{"as_of":"2026-08-18T05:58:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2c4e8141537f5b50020a9ebca21fe7134d332e199d9e9180ce9118f7c354baba","coverage":[{"denominator":47,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":47,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T04:31:05.831009Z","state":"measured"},{"denominator":47,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":47,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-17T06:30:58.91139+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/2505.03799/citation-record","integrity":"/paper/2505.03799/integrity","json":"/paper/2505.03799/citation-record.json","paper":"/paper/2505.03799"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"1810.04805","last_updated":"2019-05-24T20:37:26Z","snapshot_observed_at":"2026-08-14T18:16:28.847993Z","submitted_at":"2018-10-11T00:50:01Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.04805","snapshot_observed_at":"2026-08-16T04:31:05.558453Z","title":"Bert: Pre-training of deep bidirectional transformers for language understanding,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.558453Z"},"links":{"cited_paper":"/paper/1810.04805","citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:31ba22533440459447abea97a706c35af6a6397a3ea76492de7be73585e0528f","observation_id":"054bc2d4-258a-470e-bf30-cf8f8136be70","resolution":{"observed_at":"2026-08-16T04:31:05.558453Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.13971","last_updated":"2023-02-27T17:11:15Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-02-27T17:11:15Z","title":"LLaMA: Open and Efficient Foundation Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.13971","snapshot_observed_at":"2026-08-16T04:31:05.564981Z","title":"Llama: Open and efficient foundation language models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.564981Z"},"links":{"cited_paper":"/paper/2302.13971","citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:f1f1b28b58888690f09e60cb06a612d130fc3ce498ee199d5ef6cb3855dcb928","observation_id":"78d05784-ba7a-4db2-89fb-cc4109c71737","resolution":{"observed_at":"2026-08-16T04:31:05.564981Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2103.00020","last_updated":"2021-02-26T19:04:58Z","snapshot_observed_at":"2026-07-06T10:45:03.059688Z","submitted_at":"2021-02-26T19:04:58Z","title":"Learning Transferable Visual Models From Natural Language Supervision","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2103.00020","snapshot_observed_at":"2026-08-16T04:31:05.570329Z","title":"Learning transferable visual models from natural language supervision,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.570329Z"},"links":{"cited_paper":"/paper/2103.00020","citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:f80353dc0a0d305e9767de49675acc53755a183fbb50e205ed0fe30bd1dceaaf","observation_id":"fba1df1f-444b-469f-a5f6-c0fa2a7baaa8","resolution":{"observed_at":"2026-08-16T04:31:05.570329Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2204.14198","last_updated":"2022-11-15T23:07:37Z","snapshot_observed_at":"2026-08-17T03:00:38.806206Z","submitted_at":"2022-04-29T16:29:01Z","title":"Flamingo: a Visual Language Model for Few-Shot Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.14198","snapshot_observed_at":"2026-08-16T04:31:05.576328Z","title":"Flamingo: a visual language model for few-shot learning,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.576328Z"},"links":{"cited_paper":"/paper/2204.14198","citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:92f58a9dd86ba2d24c8164f2b7a590d79dbbf8245579a06fb4ecee095b7d05f3","observation_id":"2cde9c55-99e2-45d0-b8d7-eb5c018d153d","resolution":{"observed_at":"2026-08-16T04:31:05.576328Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2301.12597","last_updated":"2023-06-15T07:57:29Z","snapshot_observed_at":"2026-08-12T12:01:54.105712Z","submitted_at":"2023-01-30T00:56:51Z","title":"BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2301.12597","snapshot_observed_at":"2026-08-16T04:31:05.581678Z","title":"Blip-2: Bootstrapping language- image pre-training with frozen image encoders and large language