{"as_of":"2026-08-11T12:18:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:f92b671d7567907c74a44f4393f54b8c27a3df74d1686b60321cafd088c8d40c","coverage":[{"denominator":54,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":54,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T16:50:52.843844Z","state":"measured"},{"denominator":54,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":54,"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/2501.12857/citation-record","integrity":"/paper/2501.12857/integrity","json":"/paper/2501.12857/citation-record.json","paper":"/paper/2501.12857"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-10T16:50:53.392438Z","title":null,"venue":null,"work_id":"83803352-2139-4788-bc5b-96995cb9718f","year":2024},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.650611Z"},"links":{"citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:33251b020b5b4ab009ab4f4caabdf3cb5a4e57da59e24c6597ef4f5d43fe919e","observation_id":"9afd5097-aeab-4817-8cb5-d02334730717","resolution":{"observed_at":"2026-08-10T16:50:53.396335Z","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":"2005.14165","last_updated":"2020-07-22T19:47:17Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2020-05-28T17:29:03Z","title":"Language Models are Few-Shot Learners","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2005.14165","snapshot_observed_at":"2026-08-10T16:50:52.655347Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.655347Z"},"links":{"cited_paper":"/paper/2005.14165","citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:0931fbd38c85c2b1a0d036c030a290d04f43d6ac2fa814364f50e32ce70fb9bd","observation_id":"1bb8c5fb-29d2-402e-9851-a6391d9a64ea","resolution":{"observed_at":"2026-08-10T16:50:52.655347Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.08170","last_updated":"2024-04-11T05:01:12Z","snapshot_observed_at":"2026-08-04T08:10:16.676870Z","submitted_at":"2024-02-13T02:03:26Z","title":"LLaGA: Large Language and Graph Assistant","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.08170","snapshot_observed_at":"2026-08-10T16:50:52.659501Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.659501Z"},"links":{"cited_paper":"/paper/2402.08170","citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:dc9643443b71a325904be1622fe92343b72de6fe57fae3aa775f108a0e54dd04","observation_id":"7abfbfee-2ba1-4f89-9ac0-1fc328da1730","resolution":{"observed_at":"2026-08-10T16:50:52.659501Z","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":"2026-08-10T16:50:53.381030Z","title":null,"venue":null,"work_id":"6e265539-6806-425d-ba50-21092cc07df1","year":2021},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.663374Z"},"links":{"citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:60df65e6d37cf6dd225f1c45cb8ef9c06e68b425edbcb880b90f64f4d5a1b954","observation_id":"b049d9f8-50cc-40ef-93b4-88d0650a03dc","resolution":{"observed_at":"2026-08-10T16:50:53.384931Z","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":"2111.00064","last_updated":"2022-03-11T14:02:28Z","snapshot_observed_at":"2026-08-01T19:53:36.776939Z","submitted_at":"2021-10-29T19:55:12Z","title":"Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.00064","snapshot_observed_at":"2026-08-10T16:50:52.667223Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.667223Z"},"links":{"cited_paper":"/paper/2111.00064","citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:2af3cc34084159f7a3d13b1f994d007d16faadb2829f34fe51e5f51b643378cb","observation_id":"d3a5df3f-0794-4415-92b7-5d2f4d5894e7","resolution":{"observed_at":"2026-08-10T16:50:52.667223Z","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":"2026-08-10T16:50:53.369895Z","title":null,"venue":null,"work_id":"653a393d-999a-4f9c-9a48-0b5d412df07b","year":2020},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.670659Z"},"links":{"citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:677d48d7ee9b5f2e373f6640c0d511924de7d806e92d00b8cf0051b1d94a51b3","observation_id":"60e98172-124a-4d36-9dfc-c7718740f152","resolution":{"observed_at":"2026-08-10T16:50:53.373627Z","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":"2026-08-10T16:50:53.359023Z","title":null,"venue":null,"work_id":"d8362bdd-0736-