{"as_of":"2026-08-18T15:44:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:d35a5576f92549b923a88a50b249364fd02c7658e2eb8e74b8f68c6a243299d2","coverage":[{"denominator":13,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":13,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-10T21:01:58.808095Z","state":"measured"},{"denominator":14,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":14,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+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":"2026-08-07T14:15:47.174246Z","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":"2026-08-07T14:15:56.995506Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2501.06625","last_updated":"2025-01-11T19:21:53Z","snapshot_observed_at":"2026-08-15T11:11:55.610347Z","submitted_at":"2025-01-11T19:21:53Z","title":"Guided Code Generation with LLMs: A Multi-Agent Framework for Complex Code Tasks","version":1},"cited_work":{"arxiv_id":"2501.06625","doi":null,"metadata_source":"pith","pith_arxiv_id":"2501.06625","snapshot_observed_at":"2026-08-07T14:15:56.995506Z","title":"Guided Code Generation with LLMs: A Multi-Agent Framework for Complex Code Tasks","venue":"cs.AI","work_id":"f0eca93b-eff7-49b7-952c-4fc96a27a455","year":2025},"citing_paper":{"arxiv_id":"2505.19443","last_updated":"2025-05-26T03:00:21Z","snapshot_observed_at":"2026-08-07T14:11:48.229560Z","submitted_at":"2025-05-26T03:00:21Z","title":"Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AI","version":1},"reference_index":154,"source":"pdf_text","source_observed_at":"2026-08-07T14:15:47.174246Z"},"links":{"cited_paper":"/paper/2501.06625","citing_paper":"/paper/2505.19443"},"observation_digest":"sha256:f60a82d3ae5834988805774222d78ffc96f81195058c8556fa93c5ab2fd81793","observation_id":"a8a9e21f-e885-47c5-b441-978dfe4c7a87","resolution":{"observed_at":"2026-08-07T14:15:57.073888Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2501.06625/citation-record","integrity":"/paper/2501.06625/integrity","json":"/paper/2501.06625/citation-record.json","paper":"/paper/2501.06625"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2309.16039","last_updated":"2023-11-14T01:40:13Z","snapshot_observed_at":"2026-08-16T14:57:02.846476Z","submitted_at":"2023-09-27T21:41:49Z","title":"Effective Long-Context Scaling of Foundation Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.16039","snapshot_observed_at":"2026-08-10T21:01:58.744944Z","title":"Effective Long-Context Scaling of Foundation Mod- els,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2501.06625","last_updated":"2025-01-11T19:21:53Z","snapshot_observed_at":"2026-08-15T11:11:55.610347Z","submitted_at":"2025-01-11T19:21:53Z","title":"Guided Code Generation with LLMs: A Multi-Agent Framework for Complex Code Tasks","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-10T21:01:58.744944Z"},"links":{"cited_paper":"/paper/2309.16039","citing_paper":"/paper/2501.06625"},"observation_digest":"sha256:6e3439d67fc1650912aa575781fe4e6a8343527559874844eb496ab06e344ee1","observation_id":"02fb6abf-b3da-4d9a-8409-20c689e484c8","resolution":{"observed_at":"2026-08-10T21:01:58.744944Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.02060","last_updated":"2024-06-12T02:46:16Z","snapshot_observed_at":"2026-08-18T10:06:55.154937Z","submitted_at":"2024-04-02T15:59:11Z","title":"Long-context LLMs Struggle with Long In-context Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.02060","snapshot_observed_at":"2026-08-10T21:01:58.751544Z","title":"Long-context LLMs Struggle with Long In-context Learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.06625","last_updated":"2025-01-11T19:21:53Z","snapshot_observed_at":"2026-08-15T11:11:55.610347Z","submitted_at":"2025-01-11T19:21:53Z","title":"Guided Code Generation with LLMs: A Multi-Agent Framework for Complex Code