models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.581678Z"},"links":{"cited_paper":"/paper/2301.12597","citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:aed760457e2cc0465eb9756050694ea67adb7ba19fc2d6ad33ab38fd61490cc7","observation_id":"bcb9e8c3-0399-4aa9-8431-e787b3f5fcb4","resolution":{"observed_at":"2026-08-16T04:31:05.581678Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2205.01917","last_updated":"2022-06-14T00:48:04Z","snapshot_observed_at":"2026-08-13T15:12:42.567441Z","submitted_at":"2022-05-04T07:01:14Z","title":"CoCa: Contrastive Captioners are Image-Text Foundation Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2205.01917","snapshot_observed_at":"2026-08-16T04:31:05.586742Z","title":"Coca: Contrastive captioners are image-text foundation models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.586742Z"},"links":{"cited_paper":"/paper/2205.01917","citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:e6b1dc805662f5a8b6dcf67d090c92b796722db883c96c45a1e6520d663b49d3","observation_id":"7e340581-3f2d-413b-a926-d780c297ee7d","resolution":{"observed_at":"2026-08-16T04:31:05.586742Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2001.05140","last_updated":"2020-01-22T15:16:10Z","snapshot_observed_at":"2026-08-17T03:39:08.714352Z","submitted_at":"2020-01-15T05:56:59Z","title":"Graph-Bert: Only Attention is Needed for Learning Graph Representations","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2001.05140","snapshot_observed_at":"2026-08-16T04:31:05.592494Z","title":"Graph-bert: Only attention is needed for learning graph representations,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.592494Z"},"links":{"cited_paper":"/paper/2001.05140","citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:54c9165efde42c2760f0ddc901f993760f88061de9b8402ad4d7adb569f948b5","observation_id":"830567b4-d968-4476-822e-98f866a023bc","resolution":{"observed_at":"2026-08-16T04:31:05.592494Z","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":"2026-08-16T04:31:06.798137Z","title":"Graphicl: Unlocking graph learning potential in llms through structured prompt design,","venue":null,"work_id":"49c84a84-cf0b-4cdf-bc9f-49a107847e27","year":null},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.608069Z"},"links":{"citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:ec508e0bd24b66e0fe652167f4dc6a9bbed1737da0d1bb93af6a29b2105942c9","observation_id":"572b90da-296c-4803-b30e-28232284330d","resolution":{"observed_at":"2026-08-16T04:31:06.804276Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2406.01032","last_updated":"2024-06-03T06:33:51Z","snapshot_observed_at":"2026-08-16T13:46:52.669505Z","submitted_at":"2024-06-03T06:33:51Z","title":"LLM and GNN are Complementary: Distilling LLM for Multimodal Graph Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.01032","snapshot_observed_at":"2026-08-16T04:31:05.618702Z","title":"Llm and gnn are complementary: Distilling llm for multimodal graph learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.618702Z"},"links":{"cited_paper":"/paper/2406.01032","citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:bf93fc06e50b18923b25d3c0feb51f82655f51300e4b29e8f994242f2c6b0726","observation_id":"488dbe08-9e12-4cfb-a196-e0a73ea731d1","resolution":{"observed_at":"2026-08-16T04:31:05.618702Z","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":"2026-08-16T04:31:05.623718Z","title":"Can we soft prompt llms for graph learning tasks?","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.623718Z"},"links":{"citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:2d48022b15f2257b28f2bee9082f85a01e52a731e71cead4b746586247097e47","observation_id":"a088644a-2659-43ab-8fac-cedb107761ad","resolution":{"observed_at":"2026-08-16T04:31:05.623718Z","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":"2026-08-16T04:31:06.781015Z","title":"A survey of graph meets large language model: Progress and future directions,","venue":null,"work_id":"b36c047c-18a6-46fe-86ef-39b315fcbf8b","year":null},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.628419Z"},"links":{"citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:265a587ffbbf513d6417dd28c1b3d7abd912991609398e0f1438478d619cfc29","observation_id":"c81cf562-a620-45e1-a04a-c94f5fa8ad31","resolution":{"observed_at":"2026-08-16T04:31:06.786309Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:31:06.764170Z","title":"Challenges