4329-a415-1a9fe60e1c15","year":2017},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.674248Z"},"links":{"citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:709329b92e9eb2cbf5cb7453cdf66c2786d7ceb7657344727d724bfa19d22987","observation_id":"75b252e5-1917-43f4-aaf7-7d9d988f3ee9","resolution":{"observed_at":"2026-08-10T16:50:53.362858Z","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":"2026-08-10T16:50:52.677433Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.677433Z"},"links":{"citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:5e17247ee4db02338ac8c7ee5af44bbcd09104093c6eb42d752e81bd64691b0b","observation_id":"6b00ffcd-a264-4df5-880e-895ba9ad418b","resolution":{"observed_at":"2026-08-10T16:50:52.677433Z","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":"2026-08-10T16:50:53.334806Z","title":null,"venue":null,"work_id":"b671235a-db03-4ae8-a18a-9f3db2c94d3d","year":null},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.684773Z"},"links":{"citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:65eeef3e199a4640b7ad6d3409327d538e47aa93de768306a6e0c0e3a5208412","observation_id":"16d85320-714d-4edf-b9ee-af50ab6c6182","resolution":{"observed_at":"2026-08-10T16:50:53.338176Z","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":"2310.04560","last_updated":"2023-10-06T19:55:21Z","snapshot_observed_at":"2026-08-04T23:16:40.305780Z","submitted_at":"2023-10-06T19:55:21Z","title":"Talk like a Graph: Encoding Graphs for Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.04560","snapshot_observed_at":"2026-08-10T16:50:52.692475Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.692475Z"},"links":{"cited_paper":"/paper/2310.04560","citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:1168d32c9874a4a77ef211d2133d2301ee5875436dc821b10319884dbbe99ff9","observation_id":"f0b165a2-c75c-45e3-98d3-d92c34322ce7","resolution":{"observed_at":"2026-08-10T16:50:52.692475Z","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":"2026-08-10T16:50:53.309862Z","title":null,"venue":null,"work_id":"d77890da-31b6-410f-a789-a57ffe347971","year":2017},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.696842Z"},"links":{"citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:02fb6c67c0084d82c712a616940f288840a91de75da9006616eefa66eae48548","observation_id":"c19e13d2-3ee5-4f18-9773-689660a9eafc","resolution":{"observed_at":"2026-08-10T16:50:53.314935Z","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":"2026-08-10T16:50:53.296565Z","title":null,"venue":null,"work_id":"54752251-9faa-4dc7-abe7-49c33aa035c8","year":2020},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.700688Z"},"links":{"citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:6d801fd3c389605620c57fedd99520d21dcf9a24f6dcad9783527ac7738af248","observation_id":"c4a0028e-dead-4518-b2bc-1a984962aaaa","resolution":{"observed_at":"2026-08-10T16:50:53.300714Z","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":"2305.15066","last_updated":"2023-07-11T15:08:00Z","snapshot_observed_at":"2026-08-05T16:16:01.066507Z","submitted_at":"2023-05-24T11:53:19Z","title":"GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.15066","snapshot_observed_at":"2026-08-10T16:50:52.704494Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.704494Z"},"links":{"cited_paper":"/paper/2305.15066","citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:8a9f43996c26578fc22d400139848e708a41ac0edd5cfa6d571516cc5710a457","observation_id":"56c39fed-6f72-4c6b-8484-fe8ec6801680","resolution":{"observed_at":"2026-08-10T16:50:52.704494Z","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":"2026-08-10T16:50:53.282505Z","title":null,"venue":null,"work_id":"a08e6703-2e10-424e-ad2c-3a0690c66695","year":2020},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.708499Z"},"links":{"citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:828b5380722f14195eaa168c8185d5e1dd79a01ce3a5c4b165ca4ccb290ca443","observation_id":"0425675e-f6aa-43e5-a2e9-07c060ee1e4a","resolution":{"observed_at":"2026-08-10T16:50:53.286672Z","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":"2305.12268","last_updated":"2023-05-20T19:17:10Z","snapshot_observed_at":"2026-08-03T02:33:34.894752Z","submitted_at":"2023-05-20T19:17:10Z","title":"Patton: Language Model Pretraining on Text-Rich Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.12268","snapshot_observed_at":"2026-08-10T16:50:52.712362Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.712362Z"},"links":{"cited_paper":"/paper/2305.12268","citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:b6d35a0406425c1a2d9691bb29df0b7d8a0e2b7c1bece8c2baf97e762aae7471","observation_id":"1c98db87-d1e8-4482-aa23-517736de4bab","resolution":{"observed_at":"2026-08-10T16:50:52.712362Z","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":"2026-08-10T16:50:53.271725Z","title":null,"venue":null,"work_id":"6e6b6471-0ce3-4f93-9eb7-13f9110be4dd","year":2023},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.716720Z"},"links":{"citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:d561410a843f39ca061a21ea14f575f10e54c04dbe5675040a98b6f938001f2e","observation_id":"f651c310-6270-474b-9d82-7149075b53be","resolution":{"observed_at":"2026-08-10T16:50:53.275481Z","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":"2026-08-10T16:50:53.260613Z","title":null,"venue":null,"work_id":"1a15eb72-0a44-44a2-bb7b-79454529ecc5","year":2021},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.720377Z"},"links":{"citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:aa144a57cd57fc23efa9758ef89a2697a4e048d3b441acd5e9d0f5fc4b436a40","observation_id":"3dc3cb99-effe-4e99-bf80-e97ca36c6273","resolution":{"observed_at":"2026-08-10T16:50:53.264234Z","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":"2026-08-10T16:50:52.723959Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.723959Z"},"links":{"citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:8e3fe827c93d95f4076b8cf163b37459385e87356111616900878d15515f5d0f","observation_id":"2ac34089-6873-4bae-8200-6ef390a481c2","resolution":{"observed_at":"2026-08-10T16:50:52.723959Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.04541","last_updated":"2022-03-21T17:57:13Z","snapshot_observed_at":"2026-08-09T09:21:23.154524Z","submitted_at":"2021-10-09T11:05:16Z","title":"The Inductive Bias of In-Context Learning: Rethinking Pretraining Example Design","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.04541","snapshot_observed_at":"2026-08-10T16:50:52.727150Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.727150Z"},"links":{"cited_paper":"/paper/2110.04541","citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:3ff70ee9c711e7bf63a87daa02cb9fde508ec427e5b04e01a4b804f9af19fc3b","observation_id":"5a644f2e-5c39-498e-9bf9-23fbe3f3533b","resolution":{"observed_at":"2026-08-10T16:50:52.727150Z","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":"2026-08-10T16:50:52.730091Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.730091Z"},"links":{"citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:77ac5fbde50116c94ba14ae8aceeeafe3c2bdfa34a8fa72ae843a10a75228fdf","observation_id":"8fdf4fcf-5db8-4cc5-915e-baefa5e192c0","resolution":{"observed_at":"2026-08-10T16:50:52.730091Z","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":"2026-08-10T16:50:53.237539Z","title":null,"venue":null,"work_id":"3de91f14-61e1-4bb5-b4d4-cc23b88cec50","year":2022},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.733389Z"},"links":{"citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:0fabff198f3d5857d6848ef13ff68667728cb4be27f04bcaa953fc47c5ffae65","observation_id":"1a34e7e9-6036-4f15-b36e-45566d3e57c3","resolution":{"observed_at":"2026-08-10T16:50:53.241192Z","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":"1903.10318","last_updated":"2019-09-05T15:35:21Z","snapshot_observed_at":"2026-08-11T03:42:30.396818Z","submitted_at":"2019-03-25T13:42:45Z","title":"Fine-tune BERT for Extractive