{"as_of":"2026-08-15T23:17:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:252aaa30e08c385682e96b704fbcb6db2594d63338283525c717a29359b690cd","coverage":[{"denominator":57,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":57,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-11T05:56:03.977881Z","state":"measured"},{"denominator":58,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":58,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T23:26:53.248472Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-06T23:26:55.709250Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"cited_work":{"arxiv_id":"2412.17029","doi":null,"metadata_source":"pith","pith_arxiv_id":"2412.17029","snapshot_observed_at":"2026-08-06T23:26:55.709250Z","title":"GraphAgent: Agentic Graph Language Assistant","venue":"cs.AI","work_id":"c097eae6-e2c5-40bd-b50d-0b683efef480","year":2024},"citing_paper":{"arxiv_id":"2506.18019","last_updated":"2025-07-04T14:29:40Z","snapshot_observed_at":"2026-08-12T12:16:56.653983Z","submitted_at":"2025-06-22T12:59:12Z","title":"Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunities","version":3},"reference_index":139,"source":"pdf_text","source_observed_at":"2026-08-06T23:26:53.248472Z"},"links":{"cited_paper":"/paper/2412.17029","citing_paper":"/paper/2506.18019"},"observation_digest":"sha256:fbf7843d6adcb6e17fbc7f622161f83e92dd10d483a38a2aab2b4ab40ad3d7fe","observation_id":"e45a494e-b9a3-49ea-a918-917ed2b1b044","resolution":{"observed_at":"2026-08-06T23:26:55.717155Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2412.17029/citation-record","integrity":"/paper/2412.17029/integrity","json":"/paper/2412.17029/citation-record.json","paper":"/paper/2412.17029"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-11T05:56:03.662379Z","title":"write newline","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.662379Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:140c8fbdb7c3f0921bcefc1aa7d0e35051f72e9961432e1027521738c69214dd","observation_id":"6a71612e-2b26-490f-ab2f-b6c8c3e2d0c9","resolution":{"observed_at":"2026-08-11T05:56:03.662379Z","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":"2026-08-11T05:56:05.205994Z","title":"Curriculum learning","venue":null,"work_id":"82084b21-48af-4460-94d5-08d5c7f6c40f","year":2009},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.669357Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:c4ac0a7e9f3784b68db37733bcfacae5e58eb0ec0218f99b0e80930aee8e6024","observation_id":"4100f588-1ad6-49f7-babb-402ed5e1e4b2","resolution":{"observed_at":"2026-08-11T05:56:05.211176Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T05:56:05.187147Z","title":"Edgi: Equivariant diffusion for planning with embodied agents","venue":null,"work_id":"022df97c-8dce-484f-8bfa-0a891a9e0b6e","year":2024},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.675922Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:11686ff6f40fe6ae39993feee5e3c7f25e794baa404bfbe5f9fcd291516eab74","observation_id":"9fe36266-8bb1-444f-993b-5e1ed344a271","resolution":{"observed_at":"2026-08-11T05:56:05.192339Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T05:56:05.168416Z","title":"Web-scale academic name disambiguation: the whoiswho benchmark, leaderboard, and toolkit","venue":null,"work_id":"da8c1081-a50f-432f-a9c3-4304f276137a","year":2023},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.682443Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:f26474365415b9ea9131ba035ad99a7e4b8a8cbe6cbef7fa8642a4b99039e163","observation_id":"fabdf366-2d2c-4e97-8407-0e14de982bb1","resolution":{"observed_at":"2026-08-11T05:56:05.173874Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T05:56:05.148916Z","title":"Simple and deep graph convolutional networks","venue":null,"work_id":"219ebe07-3750-4206-8a8c-341e873db364","year":2020},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.688506Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:43198853b48f5788d8d092c5e343bc6a4846b05b86d3118a38563d71213f21a4","observation_id":"ef7594b4-fa0c-48a5-bbcb-35a96ed5b2d4","resolution":{"observed_at":"2026-08-11T05:56:05.156109Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T05:56:05.130487Z","title":"Llaga: Large language and graph assistant","venue":null,"work_id":"668d2837-cc9b-45f9-9d56-da5dc9c6e313","year":2024},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.694089Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:428dfb6aa4b878031143b0faa3b63775131caa48ae1b8548be9212e32f1d3e82","observation_id":"43139ace-4e54-4e77-8474-295b8d123612","resolution":{"observed_at":"2026-08-11T05:56:05.135728Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T05:56:05.112195Z","title":"Llaga: Large language and graph assistant","venue":null,"work_id":"cc45afd4-f5f2-4e18-8f60-43c415279642","year":2024},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.699511Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:8ae349c042ccf420fefacc56cf57d0bc950394c6c79d61786b333d4914de9a56","observation_id":"c95c6db2-cf9c-49d2-88dc-5ad0294fb7b2","resolution":{"observed_at":"2026-08-11T05:56:05.117343Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T05:56:05.094519Z","title":"Towards robust graph neural networks for noisy graphs with sparse labels","venue":null,"work_id":"32e75abe-618f-49c8-bed5-88f602e480f6","year":2022},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.707634Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:826705ae2949d79e64f031f391855a32eaaff631cac3c5b753132dd75f9e12f2","observation_id":"79cc1d9a-d8d9-4cc5-9e37-72b91f38561c","resolution":{"observed_at":"2026-08-11T05:56:05.100313Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T05:56:05.076585Z","title":"Benchmarking graph neural networks","venue":null,"work_id":"ff82ee5c-4919-4a47-a2d3-cb3b919c59ca","year":2023},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.713133Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:8c3ebbe12db213163f9c92da178c17868db5dc8dd507954053cd51e30174889a","observation_id":"3b19e5e0-b695-41ec-b567-bd93332c0cee","resolution":{"observed_at":"2026-08-11T05:56:05.082017Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1903.02428","last_updated":"2019-04-25T10:06:09Z","snapshot_observed_at":"2026-08-02T18:54:43.326912Z","submitted_at":"2019-03-06T14:50:02Z","title":"Fast Graph Representation Learning with PyTorch Geometric","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1903.02428","snapshot_observed_at":"2026-08-11T05:56:03.718532Z","title":"Fast graph representation learning with pytorch geometric","venue":null,"work_id":null,"year":1903},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.718532Z"},"links":{"cited_paper":"/paper/1903.02428","citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:b6bf6c1f943f89d57056596d2854c25a16ff71c5acd7e2fd3ab7a6c374b21896","observation_id":"93f16338-eb57-4483-9d87-3beddb095fc9","resolution":{"observed_at":"2026-08-11T05:56:03.718532Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.04615","last_updated":"2023-12-07T18:51:41Z","snapshot_observed_at":"2026-08-13T05:07:43.941227Z","submitted_at":"2023-12-07T18:51:41Z","title":"Relational Deep Learning: Graph Representation Learning on Relational Databases","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.04615","snapshot_observed_at":"2026-08-11T05:56:03.725267Z","title":"Relational deep learning: Graph representation learning on relational databases","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.725267Z"},"links":{"cited_paper":"/paper/2312.04615","citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:89c9ce36b81fb05453d060e905fe8e322f22bc06d2629be27bd8f49f10e6b5b9","observation_id":"1ac4cc63-daa5-4883-a5a2-f8464312953c","resolution":{"observed_at":"2026-08-11T05:56:03.725267Z","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":"2026-08-11T05:56:05.059191Z","title":"Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding","venue":null,"work_id":"9ad92c0e-dc89-4964-8db8-b6c6d87e648e","year":2020},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.731338Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:49e784b1f1ebb37fe319f7b161c3ce1074c82c60235b4e8dcb9ba2cfc5e2a8c8","observation_id":"2584602b-e2e5-4887-8cc1-bd0345c886ca","resolution":{"observed_at":"2026-08-11T05:56:05.064782Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T05:56:03.736619Z","title":"Graph representation learning","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.736619Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:754e025a9fbc038ff232d96012e0b16aee2ae9c9dd44cf167b0a7aef684754b9","observation_id":"6f76b94f-8177-40de-a092-21ed113da6a2","resolution":{"observed_at":"2026-08-11T05:56:03.736619Z","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":"2026-08-11T05:56:05.026109Z","title":"Hamilton, Zhitao Ying, and Jure Leskovec","venue":null,"work_id":"fb5d5bb5-81c8-4173-84e9-51e8a1abd138","year":2017},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.742109Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:c0bb442028fac04e687f9adb54dd20897c054a7f8c84fcd3b709013812dc6662","observation_id":"68d8fc78-ad31-4706-ba76-790f05d15df9","resolution":{"observed_at":"2026-08-11T05:56:05.032217Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T05:56:05.006917Z","title":"Gat-mf: Graph attention mean field for very large scale multi-agent reinforcement learning","venue":null,"work_id":"70f4badd-3534-48d4-ad48-1c2e7f209675","year":2023},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.747188Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:f11385be2ca8afc7787421b5f3405a59002e5bfd0a0156c03cc9ddcf2b76733d","observation_id":"b7657e57-7bd3-4dff-9fe4-a713c857eaac","resolution":{"observed_at":"2026-08-11T05:56:05.012618Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.19523","last_updated":"2024-03-07T02:45:36Z","snapshot_observed_at":"2026-08-14T09:16:02.849983Z","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":"2026-08-11T05:56:03.752386Z","title":"Harnessing explanations: Llm-to-lm interpreter for enhanced text-attributed graph representation learning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.752386Z"},"links":{"cited_paper":"/paper/2305.19523","citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:0212d1b533f8eb9478b0a3c835bfbdda0904baacd5dbcfb04a82904a40561a1f","observation_id":"17e79646-3c08-4682-b5d1-3870182be547","resolution":{"observed_at":"2026-08-11T05:56:03.752386Z","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":"2026-08-11T05:56:04.987983Z","title":"Cogagent: A visual language model for gui agents","venue":null,"work_id":"0318cea6-a0ff-4c69-9cfe-0d2114bb282d","year":2024},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.757878Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:7f686ced75e435ee850b19bcef9f29f02c663e57d67a8da0a2efe24e2223d17d","observation_id":"15b6ba6b-6495-4fc6-ae6a-c11b41c96526","resolution":{"observed_at":"2026-08-11T05:56:04.993934Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T05:56:04.970673Z","title":"Heterogeneous graph transformer","venue":null,"work_id":"0d6d7fdb-c94d-4fef-b524-0c996c66ce54","year":2020},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.763567Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:5cea2cb39e5f706e7cf776ea0e69277ab7162eb5dc70bcbb3c7484f347d8a06a","observation_id":"bf33693e-5715-4f86-8415-52f1410cf2e4","resolution":{"observed_at":"2026-08-11T05:56:04.975974Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T05:56:04.951038Z","title":"Uncertainty quantification over graph with conformalized graph neural networks","venue":null,"work_id":"77f8633c-050a-4bab-bcb9-104d85fb6483","year":2024},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.768633Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:7bf4c0553cd9a397d02f9c7988682c1a29cbbf0f2ba97b2aa05d3aabf3023e4b","observation_id":"3a681f35-83d5-4bf1-b5e0-300991a6104e","resolution":{"observed_at":"2026-08-11T05:56:04.958405Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T05:56:04.932853Z","title":"Grounded decoding: Guiding text generation with grounded models for embodied agents","venue":null,"work_id":"c2227389-eabc-454e-9fcb-d5f45a275f1b","year":2024},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.775676Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:a19adb5f7d23ad8725e7088a096d808b5f998840fc0958fbde55491aa76eedb1","observation_id":"60e29136-f75c-4cf1-828c-76a0f9baefb2","resolution":{"observed_at":"2026-08-11T05:56:04.939124Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T05:56:03.781684Z","title":"Swe-bench: Can language models resolve real-world github issues? In ICLR, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.781684Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:8502f94a6ca8447a17d2bf16cbca670446b42de958b2320c001ce165eef05929","observation_id":"ddb13453-7a78-47ca-9b87-42b8fab416c5","resolution":{"observed_at":"2026-08-11T05:56:03.781684Z","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":"2026-08-11T05:56:04.900951Z","title":"Graph structure learning for robust graph neural networks","venue":null,"work_id":"5aa6efb8-24c7-4b5c-9cf8-06ed98bf3a2b","year":2020},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.786837Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:c30fb11a025fb58acf5ad12f116c5f743037636653f07a341d78812a3a48811a","observation_id":"4172f921-438e-4dec-bd8b-4029703f4169","resolution":{"observed_at":"2026-08-11T05:56:04.906326Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T05:56:03.791949Z","title":"Semi-supervised classification with graph convolutional networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.791949Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:56cf81ab010bf54554e2efe7cfd6ce61af8c0412dc3fe11bfc127b8fb62e1ae0","observation_id":"940d6c1b-6fb2-4306-a1ce-a20098d94270","resolution":{"observed_at":"2026-08-11T05:56:03.791949Z","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":"2026-08-11T05:56:04.870815Z","title":"Visualwebarena: Evaluating multimodal agents on realistic visual web tasks","venue":null,"work_id":"eebe2a66-18c5-4f82-86fb-5b6207ef6047","year":2024},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.796568Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:f48587e86819a638debeb626933187b8b4d0555222378b8f864a3b6176c2c081","observation_id":"e5e8252e-ea7a-4d1a-b9ea-fd1dd635ab51","resolution":{"observed_at":"2026-08-11T05:56:04.876215Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T05:56:04.851078Z","title":"Text is all you need: Learning language representations for sequential recommendation","venue":null,"work_id":"124dd397-8c04-4d5c-8bd3-34a6abeae02e","year":2023},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.801419Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:66cd48318bf9cb81cec0a196c4317729a2d1166d47ecca9041daa313a7d0d671","observation_id":"43d50890-d9fc-47c4-add6-842a6092aba7","resolution":{"observed_at":"2026-08-11T05:56:04.856814Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T05:56:04.831383Z","title":"Zerog: Investigating cross-dataset zero-shot transferability in graphs","venue":null,"work_id":"97843939-70b0-4b76-bfc9-2bea9c690204","year":2024},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.806827Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:a228be04bf2b55a963db01bcfe25c84bc81bc16fb32f49d651f643ca5b11983e","observation_id":"d6ea41c1-d647-4a24-a123-eaa7e5cef373","resolution":{"observed_at":"2026-08-11T05:56:04.837478Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T05:56:03.812045Z","title":"Rouge: A package for automatic evaluation of summaries","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.812045Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:02d0bc83fbfa37e6cc77c023500fe68bfadb0a470448c1fd0dd393175cc7cb61","observation_id":"efd011bc-1558-4db1-ae23-4e447cddfd50","resolution":{"observed_at":"2026-08-11T05:56:03.812045Z","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":"2026-08-11T05:56:03.817388Z","title":"Visual instruction tuning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.817388Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:1e93922705935a401130bee7251db799ebcc4efcec2e26fd04775e39e62d2db5","observation_id":"1d27e31a-261e-46d9-a45b-e762b258fd69","resolution":{"observed_at":"2026-08-11T05:56:03.817388Z","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":"2026-08-11T05:56:04.790195Z","title":"Revisiting graph contrastive learning from the perspective of graph spectrum","venue":null,"work_id":"9be8363e-35dc-499d-98d5-de4c87449201","year":2022},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.822556Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:93bf28081a79c586aaa753f34117e298251d99c5c7abfd527a25b36121c48ac5","observation_id":"77c95761-3e0a-4186-af84-8f79300737d5","resolution":{"observed_at":"2026-08-11T05:56:04.796046Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T05:56:04.601428Z","title":"Llmscore: Unveiling the power of large language models in text-to-image synthesis evaluation","venue":null,"work_id":"0a1a31e3-11cd-4262-827c-2ae91f77d4c2","year":2024},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.832606Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:cab41986db8fc561868d7cb9aeed80ba15dddcf9f702e84cc811a90bac3bfa2f","observation_id":"8b85a6ea-fad8-411a-8b12-9d41eb06b5d9","resolution":{"observed_at":"2026-08-11T05:56:04.606976Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T05:56:04.582365Z","title":"Graph foundation models","venue":null,"work_id":"9178b857-4a88-4092-b405-9959646f6d08","year":2024},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.837799Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:7d88627ee350230a5f78115f7b0e591b7238dbdef30fc84c00ebb95525676f72","observation_id":"3b7d7926-74eb-45f0-b7ec-ca57a2efb841","resolution":{"observed_at":"2026-08-11T05:56:04.588787Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T05:56:04.559100Z","title":"Bleu: a method for automatic evaluation of machine translation","venue":null,"work_id":"fe5e6aca-ec92-4111-9ac7-bab5210f2c5b","year":2002},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.843925Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:42bb708d090b42ff94a4d6a03d198c084bae6dc8e520724fba45907badaaf580","observation_id":"cb074a9a-7dae-4b29-9103-1c4eeff8b07d","resolution":{"observed_at":"2026-08-11T05:56:04.566832Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T05:56:04.540529Z","title":"Reflexion: Language agents with verbal reinforcement learning","venue":null,"work_id":"2520999c-1feb-4f38-bdd1-fd1c171b097f","year":2023},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.849983Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:ade448d203db0b70cac9584b7813d6572f146588684ea694c43c4646ec815d24","observation_id":"aa1b9cca-6dca-4bbe-a48f-67306bfe0923","resolution":{"observed_at":"2026-08-11T05:56:04.546485Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T05:56:04.523668Z","title":"A review-aware graph contrastive learning framework for recommendation","venue":null,"work_id":"2bfd39dc-df2b-45b5-a826-20ea26591c26","year":2022},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.856205Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:0c4fa9a35ff4e502f3e6e5f9c701a6edf5182fc1151c784fe4c1552fc3e49997","observation_id":"7c64cdc8-b9d5-44f0-ab40-14883965e795","resolution":{"observed_at":"2026-08-11T05:56:04.529316Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T05:56:04.505391Z","title":"Graphgpt: Graph instruction tuning for large language models","venue":null,"work_id":"b8cc1605-009b-4c70-82b7-ffb6b3906a6c","year":2024},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.861971Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:9c74bac37b804867ab79c8c8133d67fca17aa5ca4b2ef3426c00079183b6366d","observation_id":"43b15cdd-8833-4f78-bb42-651676fd8ab2","resolution":{"observed_at":"2026-08-11T05:56:04.511414Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T05:56:04.484166Z","title":"Higpt: Heterogeneous graph language model","venue":null,"work_id":"63b3f8a9-e96d-444d-9cd5-4970a1ce3356","year":2024},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.867130Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:6ba73d4181b5dedb6d254cc62ee98dc3c848cb8eab274fd6d9bad44244ef39ad","observation_id":"87db8d7b-899e-44e2-82d9-967e0770d983","resolution":{"observed_at":"2026-08-11T05:56:04.491005Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T05:56:04.465367Z","title":"Graph attention networks","venue":null,"work_id":"e58aead6-7848-43d8-8732-a0483fe20416","year":2018},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.872195Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:35c372f9d15f6ba010a473fed06e52316944d594552349ccf175cc628ed85620","observation_id":"73a5a1e3-55c7-4d79-b80f-49bb1740988c","resolution":{"observed_at":"2026-08-11T05:56:04.470976Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T05:56:04.447792Z","title":"Graph attention networks","venue":null,"work_id":"5852c42b-c650-4724-98da-4e1610d78d5c","year":2018},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.877429Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:742e0a8013dfdbb20e2b8cd27eb429ec42d05d630cf099bbf621074f96273cb8","observation_id":"b92cbd58-1815-44a3-9d87-bd3e904c7576","resolution":{"observed_at":"2026-08-11T05:56:04.452843Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T05:56:04.431137Z","title":"Heterogeneous graph attention network","venue":null,"work_id":"2be8488a-a9c9-44ce-bd82-9cba6a7f926e","year":2022},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.882141Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:88692d695bab097ba62004c8b4b41b99504b89eb42e125cab233a11f27b4ef47","observation_id":"f740aa7d-d9ef-4141-9e78-512a2b4a1e25","resolution":{"observed_at":"2026-08-11T05:56:04.436491Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T05:56:04.413916Z","title":"Heterogeneous graph attention network","venue":null,"work_id":"43f85e9f-4dc7-41e5-a7ac-64e2f2702f6d","year":2022},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.887166Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:5bd4e7f814d89fdcb91f4aa410fa45af00cb5c7aa63e5e2b0cc0320b914c878d","observation_id":"0cbe24fc-db37-46b0-886a-e55940f62e6a","resolution":{"observed_at":"2026-08-11T05:56:04.419857Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T05:56:04.396731Z","title":"A survey on heterogeneous graph embedding: methods, techniques, applications and sources","venue":null,"work_id":"de756ad5-0013-4bd2-b8cb-8460292296bc","year":2022},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.891992Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:2ecbf07d6544c122c275faf87bb7b30bdbd8a317526ed641bc33dae6118b279f","observation_id":"f1405f96-5a8b-4c85-9097-c828a19b6759","resolution":{"observed_at":"2026-08-11T05:56:04.402151Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T05:56:03.896861Z","title":"Chain-of-thought prompting elicits reasoning in large language models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.896861Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:04ba2706e2ae3c85e6230f16306db5b64e13c98795e82c3799932fa05ae5154b","observation_id":"f784dfa1-6768-417b-84bf-66afca4797ff","resolution":{"observed_at":"2026-08-11T05:56:03.896861Z","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":"2026-08-11T05:56:04.366857Z","title":"Graph convolutional kernel machine versus graph convolutional networks","venue":null,"work_id":"3408b957-3500-42df-9786-2ef8a88f0ed1","year":2024},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.902343Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:937c27ffcd82adf86d2cb3a81eaac9be752fabd8a1e44658d4fdef88ed1eccc5","observation_id":"6fc64453-b86b-409a-9557-e3763aed2c36","resolution":{"observed_at":"2026-08-11T05:56:04.372093Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T05:56:04.349171Z","title":"A comprehensive survey on graph neural networks","venue":null,"work_id":"fcc0185b-4cc9-457a-939b-76e9e17b187d","year":2020},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.907175Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:305b96db99c36d2c481f5639adfc213998c631029544cb13f4824fca817dabdb","observation_id":"fdf17c33-8016-4ff8-9ce7-057148dc9213","resolution":{"observed_at":"2026-08-11T05:56:04.355185Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.10700","last_updated":"2024-08-20T09:57:13Z","snapshot_observed_at":"2026-08-14T14:37:35.362821Z","submitted_at":"2024-08-20T09:57:13Z","title":"AnyGraph: Graph Foundation Model in the Wild","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.10700","snapshot_observed_at":"2026-08-11T05:56:03.912077Z","title":"Anygraph: Graph foundation model in the wild","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.912077Z"},"links":{"cited_paper":"/paper/2408.10700","citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:c5912f128780ad01c69c07e9936424cae277c4494808db6610981660537e8a15","observation_id":"5eae998a-c5d6-45ff-a83b-2bacda4bc437","resolution":{"observed_at":"2026-08-11T05:56:03.912077Z","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":"2026-08-11T05:56:04.330584Z","title":"Openagents: An open platform for language agents in the wild","venue":null,"work_id":"48a9a08a-b0ac-437c-b50a-e70cf408ebef","year":2023},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.917763Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:7f0e8bd5075897c2933110e9fccc79cd181903cc73526c6a124b69ba499117eb","observation_id":"7ad73c4d-c2c7-4c6e-a4c7-2b46e61d6450","resolution":{"observed_at":"2026-08-11T05:56:04.336785Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T05:56:04.313695Z","title":"Curriculum learning for natural language understanding","venue":null,"work_id":"34890c49-7f16-41c4-be6c-04fd5c475e09","year":2020},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.923440Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:5dd665342c0c32f40ea8461bbe479af1c3a221ce9c72124a37b239d73c6cf19f","observation_id":"b096ae90-8339-4199-93a9-a67b566b8b8a","resolution":{"observed_at":"2026-08-11T05:56:04.318945Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.10671","last_updated":"2024-09-10T13:25:53Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-07-15T12:35:42Z","title":"Qwen2 Technical Report","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.10671","snapshot_observed_at":"2026-08-11T05:56:03.928451Z","title":"Qwen2 technical report","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.928451Z"},"links":{"cited_paper":"/paper/2407.10671","citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:192b47fe0df7b1486b99337bc282d4e3dc146482676b6ec0dca7d3ca23844e90","observation_id":"91268251-b562-4e19-9629-77d4e4291d1b","resolution":{"observed_at":"2026-08-11T05:56:03.928451Z","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":"2026-08-11T05:56:04.293824Z","title":"Understanding negative sampling in graph representation learning","venue":null,"work_id":"23449107-f95f-4923-a991-427a776a8216","year":2020},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.933386Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:0bcfbeddddbd6d7d9d82eebfc65c3474a91eb78134780fe44be5dcfd0166c744","observation_id":"afebdded-145f-44d0-8f27-866e7a36f43f","resolution":{"observed_at":"2026-08-11T05:56:04.299974Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T05:56:03.938235Z","title":"React: Synergizing reasoning and acting in