and opportunities in gnn-llm integration,","venue":null,"work_id":"6b56bdf8-573b-4c44-92a6-af4fd2625494","year":2023},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.643467Z"},"links":{"citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:b2bd655c59a53cdc01b190227dc11b24d662e671f056f3bfc1e371fcffbffb8f","observation_id":"e61b0a34-fd51-4c06-a7fb-a8fa00502efa","resolution":{"observed_at":"2026-08-16T04:31:06.769606Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.08170","last_updated":"2024-04-11T05:01:12Z","snapshot_observed_at":"2026-08-16T14:19:08.144752Z","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":"2026-08-16T04:31:05.648169Z","title":"Llaga: Large language and graph assistant,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.648169Z"},"links":{"cited_paper":"/paper/2402.08170","citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:d9b57ba161b384e9a992e85668d67ef1e92d19118139b472b9b93b6b8f8e71eb","observation_id":"ae43084d-2447-48e8-9263-1066ad71f669","resolution":{"observed_at":"2026-08-16T04:31:05.648169Z","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":"2026-08-16T04:31:06.746998Z","title":"A Note on Over-Smoothing for Graph Neural Networks,","venue":null,"work_id":"3a7af3a5-accf-4a65-a870-07e47ecbe67d","year":2020},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.653591Z"},"links":{"citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:b7aac2843f15a7cfdc52683840038b8a70674c054a4e09a40986dbfba8f300c8","observation_id":"28d62395-b304-4b34-b25f-82711cd19f61","resolution":{"observed_at":"2026-08-16T04:31:06.752482Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.10947","last_updated":"2021-01-06T13:32:14Z","snapshot_observed_at":"2026-08-17T02:06:18.538217Z","submitted_at":"2019-05-27T02:59:06Z","title":"Graph Neural Networks Exponentially Lose Expressive Power for Node Classification","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1905.10947","snapshot_observed_at":"2026-08-16T04:31:05.658971Z","title":"Graph neural networks exponentially lose expressive power for node classification,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.658971Z"},"links":{"cited_paper":"/paper/1905.10947","citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:10145e0d7f8afc55ea7370edaae4dfd0dad21a7af1bc9ce78f0dc683a1c2c8a1","observation_id":"0b99e848-16ac-4820-9a8a-c42ff1472ecf","resolution":{"observed_at":"2026-08-16T04:31:05.658971Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.07134","last_updated":"2024-02-06T03:08:44Z","snapshot_observed_at":"2026-08-18T02:50:07.023808Z","submitted_at":"2023-08-14T13:41:09Z","title":"Language is All a Graph Needs","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.07134","snapshot_observed_at":"2026-08-16T04:31:05.664494Z","title":"Language is all a graph needs,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.664494Z"},"links":{"cited_paper":"/paper/2308.07134","citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:c6f32829c9c987802b94d1e23d98f092c9baa7ac12506e3d3f86592aabbcfc86","observation_id":"cb7b0195-2420-4e21-88f9-68e8e1a1dcd4","resolution":{"observed_at":"2026-08-16T04:31:05.664494Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1609.02907","last_updated":"2017-02-22T09:55:36Z","snapshot_observed_at":"2026-08-17T10:49:36.026134Z","submitted_at":"2016-09-09T19:48:41Z","title":"Semi-Supervised Classification with Graph Convolutional Networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.02907","snapshot_observed_at":"2026-08-16T04:31:05.670604Z","title":"Semi-supervised classification with graph convolutional networks,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.670604Z"},"links":{"cited_paper":"/paper/1609.02907","citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:eaec79d3ea754b5c0fcfc91e9824afeb940a78e5bd8d298d0f0d373e46018e68","observation_id":"01edd159-7ecd-4561-ac36-6e8070c9c5b5","resolution":{"observed_at":"2026-08-16T04:31:05.670604Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10903","last_updated":"2018-02-04T19:13:29Z","snapshot_observed_at":"2026-08-13T22:35:40.714745Z","submitted_at":"2017-10-30T12:41:12Z","title":"Graph