Summarization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1903.10318","snapshot_observed_at":"2026-08-10T16:50:52.736304Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.736304Z"},"links":{"cited_paper":"/paper/1903.10318","citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:f7bc814ee64f56ab1f08e1485834afb22ce3ad10510853fb0abbcac155c3bad2","observation_id":"1e8bf25e-ce44-4527-b51c-dd4acb7ddaf6","resolution":{"observed_at":"2026-08-10T16:50:52.736304Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"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":"2026-08-10T16:50:52.739396Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.739396Z"},"links":{"cited_paper":"/paper/1907.11692","citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:76d475662606a49e011ec423f8aa73fdee78094f5072c47d5dedf5025e30aaed","observation_id":"38361565-4ce4-47f1-afd6-efa36a11ab5d","resolution":{"observed_at":"2026-08-10T16:50:52.739396Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.05101","last_updated":"2019-01-04T21:01:49Z","snapshot_observed_at":"2026-08-09T20:34:52.923500Z","submitted_at":"2017-11-14T14:24:06Z","title":"Decoupled Weight Decay Regularization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.05101","snapshot_observed_at":"2026-08-10T16:50:52.742401Z","title":null,"venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.742401Z"},"links":{"cited_paper":"/paper/1711.05101","citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:b1903122378f82fed78cb32d2a8d2344b1cabd718490c3fca62038c7ccfcba0f","observation_id":"f90b1448-a58e-401a-8c02-94509099f327","resolution":{"observed_at":"2026-08-10T16:50:52.742401Z","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":"2026-08-10T16:50:53.227796Z","title":null,"venue":null,"work_id":"3c1e1aaa-e4bf-461b-b312-6f4cb8b1a164","year":2022},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.746146Z"},"links":{"citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:50c01bf7f3dad9e50af5ea5a1df848f342f6ad415e1dc7de18e231460b25abad","observation_id":"fcbb789d-1a55-4077-a303-35eb58ba4551","resolution":{"observed_at":"2026-08-10T16:50:53.231373Z","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":"2026-08-10T16:50:52.749810Z","title":null,"venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.749810Z"},"links":{"citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:42b9d785245e4e7fa5aa8a589683bddda37ab1845181de11598e996a973256f5","observation_id":"8db750ea-2c74-46f9-b14b-3f29c59e9722","resolution":{"observed_at":"2026-08-10T16:50:52.749810Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.08774","last_updated":"2024-03-04T06:01:33Z","snapshot_observed_at":"2026-08-07T07:30:12.213965Z","submitted_at":"2023-03-15T17:15:04Z","title":"GPT-4 Technical Report","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.08774","snapshot_observed_at":"2026-08-10T16:50:52.753644Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.753644Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:187ab37784cff642b1a8af57ed2eeb04a41adf29fa1534b6ba3c840ae957d27f","observation_id":"059a1de3-0261-4746-affd-3c424449a64d","resolution":{"observed_at":"2026-08-10T16:50:52.753644Z","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":"2026-08-10T16:50:52.757632Z","title":null,"venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.757632Z"},"links":{"citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:aa843c0b6eb8797acef74bf0558625044089b90d6dbe6f2010a6cd9c2f4cac96","observation_id":"110ff36f-20f4-47cb-b0f1-0e4d4f863ce8","resolution":{"observed_at":"2026-08-10T16:50:52.757632Z","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":"2026-08-10T16:50:52.761473Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.761473Z"},"links":{"citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:1c9fa5dc482f109f04cbae206d715a68ba6766cb0b74f3700e34a38b4cf2bb29","observation_id":"a5a79f1f-eb66-4454-beec-508ff046a997","resolution":{"observed_at":"2026-08-10T16:50:52.761473Z","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":"2026-08-10T16:50:52.764962Z","title":null,"venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.764962Z"},"links":{"citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:5d2c5ea34515fb32b88f2253a5756775861050416cd6d609a1d6b79f8493b4ba","observation_id":"58d3ff8a-4cd8-44eb-adea-fce4f95157ee","resolution":{"observed_at":"2026-08-10T16:50:52.764962Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1910.01108","last_updated":"2020-03-01T02:57:50Z","snapshot_observed_at":"2026-08-07T19:07:36.327251Z","submitted_at":"2019-10-02T17:56:28Z","title":"DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1910.01108","snapshot_observed_at":"2026-08-10T16:50:52.768492Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.768492Z"},"links":{"cited_paper":"/paper/1910.01108","citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:ca1b845d59e46420cb9de52fde95eb8180ab2cc0fe08fddafaaf7d584a4adfdb","observation_id":"d1f9cf98-4295-4a1d-97c2-7099e37d853c","resolution":{"observed_at":"2026-08-10T16:50:52.768492Z","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":"2026-08-10T16:50:53.192852Z","title":null,"venue":null,"work_id":"4cd723f7-db39-484d-8a29-0cde8ee1a640","year":2019},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.772265Z"},"links":{"citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:61ed63127782cc0b0301950bc9495ec6134f7997e46f90f257ee9cc9141f5456","observation_id":"b068ad84-7ae3-4476-803e-20077269a10c","resolution":{"observed_at":"2026-08-10T16:50:53.195863Z","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":"2026-08-10T16:50:53.183222Z","title":null,"venue":null,"work_id":"80e83e20-349a-4032-ac04-19771344487b","year":2011},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.775989Z"},"links":{"citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:5612d19085e51d3a08ce7f32e717b8c4cc81392105cebf66fea43f239dd9f6f4","observation_id":"fdf5a696-ec10-4920-9930-91fb05dff795","resolution":{"observed_at":"2026-08-10T16:50:53.186508Z","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":"2026-08-10T16:50:52.779109Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.779109Z"},"links":{"citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:6aa608288ed13e4da1f0ab244d0430fbed28d127ae852980359c2707e40db6a8","observation_id":"6d0fc214-2c5d-4f58-ae9b-c8ae846c48be","resolution":{"observed_at":"2026-08-10T16:50:52.779109Z","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":"2026-08-10T16:50:52.782852Z","title":null,"venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.782852Z"},"links":{"citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:1fc75695d837d7ba4b2771e556a134f48db68875479965497b03522a05a19260","observation_id":"3567777a-bfb7-4f80-babf-2f749d8a9f74","resolution":{"observed_at":"2026-08-10T16:50:52.782852Z","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":"2026-08-10T16:50:52.786017Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.786017Z"},"links":{"citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:d7b9c4a5fa0c70ec5e317c94151aec5f05787c5aaf21e6757b8aebc8b286c715","observation_id":"1b922da6-9bd6-471b-a4b3-865c68d874d4","resolution":{"observed_at":"2026-08-10T16:50:52.786017Z","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":"2026-08-10T16:50:52.789498Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.789498Z"},"links":{"citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:ba3c24cd8bb77ef1d18f1bf966a63543bc68debd5a72b2eb0a0c8e8c4c64ce26","observation_id":"d97c253e-05d7-4743-a985-fd98d2f04c52","resolution":{"observed_at":"2026-08-10T16:50:52.789498Z","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":"2026-08-10T16:50:53.135908Z","title":null,"venue":null,"work_id":"4097de3c-2e68-482b-880e-66748c14a6df","year":2020},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.796326Z"},"links":{"citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:ef727bb57a400b92054aaf4e25c826ad588adaf2c3f38c1bbc2f67a7959d54e1","observation_id":"812329b2-717b-4e6d-b7e0-df2973ec9a67","resolution":{"observed_at":"2026-08-10T16:50:53.139585Z","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":"2026-08-10T16:50:52.799850Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.799850Z"},"links":{"citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:2d884c4a622d9f89e06fe03bad274e37682f994a7d0990ab074536ee193e2724","observation_id":"99596991-a116-42ad-8d46-9fb65717206a","resolution":{"observed_at":"2026-08-10T16:50:52.799850Z","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":"2026-08-10T16:50:52.803509Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.803509Z"},"links":{"citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:308517836cfe94c2eb22f5f82a2bb49ba4e86144bb203f9065ad183e69d4aa37","observation_id":"4c892fa5-ba1b-4629-823e-37671181f4d3","resolution":{"observed_at":"2026-08-10T16:50:52.803509Z","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