Tasks","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-10T21:01:58.751544Z"},"links":{"cited_paper":"/paper/2404.02060","citing_paper":"/paper/2501.06625"},"observation_digest":"sha256:d7cb8ecdef9015e3be25047fd4c422af82fa16d37a3d2462554385c7c9eb2295","observation_id":"a48ee7e6-5632-473f-a33b-689c00f1ecba","resolution":{"observed_at":"2026-08-10T21:01:58.751544Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.02414","last_updated":"2023-10-25T05:22:43Z","snapshot_observed_at":"2026-08-17T15:43:31.970425Z","submitted_at":"2022-10-05T17:34:44Z","title":"GLM-130B: An Open Bilingual Pre-trained Model","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.02414","snapshot_observed_at":"2026-08-10T21:01:58.756814Z","title":"GLM-130B: An Open Bilingual Pre-trained Model,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2501.06625","last_updated":"2025-01-11T19:21:53Z","snapshot_observed_at":"2026-08-15T11:11:55.610347Z","submitted_at":"2025-01-11T19:21:53Z","title":"Guided Code Generation with LLMs: A Multi-Agent Framework for Complex Code Tasks","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-10T21:01:58.756814Z"},"links":{"cited_paper":"/paper/2210.02414","citing_paper":"/paper/2501.06625"},"observation_digest":"sha256:36f41dc3baa476a3887a2c713b3b51671ae884d5e4a4111c7b7d38ea8517350a","observation_id":"fcd3e738-28e0-4adb-b206-938a881f2027","resolution":{"observed_at":"2026-08-10T21:01:58.756814Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.13753","last_updated":"2024-02-21T12:30:33Z","snapshot_observed_at":"2026-08-02T12:47:57.325302Z","submitted_at":"2024-02-21T12:30:33Z","title":"LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.13753","snapshot_observed_at":"2026-08-10T21:01:58.762144Z","title":"Longrope: Extending llm context window beyond 2 million tokens,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.06625","last_updated":"2025-01-11T19:21:53Z","snapshot_observed_at":"2026-08-15T11:11:55.610347Z","submitted_at":"2025-01-11T19:21:53Z","title":"Guided Code Generation with LLMs: A Multi-Agent Framework for Complex Code Tasks","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-10T21:01:58.762144Z"},"links":{"cited_paper":"/paper/2402.13753","citing_paper":"/paper/2501.06625"},"observation_digest":"sha256:3312cf02581b4160868da78dbc9050e848eb6557ada2a8b14d89f11c5a06798e","observation_id":"88bb9ee1-9aee-41de-ab67-74940e9e54b5","resolution":{"observed_at":"2026-08-10T21:01:58.762144Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.07143","last_updated":"2024-08-09T22:37:25Z","snapshot_observed_at":"2026-08-17T19:41:15.885170Z","submitted_at":"2024-04-10T16:18:42Z","title":"Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.07143","snapshot_observed_at":"2026-08-10T21:01:58.767024Z","title":"Leave no context behind: Ef- ficient infinite context transformers with infini-attention,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.06625","last_updated":"2025-01-11T19:21:53Z","snapshot_observed_at":"2026-08-15T11:11:55.610347Z","submitted_at":"2025-01-11T19:21:53Z","title":"Guided Code Generation with LLMs: A Multi-Agent Framework for Complex Code Tasks","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-10T21:01:58.767024Z"},"links":{"cited_paper":"/paper/2404.07143","citing_paper":"/paper/2501.06625"},"observation_digest":"sha256:4be07847989bd949eb1ebece102b56cff7df91d327302181e52538957f8e55a5","observation_id":"a1f15bd7-13e0-4f35-8ed6-346c68fec6ca","resolution":{"observed_at":"2026-08-10T21:01:58.767024Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.11230","last_updated":"2025-02-11T02:17:24Z","snapshot_observed_at":"2026-08-16T13:42:24.755270Z","submitted_at":"2024-06-17T05:54:06Z","title":"Multimodal Needle in a Haystack: Benchmarking Long-Context Capability of Multimodal Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.11230","snapshot_observed_at":"2026-08-10T21:01:58.772100Z","title":"Multimodal Needle in a Haystack: Benchmarking Long-Context Capability of Multimodal Large Language Models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.06625","last_updated":"2025-01-11T19:21:53Z","snapshot_observed_at":"2026-08-15T11:11:55.610347Z","submitted_at":"2025-01-11T19:21:53Z","title":"Guided Code Generation with LLMs: A