Attention Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.10903","snapshot_observed_at":"2026-08-16T04:31:05.677254Z","title":"Graph attention networks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.677254Z"},"links":{"cited_paper":"/paper/1710.10903","citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:928ebafd9b16ea93b79642e4963cb662828d9de105b3ba94ce3a0890439a976d","observation_id":"26dcbff7-21c7-45bf-97a9-fc2fe507ff02","resolution":{"observed_at":"2026-08-16T04:31:05.677254Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1706.02216","last_updated":"2018-09-10T14:26:58Z","snapshot_observed_at":"2026-08-16T10:46:47.387624Z","submitted_at":"2017-06-07T14:51:05Z","title":"Inductive Representation Learning on Large Graphs","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.02216","snapshot_observed_at":"2026-08-16T04:31:05.685486Z","title":"Inductive representation learning on large graphs,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.685486Z"},"links":{"cited_paper":"/paper/1706.02216","citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:984c148de1f263c44ae84e1761cf9ca89a81b28401742b282f7e1a98ad739dc5","observation_id":"b6e91533-1ebf-4880-9e57-444881d6846c","resolution":{"observed_at":"2026-08-16T04:31:05.685486Z","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":"2026-08-17T09:58:46.058102Z","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":"2026-08-16T04:31:05.692365Z","title":"Gpt-4 technical report,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.692365Z"},"links":{"cited_paper":"/paper/2303.08774","citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:7dfce93073b465a7e96bb2599dbae6df20241f9f39e6005977b2a71323a3aac9","observation_id":"02b7a2cc-27d9-4a3a-8c03-94bce7f6e240","resolution":{"observed_at":"2026-08-16T04:31:05.692365Z","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":"2026-08-16T04:31:05.698206Z","title":"Scaling instruction-finetuned language models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.698206Z"},"links":{"citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:d712f770d795e4bee539b5a6d59a54638cdc019d5eb739fd8f488ba33174b5ca","observation_id":"b7179ccd-c334-4d60-a08e-1e4d76e61429","resolution":{"observed_at":"2026-08-16T04:31:05.698206Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.15066","last_updated":"2023-07-11T15:08:00Z","snapshot_observed_at":"2026-08-17T17:57:54.380112Z","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":"2026-08-16T04:31:05.703500Z","title":"Gpt4graph: Can large language models understand graph structured data ? an empirical evaluation and benchmarking,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.703500Z"},"links":{"cited_paper":"/paper/2305.15066","citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:98da9ea2c6621b23d10d56b691fd58016ebc0f4577db35b30b01921f5dcfde3e","observation_id":"5782b229-7cea-441e-a48a-50b6665fbcd9","resolution":{"observed_at":"2026-08-16T04:31:05.703500Z","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":"2026-08-16T04:31:06.719757Z","title":"A survey of large language models for graphs,","venue":null,"work_id":"f022fbf4-d22a-42d4-9986-67591bc9dbe8","year":2024},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.709882Z"},"links":{"citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:19eb82e9413125d82040a0157bdf3d0588e94a9cbb5ce740aeed87e14648bd47","observation_id":"c3587d7a-c41c-4825-aba7-f9e16901c17e","resolution":{"observed_at":"2026-08-16T04:31:06.725209Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.13023","last_updated":"2024-05-07T10:10:14Z","snapshot_observed_at":"2026-08-16T14:50:49.761494Z","submitted_at":"2023-10-19T06:17:46Z","title":"GraphGPT: Graph Instruction Tuning for Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.13023","snapshot_observed_at":"2026-08-16T04:31:05.715411Z","title":"Graphgpt: Graph instruction tuning for large language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.715411Z"},"links":{"cited_paper":"/paper/2310.13023","citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:9ba5171635dcb422e7f0611ecdacabf658b5685984f8dd926e3f4bde0f8b2dc7","observation_id":"5fd6a5d6-3083-4cee-99e5-c7f1e2d779ea","resolution":{"observed_at":"2026-08-16T04:31:05.715411Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16024","last_updated":"2024-05-19T02:28:56Z","snapshot_observed_at":"2026-08-16T14:15:35.073855Z","submitted_at":"2024-02-25T08:07:22Z","title":"HiGPT: Heterogeneous Graph Language Model","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.16024","snapshot_observed_at":"2026-08-16T04:31:05.721411Z","title":"Higpt: Heterogeneous graph language model,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.721411Z"},"links":{"cited_paper":"/paper/2402.16024","citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:ee015656c1ef72a9de3146f38e25beebcaf3402612774866dd04933bfc49b581","observation_id":"8ce2ff35-03d7-40e1-80e0-0e7e085f27d6","resolution":{"observed_at":"2026-08-16T04:31:05.721411Z","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":"2026-08-16T04:31:05.727277Z","title":"Graphllm: Boosting graph reasoning ability of large language model,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.727277Z"},"links":{"citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:484fb016cc0b490ae4e7c47065de671157aa8458d5277b033e070e960a10e18a","observation_id":"cb4cc75f-8648-407c-b7f9-1fffb447c2cc","resolution":{"observed_at":"2026-08-16T04:31:05.727277Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.02848","last_updated":"2023-09-06T09:12:52Z","snapshot_observed_at":"2026-08-16T15:02:54.700594Z","submitted_at":"2023-09-06T09:12:52Z","title":"Prompt-based Node Feature Extractor for Few-shot Learning on Text-Attributed Graphs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.02848","snapshot_observed_at":"2026-08-16T04:31:05.739167Z","title":"Prompt-based node feature extractor for few-shot learning on text-attributed graphs,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.739167Z"},"links":{"cited_paper":"/paper/2309.02848","citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:1f65cf15296970750f27dd9b9359374f283036ad73c562511e8da2cd8d6bd65b","observation_id":"fdab10ca-4215-4d38-9bcc-3a1f1550cfcd","resolution":{"observed_at":"2026-08-16T04:31:05.739167Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.01121","last_updated":"2024-10-09T12:10:38Z","snapshot_observed_at":"2026-08-16T14:13:32.272142Z","submitted_at":"2024-03-02T08:05:03Z","title":"OpenGraph: Towards Open Graph Foundation Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.01121","snapshot_observed_at":"2026-08-16T04:31:05.744402Z","title":"Opengraph: Towards open graph foundation models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.744402Z"},"links":{"cited_paper":"/paper/2403.01121","citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:305614ae6b08ca84351c3a03fdf741e5c654aae16a650fee551cd636c7bda2b8","observation_id":"c81755ca-b3d7-4ce4-8ae2-f8804b0c4c62","resolution":{"observed_at":"2026-08-16T04:31:05.744402Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2201.08860","last_updated":"2022-01-21T19:00:05Z","snapshot_observed_at":"2026-08-16T17:26:47.524844Z","submitted_at":"2022-01-21T19:00:05Z","title":"GreaseLM: Graph REASoning Enhanced Language Models for Question Answering","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.08860","snapshot_observed_at":"2026-08-16T04:31:05.749881Z","title":"Greaselm: Graph reasoning enhanced language models for question answering,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.749881Z"},"links":{"cited_paper":"/paper/2201.08860","citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:36e2667db9508305cc4c57aa91cbc586d793bd21c58336b063cb11b0a312f16d","observation_id":"0b7435a0-7250-4592-b96c-68f5d055aa79","resolution":{"observed_at":"2026-08-16T04:31:05.749881Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.18152","last_updated":"2024-03-09T16:08:16Z","snapshot_observed_at":"2026-08-16T14:48:16.178231Z","submitted_at":"2023-10-27T14:00:04Z","title":"Disentangled Representation Learning with Large Language Models for Text-Attributed Graphs","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.18152","snapshot_observed_at":"2026-08-16T04:31:05.756100Z","title":"Disentangled representation learning with large language models for text-attributed