":"2026-08-10T16:50:53.100988Z","title":null,"venue":null,"work_id":"23b12d44-ccae-4c1c-9fab-6dd482218400","year":2022},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.810469Z"},"links":{"citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:261610006c13b9b957df7d46731438caa6eb422fb10136cf4b34a7a4ce546359","observation_id":"9a9d6c8c-de84-4aa9-b264-5e97fdee7095","resolution":{"observed_at":"2026-08-10T16:50:53.104667Z","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":"2203.15827","last_updated":"2022-03-29T18:01:24Z","snapshot_observed_at":"2026-08-06T19:04:02.601531Z","submitted_at":"2022-03-29T18:01:24Z","title":"LinkBERT: Pretraining Language Models with Document Links","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2203.15827","snapshot_observed_at":"2026-08-10T16:50:52.814038Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.814038Z"},"links":{"cited_paper":"/paper/2203.15827","citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:6550e2d3d7c8de0571d5511865fc5f6a9dbc787f3d533411a945d1106eda3a73","observation_id":"bfdebd4b-4286-4328-813a-84fb0e41fa66","resolution":{"observed_at":"2026-08-10T16:50:52.814038Z","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":"2026-08-10T16:50:53.090451Z","title":null,"venue":null,"work_id":"cb142e9b-060a-4cbd-88cd-a9a608ff9ace","year":2024},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.817996Z"},"links":{"citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:159203fadc223fe489efb0a2b452ca816646faa779a3658e10269aad56de7558","observation_id":"1ee9ac1a-9903-4c1a-824b-32ddb9b4d0a4","resolution":{"observed_at":"2026-08-10T16:50:53.094289Z","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":"2026-08-10T16:50:53.080088Z","title":null,"venue":null,"work_id":"1b3786ce-a011-482f-bff7-049ce842a943","year":2021},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.821607Z"},"links":{"citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:65efb46ad5dbb3c3abde55b518fd8c98666a6f9c43051619e495cedbcfa30105","observation_id":"0e10f7ba-171a-43b6-8b1a-d11fc690171a","resolution":{"observed_at":"2026-08-10T16:50:53.083608Z","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":"2026-08-10T16:50:53.068650Z","title":null,"venue":null,"work_id":"20ac32c5-e728-4a91-b33e-151c1bf75af7","year":2023},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.825137Z"},"links":{"citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:e1e889e528a8b7e1cf89d17b076ccd4c38b79722521010e1240e506d369b9067","observation_id":"5b46b21a-2469-4830-8fe4-9d37b90abb36","resolution":{"observed_at":"2026-08-10T16:50:53.072374Z","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":"2026-08-10T16:50:53.058042Z","title":null,"venue":null,"work_id":"a3714993-9bf5-4e96-8feb-5fef05a0664f","year":2023},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.828687Z"},"links":{"citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:f44c7e2fa731f2f035377994a48c67d439289d9a00e3900e7e828411b5bf6360","observation_id":"4fa77aa6-4953-47bb-a705-7981c08bc347","resolution":{"observed_at":"2026-08-10T16:50:53.061551Z","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":"2026-08-10T16:50:53.047648Z","title":null,"venue":null,"work_id":"a97e4bd5-3e4e-48c1-a5a8-995d70f9b5b6","year":2019},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.832525Z"},"links":{"citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:461796ebe7a2806f3d28293eff91fe4bcba976ca9f0ecfa6ea76138108760407","observation_id":"5f6eb58c-0aca-48c5-93de-41b9417501e2","resolution":{"observed_at":"2026-08-10T16:50:53.051114Z","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":"2026-08-10T16:50:53.036693Z","title":null,"venue":null,"work_id":"bf92cc55-847d-418a-b9fe-25b53e41b333","year":2019},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.836162Z"},"links":{"citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:3386b2ed06a022a6fc89cae1c4adf298da0b3974e74882f8c6b8c6c353c5e638","observation_id":"34c2ec78-1c22-4db8-8a42-939ddbb9af51","resolution":{"observed_at":"2026-08-10T16:50:53.040585Z","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":"2026-08-10T16:50:53.025478Z","title":null,"venue":null,"work_id":"da31f47f-1ece-4520-a822-ab32595adc35","year":2021},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.840192Z"},"links":{"citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:a4909ee5115bf9c991093fb6520ba06ced4f8432d336dcf3fcabedecd039edc1","observation_id":"c8104b03-107c-41cc-9d23-a4da36966689","resolution":{"observed_at":"2026-08-10T16:50:53.029251Z","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":"2310.12580","last_updated":"2023-10-23T01:46:04Z","snapshot_observed_at":"2026-07-06T16:35:31.876632Z","submitted_at":"2023-10-19T08:41:21Z","title":"Pretraining