language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.938235Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:5c54ae3286462c93e212f2ded1f4e45f3a253e2b444beb861c34960ed21118ce","observation_id":"d18614f2-3496-4b6d-8546-ccf0a4972be3","resolution":{"observed_at":"2026-08-11T05:56:03.938235Z","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":"2026-08-11T05:56:04.259245Z","title":null,"venue":null,"work_id":"699d266c-d24c-4172-844f-5c8aa127c629","year":2019},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.945462Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:65aebd3cc195c500ce3658a81df1e90f35199d75a019a251c7ba3694e62b7371","observation_id":"73d15a0e-7bc3-47d4-a600-d00296bbfd20","resolution":{"observed_at":"2026-08-11T05:56:04.265008Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T05:56:04.238801Z","title":"Graph attention multi-layer perceptron","venue":null,"work_id":"22dbd581-2989-497b-8d76-7aa8450a10ef","year":2022},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.950622Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:59cb7a5a64c4fa7779b28e02256617c3a69c1590b2d7eda8948e4d83765e802a","observation_id":"1ff9d953-fc97-4b73-9210-499c844649b8","resolution":{"observed_at":"2026-08-11T05:56:04.245449Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T05:56:04.219599Z","title":"Judging llm-as-a-judge with mt-bench and chatbot arena","venue":null,"work_id":"d98cd462-a127-4f96-b830-a97ea1bd9753","year":2024},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.956081Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:84c04c29a33aaaa855a876c8ff56f1a63862030a4f4ec375a66c305e9692f314","observation_id":"deeecb60-fbd3-47ef-bc63-350e12a29415","resolution":{"observed_at":"2026-08-11T05:56:04.225281Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T05:56:04.198539Z","title":"Knowledge-augmented graph machine learning for drug discovery: From precision to interpretability","venue":null,"work_id":"54d535ec-b563-43ec-9e39-8945e9d0e364","year":2023},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.961039Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:803a60b286a65c524a4eee86263658d15ee15f304d83deee68c60d8a5d303627","observation_id":"ed034af2-a450-459e-aac7-9cbb007c2e97","resolution":{"observed_at":"2026-08-11T05:56:04.207798Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+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":"2026-08-11T05:56:03.966590Z","title":"@esa (Ref","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.966590Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:5b8d98f44a3f5634880484d725eb1b74637cb00ae092b3cfa4c5bd4bf7475bfb","observation_id":"9a6f3e90-1b0c-4268-91e8-5bed338603f0","resolution":{"observed_at":"2026-08-11T05:56:03.966590Z","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":"2026-08-11T05:56:03.971779Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.971779Z"},"links":{"citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:0e1f6b1b6d4859f36d163222f23e929fcdf566f35e7d2639ea313d8ca0e71450","observation_id":"af9703f5-832c-4514-ab2c-89485a979e32","resolution":{"observed_at":"2026-08-11T05:56:03.971779Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.21783","last_updated":"2024-11-23T23:27:33Z","snapshot_observed_at":"2026-08-13T17:20:44.002518Z","submitted_at":"2024-07-31T17:54:27Z","title":"The Llama 3 Herd of Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.21783","snapshot_observed_at":"2026-08-11T05:56:03.977881Z","title":"̮t O : ,EC[v t cf, Ked /w(]GJ>ѵ z z ƹAH\\ 4 ߀k S<_|d 1=8 El5GK;Θ9 Iijk_Fuar=h* es","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant","version":1},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-11T05:56:03.977881Z"},"links":{"cited_paper":"/paper/2407.21783","citing_paper":"/paper/2412.17029"},"observation_digest":"sha256:b2b62e8bba4c1a118a3472c04dc8f21da5a992805f991174468ef55d72f09949","observation_id":"f1eaa440-c26a-43c6-a607-acbc0efee9e7","resolution":{"observed_at":"2026-08-11T05:56:03.977881Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2412.17029","last_updated":"2024-12-22T14:13:32Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-15T17:36:56.308509Z","submitted_at":"2024-12-22T14:13:32Z","title":"GraphAgent: Agentic Graph Language Assistant"},"reference_resolution":{"displayed":57,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":17,"verified_exact":0,"verified_fuzzy":40},"total_outbound_references":57},"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":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 1 inbound Pith citation observation for arXiv:2412.17029."}