graphs,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.756100Z"},"links":{"cited_paper":"/paper/2310.18152","citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:813842f9981fb836ebb36b7787016db82f38e95f7b97a5789c3bbce51a8830d6","observation_id":"1e271753-f18e-46d8-830b-092a2a3652d9","resolution":{"observed_at":"2026-08-16T04:31:05.756100Z","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":"2026-08-16T04:31:06.692697Z","title":"Walklm: A uniform language model fine-tuning framework for attributed graph embedding,","venue":null,"work_id":"618ac8c0-c93a-434c-872f-f922e33066d9","year":2023},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.762090Z"},"links":{"citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:3f82e8047b1f1e1aff8c95ecdc77763dfe758ef120eb6e948a764e45b0a2f3ab","observation_id":"ef87397f-5bea-4623-be5d-9fad8262b060","resolution":{"observed_at":"2026-08-16T04:31:06.697832Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:31:05.767593Z","title":"Enhancing graph representation learning with walklm for effective community detection,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.767593Z"},"links":{"citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:23623996896c928c36c5b5edef159fcb550915be4644d44f2b1b7fa716f9359b","observation_id":"ae37f767-d403-43d0-ab1a-809c4669d8d8","resolution":{"observed_at":"2026-08-16T04:31:05.767593Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.16029","last_updated":"2024-07-03T06:39:59Z","snapshot_observed_at":"2026-08-16T14:15:34.874445Z","submitted_at":"2024-02-25T08:41:32Z","title":"GraphWiz: An Instruction-Following Language Model for Graph Problems","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.16029","snapshot_observed_at":"2026-08-16T04:31:05.772433Z","title":"Graphwiz: An instruction-following language model for graph problems,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.772433Z"},"links":{"cited_paper":"/paper/2402.16029","citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:bda74804b4cd560347ed1d7731a7bddb9c3f4592aaf3f7e577351fef25e8baf5","observation_id":"8aa304fe-0336-4876-8d50-e40ec58c6bdc","resolution":{"observed_at":"2026-08-16T04:31:05.772433Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2012.09699","last_updated":"2021-01-24T09:38:54Z","snapshot_observed_at":"2026-08-17T09:51:04.568396Z","submitted_at":"2020-12-17T16:11:47Z","title":"A Generalization of Transformer Networks to Graphs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2012.09699","snapshot_observed_at":"2026-08-16T04:31:05.777349Z","title":"A generalization of transformer networks to graphs,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.777349Z"},"links":{"cited_paper":"/paper/2012.09699","citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:d77f5ed21496b428d39c227414b71dfaad9886e89dacb44ad2e11955905aea2c","observation_id":"20b4a409-b37e-4a35-8c29-338e7c42012d","resolution":{"observed_at":"2026-08-16T04:31:05.777349Z","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":"2026-08-16T04:31:06.815199Z","title":"Do transformers really perform bad for graph representation?","venue":null,"work_id":"588a5758-c174-40ee-b25e-52b2523a4c02","year":null},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.782927Z"},"links":{"citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:45428ee134eda4e74a8464e7bce4886f4adfd2995911b2399702a0a4d7375274","observation_id":"dd71df03-7ee3-4d9e-bd02-7859fd6d3ba3","resolution":{"observed_at":"2026-08-16T04:31:06.820706Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:31:05.794009Z","title":"Graph convolutional neural networks for web-scale recommender systems,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.794009Z"},"links":{"citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:dbffac1a13447ae38c95134049233d9ead61a6a5cf95c3ddba68c676f54a1df2","observation_id":"60e4afab-aa32-4da4-a07f-9a8050a795bd","resolution":{"observed_at":"2026-08-16T04:31:05.794009Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1603.08861","last_updated":"2016-05-26T23:57:09Z","snapshot_observed_at":"2026-08-15T20:32:58.026233Z","submitted_at":"2016-03-29T17:46:16Z","title":"Revisiting Semi-Supervised Learning with Graph Embeddings","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1603.08861","snapshot_observed_at":"2026-08-16T04:31:05.799853Z","title":"Revisiting semi- supervised