot_observed_at":"2026-08-05T14:51:28.441567Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.20828","last_updated":"2025-08-28T14:23:39Z","snapshot_observed_at":"2026-08-10T04:05:47.588066Z","submitted_at":"2025-08-28T14:23:39Z","title":"GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-05T14:51:28.441567Z"},"links":{"cited_paper":"/paper/2410.21276","citing_paper":"/paper/2508.20828"},"observation_digest":"sha256:0d1c33bd7451d08a448b210ee72664068727adabe08d4029ea0a92845b9625f2","observation_id":"5bdfa1ad-0058-44de-949a-2d0618006fdc","resolution":{"observed_at":"2026-08-05T14:51:28.441567Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:51:29.558007Z","title":null,"venue":null,"work_id":"9f62db6a-d701-4e0f-bd50-a60de2f37166","year":2020},"citing_paper":{"arxiv_id":"2508.20828","last_updated":"2025-08-28T14:23:39Z","snapshot_observed_at":"2026-08-10T04:05:47.588066Z","submitted_at":"2025-08-28T14:23:39Z","title":"GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-05T14:51:28.447161Z"},"links":{"citing_paper":"/paper/2508.20828"},"observation_digest":"sha256:0a2986796747dde375801b629175fdcebe82b5c98fcef1c8cc29dfee17b78e9a","observation_id":"81aa740f-cffa-434f-9b62-6959b110d330","resolution":{"observed_at":"2026-08-05T14:51:29.576641Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1907.11692","last_updated":"2019-07-26T17:48:29Z","snapshot_observed_at":"2026-07-31T22:31:37.910868Z","submitted_at":"2019-07-26T17:48:29Z","title":"RoBERTa: A Robustly Optimized BERT Pretraining Approach","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.11692","snapshot_observed_at":"2026-08-05T14:51:28.454268Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2508.20828","last_updated":"2025-08-28T14:23:39Z","snapshot_observed_at":"2026-08-10T04:05:47.588066Z","submitted_at":"2025-08-28T14:23:39Z","title":"GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-05T14:51:28.454268Z"},"links":{"cited_paper":"/paper/1907.11692","citing_paper":"/paper/2508.20828"},"observation_digest":"sha256:2132116e81bb7a59af902aa658b495ebf99df7009387890e00e281193cbd633e","observation_id":"68ef4050-fd0e-4234-8ce1-9a6793f49300","resolution":{"observed_at":"2026-08-05T14:51:28.454268Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:51:29.533247Z","title":null,"venue":null,"work_id":"7cfca86f-5cf8-4452-a6d1-d906cd615da4","year":2022},"citing_paper":{"arxiv_id":"2508.20828","last_updated":"2025-08-28T14:23:39Z","snapshot_observed_at":"2026-08-10T04:05:47.588066Z","submitted_at":"2025-08-28T14:23:39Z","title":"GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-05T14:51:28.462763Z"},"links":{"citing_paper":"/paper/2508.20828"},"observation_digest":"sha256:4b97258ec30858a7a8f4807d4dd276776cfece573f0995b89e3802c4962bce36","observation_id":"41c6d73f-b760-4633-932b-92bf6525663b","resolution":{"observed_at":"2026-08-05T14:51:29.546231Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:51:29.504278Z","title":null,"venue":null,"work_id":"03143e6d-ec05-4b2c-80fb-bb0102747e18","year":2006},"citing_paper":{"arxiv_id":"2508.20828","last_updated":"2025-08-28T14:23:39Z","snapshot_observed_at":"2026-08-10T04:05:47.588066Z","submitted_at":"2025-08-28T14:23:39Z","title":"GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-05T14:51:28.471310Z"},"links":{"citing_paper":"/paper/2508.20828"},"observation_digest":"sha256:533b26a904d64c4f8f5ed86eae8ddfc057fef4529779660b2547175855e6b8e7","observation_id":"d9a70b33-9b56-4711-8acc-d380dad2fcd6","resolution":{"observed_at":"2026-08-05T14:51:29.515112Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:51:29.467522Z","title":null,"venue":null,"work_id":"480b258a-b17a-44c1-b066-70898bf6825d","year":2021},"citing_paper":{"arxiv_id":"2508.20828","last_updated":"2025-08-28T14:23:39Z","snapshot_observed_at":"2026-08-10T04:05:47.588066Z","submitted_at":"2025-08-28T14:23:39Z","title":"GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-05T14:51:28.487440Z"},"links":{"citing_paper":"/paper/2508.20828"},"observation_digest":"sha256:d6b41ac1052a0c7d2b747ceafe84229a2d0eadc7f4b1296ab69134626d7624f5","observation_id":"f09b632f-7a9b-4ef0-8818-1d1d020a7438","resolution":{"observed_at":"2026-08-05T14:51:29.479314Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:51:29.441070Z","title":null,"venue":null,"work_id":"c872887e-3546-4598-97e8-4e65e2efe9a1","year":2019},"citing_paper":{"arxiv_id":"2508.20828","last_updated":"2025-08-28T14:23:39Z","snapshot_observed_at":"2026-08-10T04:05:47.588066Z","submitted_at":"2025-08-28T14:23:39Z","title":"GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-05T14:51:28.500002Z"},"links":{"citing_paper":"/paper/2508.20828"},"observation_digest":"sha256:58199b6e45dd6e6087e4c1fc976467ad7aaa7e425f308a1854440a8dd4367aea","observation_id":"fdc4162e-2f3b-42d0-b27d-6d91ddecd190","resolution":{"observed_at":"2026-08-05T14:51:29.447563Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:51:29.416123Z","title":null,"venue":null,"work_id":"ff3e978b-ae5c-4685-8e26-d65b611ceb7c","year":2024},"citing_paper":{"arxiv_id":"2508.20828","last_updated":"2025-08-28T14:23:39Z","snapshot_observed_at":"2026-08-10T04:05:47.588066Z","submitted_at":"2025-08-28T14:23:39Z","title":"GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-05T14:51:28.507512Z"},"links":{"citing_paper":"/paper/2508.20828"},"observation_digest":"sha256:3c1b082d2b3bec24461a7161a2be8d67ad35eb3bce5a6f4b4480cda1c36ea8ae","observation_id":"d8f70d20-10f5-4120-b182-62b4c43d14c2","resolution":{"observed_at":"2026-08-05T14:51:29.423688Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:51:28.515555Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.20828","last_updated":"2025-08-28T14:23:39Z","snapshot_observed_at":"2026-08-10T04:05:47.588066Z","submitted_at":"2025-08-28T14:23:39Z","title":"GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-05T14:51:28.515555Z"},"links":{"citing_paper":"/paper/2508.20828"},"observation_digest":"sha256:b46f3461c7ddab8d6eae7fa9290eaf1a02d00cec90a6c31dc8dde01eb253956c","observation_id":"d50750ed-4a91-4b5e-8963-79aa2633841c","resolution":{"observed_at":"2026-08-05T14:51:28.515555Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.3233/faia240748","metadata_source":"openalex","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T05:30:23.456663Z","title":null,"venue":"Frontiers in artificial intelligence and applications","work_id":"bf3a299a-675c-4b5a-9e88-f01c13f18055","year":2024},"citing_paper":{"arxiv_id":"2508.20828","last_updated":"2025-08-28T14:23:39Z","snapshot_observed_at":"2026-08-10T04:05:47.588066Z","submitted_at":"2025-08-28T14:23:39Z","title":"GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-05T14:51:28.524669Z"},"links":{"citing_paper":"/paper/2508.20828"},"observation_digest":"sha256:1037e5c9b5c6957e04c1c756ba3216f55a06ea09538f31441370386cacf555f1","observation_id":"b64ddf05-72ba-4f86-b399-187da5c5b800","resolution":{"observed_at":"2026-08-05T14:51:28.750077Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:51:29.383457Z","title":null,"venue":null,"work_id":"6073c72d-38c9-4fd1-839e-abd44c1ac493","year":2021},"citing_paper":{"arxiv_id":"2508.20828","last_updated":"2025-08-28T14:23:39Z","snapshot_observed_at":"2026-08-10T04:05:47.588066Z","submitted_at":"2025-08-28T14:23:39Z","title":"GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-05T14:51:28.532159Z"},"links":{"citing_paper":"/paper/2508.20828"},"observation_digest":"sha256:f3837a1b984b4f53ed537b4d99ea260cd51cb89e9398f1e72d5aeffdafeb6c9f","observation_id":"f7969684-edea-4ff5-ab47-bf6b34b4b2ef","resolution":{"observed_at":"2026-08-05T14:51:29.389974Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:51:29.355970Z","title":null,"venue":null,"work_id":"60352096-b35d-4b86-bfaa-3cb6e64df605","year":2023},"citing_paper":{"arxiv_id":"2508.20828","last_updated":"2025-08-28T14:23:39Z","snapshot_observed_at":"2026-08-10T04:05:47.588066Z","submitted_at":"2025-08-28T14:23:39Z","title":"GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-05T14:51:28.541859Z"},"links":{"citing_paper":"/paper/2508.20828"},"observation_digest":"sha256:4a294fd4e96db2d6d5ef2caa40b0d8ddedc9ea68cbaf7cd525896397ed612952","observation_id":"cd980495-a775-4aa3-8c1b-8b63dd6b74a8","resolution":{"observed_at":"2026-08-05T14:51:29.365522Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:51:29.328070Z","title":null,"venue":null,"work_id":"28ccf642-6dd4-480b-980e-0d1f33f2c17a","year":2013},"citing_paper":{"arxiv_id":"2508.20828","last_updated":"2025-08-28T14:23:39Z","snapshot_observed_at":"2026-08-10T04:05:47.588066Z","submitted_at":"2025-08-28T14:23:39Z","title":"GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-05T14:51:28.548397Z"},"links":{"citing_paper":"/paper/2508.20828"},"observation_digest":"sha256:d13bdfacac2393cc6fb35bc25f8c538e67db923a296fcbffb5f181957f6813ca","observation_id":"99826d01-beaa-4b98-8932-19a2ac305be2","resolution":{"observed_at":"2026-08-05T14:51:29.340639Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:51:29.285464Z","title":null,"venue":null,"work_id":"aaa4f16b-d195-4f19-8525-86fd54ce3778","year":2020},"citing_paper":{"arxiv_id":"2508.20828","last_updated":"2025-08-28T14:23:39Z","snapshot_observed_at":"2026-08-10T04:05:47.588066Z","submitted_at":"2025-08-28T14:23:39Z","title":"GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-05T14:51:28.557079Z"},"links":{"citing_paper":"/paper/2508.20828"},"observation_digest":"sha256:7d20842ec139fbc445797353842535d31c36f3b034a0ab1d89f4539526f63329","observation_id":"a11db480-c1c5-428c-a814-d16088ce7a8a","resolution":{"observed_at":"2026-08-05T14:51:29.293288Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:51:29.256613Z","title":null,"venue":null,"work_id":"23cb338d-e0fd-4139-8286-4cd689da3a36","year":2021},"citing_paper":{"arxiv_id":"2508.20828","last_updated":"2025-08-28T14:23:39Z","snapshot_observed_at":"2026-08-10T04:05:47.588066Z","submitted_at":"2025-08-28T14:23:39Z","title":"GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-05T14:51:28.563343Z"},"links":{"citing_paper":"/paper/2508.20828"},"observation_digest":"sha256:31983bb09747193665ec39cd15af3e6d597319d180ca502a0351163cf3c757a5","observation_id":"7bb8cc7f-75fc-4a6e-bdec-fafb692da71e","resolution":{"observed_at":"2026-08-05T14:51:29.262889Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2502.03954","last_updated":"2025-02-06T10:46:19Z","snapshot_observed_at":"2026-08-09T00:04:30.755175Z","submitted_at":"2025-02-06T10:46:19Z","title":"MAQInstruct: Instruction-based Unified Event Relation Extraction","version":1},"cited_work":{"arxiv_id":"2502.03954","doi":null,"metadata_source":"pith","pith_arxiv_id":"2502.03954","snapshot_observed_at":"2026-08-05T14:51:28.812943Z","title":"MAQInstruct: Instruction-based Unified Event Relation Extraction","venue":"cs.CL","work_id":"810e80ab-88fb-4038-836d-64fb09068bdb","year":2025},"citing_paper":{"arxiv_id":"2508.20828","last_updated":"2025-08-28T14:23:39Z","snapshot_observed_at":"2026-08-10T04:05:47.588066Z","submitted_at":"2025-08-28T14:23:39Z","title":"GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-05T14:51:28.577895Z"},"links":{"cited_paper":"/paper/2502.03954","citing_paper":"/paper/2508.20828"},"observation_digest":"sha256:d281419946a958202583092349c58c5de302203cb7afe19e17a5d563a2afbc10","observation_id":"289363e4-3d7c-45fe-8f24-0275ea9674bc","resolution":{"observed_at":"2026-08-05T14:51:28.821913Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:51:29.211691Z","title":null,"venue":null,"work_id":"65a51a36-38cf-477d-b55e-d9db26979998","year":2024},"citing_paper":{"arxiv_id":"2508.20828","last_updated":"2025-08-28T14:23:39Z","snapshot_observed_at":"2026-08-10T04:05:47.588066Z","submitted_at":"2025-08-28T14:23:39Z","title":"GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-05T14:51:28.587746Z"},"links":{"citing_paper":"/paper/2508.20828"},"observation_digest":"sha256:c11ea5bf831e10866b31b5e94399bed427318c2deed8d21b4a37631b873d67ee","observation_id":"d7c7b205-eec0-44c8-b580-9e0ed6059034","resolution":{"observed_at":"2026-08-05T14:51:29.218745Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:51:29.163254Z","title":null,"venue":null,"work_id":"f2533301-270a-4a5e-9e70-13bdc8bf090e","year":2009},"citing_paper":{"arxiv_id":"2508.20828","last_updated":"2025-08-28T14:23:39Z","snapshot_observed_at":"2026-08-10T04:05:47.588066Z","submitted_at":"2025-08-28T14:23:39Z","title":"GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-05T14:51:28.596435Z"},"links":{"citing_paper":"/paper/2508.20828"},"observation_digest":"sha256:77588074b5e55ea1cafba2ca785fe6a35a701e44f5bd1a251cc5264cf293bf61","observation_id":"e89866c3-c3ce-4e1e-ac31-b9cbac2726fa","resolution":{"observed_at":"2026-08-05T14:51:29.171608Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:51:29.132308Z","title":null,"venue":null,"work_id":"f631f98d-cec6-4892-854a-1f0e813302f5","year":2023},"citing_paper":{"arxiv_id":"2508.20828","last_updated":"2025-08-28T14:23:39Z","snapshot_observed_at":"2026-08-10T04:05:47.588066Z","submitted_at":"2025-08-28T14:23:39Z","title":"GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-05T14:51:28.603213Z"},"links":{"citing_paper":"/paper/2508.20828"},"observation_digest":"sha256:9e5c3d3def51af454d2e09a2389d686e23973ecbac56aeb0fc2840a30059dd09","observation_id":"b9dac41a-02e8-4f57-8ebf-0309ef6b39a8","resolution":{"observed_at":"2026-08-05T14:51:29.141435Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:51:29.105290Z","title":null,"venue":null,"work_id":"63fee2cd-ec87-46b6-88a3-82f3600c9c29","year":2024},"citing_paper":{"arxiv_id":"2508.20828","last_updated":"2025-08-28T14:23:39Z","snapshot_observed_at":"2026-08-10T04:05:47.588066Z","submitted_at":"2025-08-28T14:23:39Z","title":"GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-05T14:51:28.624883Z"},"links":{"citing_paper":"/paper/2508.20828"},"observation_digest":"sha256:a08fa989ced2b856dff9f3b4c204c3163998c772585f7d096005d4ffc96e3603","observation_id":"d3a3768f-91a7-48ef-a0d9-daac520f10d2","resolution":{"observed_at":"2026-08-05T14:51:29.114133Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:51:29.075259Z","title":null,"venue":null,"work_id":"bfbda6bf-998d-4bcd-8ff8-d6b4e0a2e134","year":2022},"citing_paper":{"arxiv_id":"2508.20828","last_updated":"2025-08-28T14:23:39Z","snapshot_observed_at":"2026-08-10T04:05:47.588066Z","submitted_at":"2025-08-28T14:23:39Z","title":"GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-05T14:51:28.632251Z"},"links":{"citing_paper":"/paper/2508.20828"},"observation_digest":"sha256:11367420921422aff1074133284421158dd2ff3c7232c4a9f50dba4a9bb25145","observation_id":"b0c5bd1f-8004-4e59-8d36-e05ac122d606","resolution":{"observed_at":"2026-08-05T14:51:29.085354Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:51:29.045420Z","title":null,"venue":null,"work_id":"23e19cac-f411-4196-b631-d5012736c4a3","year":2022},"citing_paper":{"arxiv_id":"2508.20828","last_updated":"2025-08-28T14:23:39Z","snapshot_observed_at":"2026-08-10T04:05:47.588066Z","submitted_at":"2025-08-28T14:23:39Z","title":"GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-05T14:51:28.644536Z"},"links":{"citing_paper":"/paper/2508.20828"},"observation_digest":"sha256:7132c73347620fb81fcc3c0a2b8f7d1bba344415a9399b4256f2bb41c3420a54","observation_id":"7ef6e67c-74cb-479e-ba35-ec44f66d8b7f","resolution":{"observed_at":"2026-08-05T14:51:29.050902Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T14:51:29.012260Z","title":null,"venue":null,"work_id":"e0032d71-cba9-4017-bd28-899542b5a92b","year":2023},"citing_paper":{"arxiv_id":"2508.20828","last_updated":"2025-08-28T14:23:39Z","snapshot_observed_at":"2026-08-10T04:05:47.588066Z","submitted_at":"2025-08-28T14:23:39Z","title":"GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-05T14:51:28.659622Z"},"links":{"citing_paper":"/paper/2508.20828"},"observation_digest":"sha256:e42d4fc69bed6716448330d89199ff80f28210bed3e5f7d0283e028f98feb34d","observation_id":"476d2594-443c-41f1-b9b9-1e39e28180f7","resolution":{"observed_at":"2026-08-05T14:51:29.025977Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2508.20828","last_updated":"2025-08-28T14:23:39Z","latest_version":1,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-10T04:05:47.588066Z","submitted_at":"2025-08-28T14:23:39Z","title":"GDLLM: A Global Distance-aware Modeling Approach Based on Large Language Models for Event Temporal Relation Extraction"},"reference_resolution":{"displayed":36,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":34,"verified_exact":2,"verified_fuzzy":0},"total_outbound_references":36},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2508.20828."}