Language Models with Text-Attributed Heterogeneous Graphs","version":2},"cited_work":{"arxiv_id":"2310.12580","doi":null,"metadata_source":"pith","pith_arxiv_id":"2310.12580","snapshot_observed_at":"2026-08-10T16:50:52.875651Z","title":"Pretraining Language Models with Text-Attributed Heterogeneous Graphs","venue":"cs.CL","work_id":"7db1440d-1321-471d-b1bf-982a73d3e708","year":2023},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.843844Z"},"links":{"cited_paper":"/paper/2310.12580","citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:c18a5195baca4e273e8cf5fd5c7c3f72900f5e1616e1d91a21a3d281ddff01c4","observation_id":"8facedbc-10db-478f-8273-6a016dbe4e4b","resolution":{"observed_at":"2026-08-10T16:50:52.880914Z","resolver_source":"local_arxiv","status":"malformed_identifier"},"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":"2026-08-10T16:50:53.322898Z","title":"the Journal of machine Learning research 9 (2008), 1871–1874","venue":null,"work_id":"77e9251f-3f01-4f84-930e-f656a927970c","year":2008},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":2008,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.688484Z"},"links":{"citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:e6ecc5504c69b2da218c8ab0d460745f649589c4676431a16a9c3c73af97067a","observation_id":"a7e5f409-78da-4f8f-a3af-2d6330c111e4","resolution":{"observed_at":"2026-08-10T16:50:53.326989Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2026-08-10T16:50:53.147270Z","title":"In The world wide web conference","venue":null,"work_id":"828da1db-b334-40dd-a7a4-ccd7eaabd1b1","year":2022},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.793058Z"},"links":{"citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:867ca02be40de574f82b23d539a298ffb316aceed392abded2bed0fc7042ce29","observation_id":"419b00b6-38f1-4b50-b7c1-8e34452b5f79","resolution":{"observed_at":"2026-08-10T16:50:53.150992Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2026-08-10T16:50:53.111660Z","title":"IEEE Transactions on Knowledge and Data Engineering 35, 2 (2021), 1637–1650","venue":null,"work_id":"6363fcf2-d15f-4625-b88c-911ea90055de","year":2021},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":2021,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.806989Z"},"links":{"citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:5b56db3464bad2a9c9c4d5dc22771cf45c2388743966dd9f490598d01ba7e7aa","observation_id":"ec38466d-5286-441e-842b-217b27fcc867","resolution":{"observed_at":"2026-08-10T16:50:53.115822Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"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":"2026-08-10T16:50:52.680852Z","title":"In Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks","version":1},"reference_index":2022,"source":"pdf_text","source_observed_at":"2026-08-10T16:50:52.680852Z"},"links":{"citing_paper":"/paper/2501.12857"},"observation_digest":"sha256:75915ed6e0842a3cd7dce5485959e761d3f93f74aff6619f3a428afb750d2186","observation_id":"1b5b48f1-b979-4235-9879-a9b06a126af5","resolution":{"observed_at":"2026-08-10T16:50:52.680852Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2501.12857","last_updated":"2025-01-22T13:09:09Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-10T16:40:16.521722Z","submitted_at":"2025-01-22T13:09:09Z","title":"HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks"},"reference_resolution":{"displayed":54,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":50,"verified_exact":0,"verified_fuzzy":3},"total_outbound_references":54},"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 54 of 54 outbound references and 0 inbound Pith citation observations for arXiv:2501.12857."}