learning with graph embeddings,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.799853Z"},"links":{"cited_paper":"/paper/1603.08861","citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:c540e6bb1cbc0026a11475a8c4c053d85641b018379408f7472d2b574a848e6d","observation_id":"c01ae631-c6dc-4d75-8f3b-28dcf2a792df","resolution":{"observed_at":"2026-08-16T04:31:05.799853Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.19523","last_updated":"2024-03-07T02:45:36Z","snapshot_observed_at":"2026-08-16T15:28:05.962866Z","submitted_at":"2023-05-31T03:18:03Z","title":"Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.19523","snapshot_observed_at":"2026-08-16T04:31:05.805308Z","title":"Harnessing explanations: Llm-to-lm interpreter for enhanced text- attributed graph representation learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.805308Z"},"links":{"cited_paper":"/paper/2305.19523","citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:01c83c998edb674f7ae8e121756e0067f119dd3aebd6f68a35ae98cccd80b8a2","observation_id":"27593ac7-ef8e-48d7-a80a-587e5e1e0c85","resolution":{"observed_at":"2026-08-16T04:31:05.805308Z","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":"2026-08-16T04:31:06.675851Z","title":"Open graph benchmark: Datasets for machine learning on graphs,","venue":null,"work_id":"59afc220-9867-4501-9f9a-026f3a308f13","year":2020},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.810451Z"},"links":{"citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:e793f063d729926f6ff53608938df610209deb1481b5b19fb932f9b8378c6cf2","observation_id":"c3ea1c45-bae6-4b4b-a3e5-898e3186c2d3","resolution":{"observed_at":"2026-08-16T04:31:06.681169Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.05234","last_updated":"2021-11-24T01:39:16Z","snapshot_observed_at":"2026-08-16T18:18:19.815640Z","submitted_at":"2021-06-09T17:18:52Z","title":"Do Transformers Really Perform Bad for Graph Representation?","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.05234","snapshot_observed_at":"2026-08-16T04:31:05.787509Z","title":"Available: https://arxiv.org/abs/2106.05234","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.787509Z"},"links":{"cited_paper":"/paper/2106.05234","citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:1086d916b755451a7b71603dce11c9a2ab5fc11cd3abb8bbe52e00cf7abe0047","observation_id":"afc69ea7-3444-4e5b-8372-f4726a2c1b26","resolution":{"observed_at":"2026-08-16T04:31:05.787509Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.14274","last_updated":"2024-01-01T22:49:19Z","snapshot_observed_at":"2026-08-16T15:37:55.701717Z","submitted_at":"2023-04-25T09:40:47Z","title":"When Do Graph Neural Networks Help with Node Classification? Investigating the Impact of Homophily Principle on Node Distinguishability","version":4},"cited_work":{"arxiv_id":"2304.14274","doi":null,"metadata_source":"pith","pith_arxiv_id":"2304.14274","snapshot_observed_at":"2026-08-16T04:31:05.892336Z","title":"When Do Graph Neural Networks Help with Node Classification? Investigating the Impact of Homophily Principle on Node Distinguishability","venue":"cs.SI","work_id":"f3116515-2b1a-491e-a998-f676085edf07","year":2023},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.825633Z"},"links":{"cited_paper":"/paper/2304.14274","citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:d23d0dfeb0adb9e2cc2c143ff8fc0aa04260421c91ed5801591c8558b8258245","observation_id":"7aa752e1-66e2-4869-b382-5219c5ca99b3","resolution":{"observed_at":"2026-08-16T04:31:05.898545Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1093/database/baz064","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T04:31:05.865157Z","title":"Pubmed text similarity model and its application to curation efforts in the conserved domain database,","venue":null,"work_id":"6bd05160-62a6-4263-b3d2-a937a1c931de","year":2019},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.831009Z"},"links":{"citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:bb9bec393bc3c74f4742c57c32426f44ea77c1b735ef6ec41421ffa8ae7b975f","observation_id":"4eaa510f-c63c-4204-80b5-d9bfb7502783","resolution":{"observed_at":"2026-08-16T04:31:05.872862Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+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":"2026-08-16T04:31:05.815525Z","title":"Decoupled weight decay regularization,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.815525Z"},"links":{"citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:2c6aa851bc15c074964b62d0c67f02cad07f857c2f35c670cd017364e271047a","observation_id":"3ad6b90e-8401-414a-918b-1b75f0e16f93","resolution":{"observed_at":"2026-08-16T04:31:05.815525Z","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":"2026-08-14T20:13:52.872565Z","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":"2026-08-16T04:31:05.820533Z","title":"Available: https://arxiv.org/abs/1711.05101","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":2019,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.820533Z"},"links":{"cited_paper":"/paper/1711.05101","citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:04fb909f00c4560fbd88c43c57a68645bf4eba61789f301b0e9cd2e204f853f9","observation_id":"ffe41436-ea32-467b-8d74-abe846e63a02","resolution":{"observed_at":"2026-08-16T04:31:05.820533Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.05845","last_updated":"2023-10-09T16:42:00Z","snapshot_observed_at":"2026-08-17T17:42:22.960135Z","submitted_at":"2023-10-09T16:42:00Z","title":"GraphLLM: Boosting Graph Reasoning Ability of Large Language Model","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.05845","snapshot_observed_at":"2026-08-16T04:31:05.732843Z","title":"Available: https://arxiv.org/abs/2310.05845","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.732843Z"},"links":{"cited_paper":"/paper/2310.05845","citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:0a49c609a505b2a7c726fec136aa4737f921e8dfbb08750b9ce2d6712816236d","observation_id":"bf24838c-e50b-4828-9409-c9dc7e0ec21e","resolution":{"observed_at":"2026-08-16T04:31:05.732843Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.12399","last_updated":"2024-04-24T08:48:13Z","snapshot_observed_at":"2026-08-16T14:41:35.826327Z","submitted_at":"2023-11-21T07:22:48Z","title":"A Survey of Graph Meets Large Language Model: Progress and Future Directions","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.12399","snapshot_observed_at":"2026-08-16T04:31:05.633287Z","title":"Available: https://arxiv.org/abs/2311.12399","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.633287Z"},"links":{"cited_paper":"/paper/2311.12399","citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:4f537dd9ae49d1943911f10c0e21a299309fd06cd7e87c0bec57fcf370603e50","observation_id":"32f01aa3-7cc2-4ae0-ab5d-acab7009194d","resolution":{"observed_at":"2026-08-16T04:31:05.633287Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.15755","last_updated":"2025-01-27T03:50:30Z","snapshot_observed_at":"2026-08-15T23:34:03.997304Z","submitted_at":"2025-01-27T03:50:30Z","title":"GraphICL: Unlocking Graph Learning Potential in LLMs through Structured Prompt Design","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.15755","snapshot_observed_at":"2026-08-16T04:31:05.613232Z","title":"Available: https://arxiv.org/abs/2501.15755","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling","version":1},"reference_index":2025,"source":"pdf_text","source_observed_at":"2026-08-16T04:31:05.613232Z"},"links":{"cited_paper":"/paper/2501.15755","citing_paper":"/paper/2505.03799"},"observation_digest":"sha256:2da6c6055c19386c7b6b2eb03f970dbf669038046a31f6e63c14c31a5baee403","observation_id":"40cf9b60-da90-4364-a051-eb56d4fbaf63","resolution":{"observed_at":"2026-08-16T04:31:05.613232Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.03799","last_updated":"2025-05-02T06:08:21Z","latest_version":1,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-17T12:45:43.773703Z","submitted_at":"2025-05-02T06:08:21Z","title":"Scalability Matters: Overcoming Challenges in InstructGLM with Similarity-Degree-Based Sampling"},"reference_resolution":{"displayed":47,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":37,"verified_exact":2,"verified_fuzzy":8},"total_outbound_references":47},"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":"2026-08-17T06:30:58.91139+00:00","source":"crossref"},{"observed_at":"2026-08-17T06:30:54.323127+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2505.03799."}