Multi-Agent Framework for Complex Code Tasks","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-10T21:01:58.772100Z"},"links":{"cited_paper":"/paper/2406.11230","citing_paper":"/paper/2501.06625"},"observation_digest":"sha256:c63bcaf700b53875b0d3914a76548106b37342e77716dc8c54750dd12fb4228e","observation_id":"1f677568-3a6d-4fbf-8d4f-79e7123c80a1","resolution":{"observed_at":"2026-08-10T21:01:58.772100Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.02060","last_updated":"2024-06-12T02:46:16Z","snapshot_observed_at":"2026-08-18T10:06:55.154937Z","submitted_at":"2024-04-02T15:59:11Z","title":"Long-context LLMs Struggle with Long In-context Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.02060","snapshot_observed_at":"2026-08-10T21:01:58.777965Z","title":"Long- context llms struggle with long in-context learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2501.06625","last_updated":"2025-01-11T19:21:53Z","snapshot_observed_at":"2026-08-15T11:11:55.610347Z","submitted_at":"2025-01-11T19:21:53Z","title":"Guided Code Generation with LLMs: A Multi-Agent Framework for Complex Code Tasks","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-10T21:01:58.777965Z"},"links":{"cited_paper":"/paper/2404.02060","citing_paper":"/paper/2501.06625"},"observation_digest":"sha256:84f31931738b195c9162892518983d68f2d45bd2cfc12d38ad1e8e2b4c6c69f3","observation_id":"180839ec-bed8-4167-9fe1-9c1506c3e16c","resolution":{"observed_at":"2026-08-10T21:01:58.777965Z","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":"2026-08-10T21:01:59.021110Z","title":null,"venue":null,"work_id":"87b4b55a-d33c-47dd-9ce0-e1b7dadc0b4b","year":2024},"citing_paper":{"arxiv_id":"2501.06625","last_updated":"2025-01-11T19:21:53Z","snapshot_observed_at":"2026-08-15T11:11:55.610347Z","submitted_at":"2025-01-11T19:21:53Z","title":"Guided Code Generation with LLMs: A Multi-Agent Framework for Complex Code Tasks","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-10T21:01:58.783351Z"},"links":{"citing_paper":"/paper/2501.06625"},"observation_digest":"sha256:ee1133071d175c247a60552ef30760489e7a47dfd1c91703d593aa73a949346b","observation_id":"489f544c-57be-4c32-bf35-98d4ea4790c7","resolution":{"observed_at":"2026-08-10T21:01:59.025759Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2110.05877","last_updated":"2021-10-12T10:33:02Z","snapshot_observed_at":"2026-08-16T17:50:01.983447Z","submitted_at":"2021-10-12T10:33:02Z","title":"OpenHands: Making Sign Language Recognition Accessible with Pose-based Pretrained Models across Languages","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.05877","snapshot_observed_at":"2026-08-10T21:01:58.788036Z","title":null,"venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.06625","last_updated":"2025-01-11T19:21:53Z","snapshot_observed_at":"2026-08-15T11:11:55.610347Z","submitted_at":"2025-01-11T19:21:53Z","title":"Guided Code Generation with LLMs: A Multi-Agent Framework for Complex Code Tasks","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-10T21:01:58.788036Z"},"links":{"cited_paper":"/paper/2110.05877","citing_paper":"/paper/2501.06625"},"observation_digest":"sha256:b705c338d6baa941b38ae58ba758ba5bb8305a728fbff8cce977af3a3dca8458","observation_id":"62e886d5-ac71-4fc1-ba45-dca09a68a750","resolution":{"observed_at":"2026-08-10T21:01:58.788036Z","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":"2026-08-10T21:01:59.006949Z","title":"Teknium, J","venue":null,"work_id":"0de40af0-752f-42ae-bcd6-9d1d36d08933","year":null},"citing_paper":{"arxiv_id":"2501.06625","last_updated":"2025-01-11T19:21:53Z","snapshot_observed_at":"2026-08-15T11:11:55.610347Z","submitted_at":"2025-01-11T19:21:53Z","title":"Guided Code Generation with LLMs: A Multi-Agent Framework for Complex Code Tasks","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-10T21:01:58.793226Z"},"links":{"citing_paper":"/paper/2501.06625"},"observation_digest":"sha256:4c530918d93a192b2fba77424fff4a0e6659c2a4242fa75b912cc091efd3fccd","observation_id":"7ab2db0a-765f-4f19-8ee8-087755a28c63","resolution":{"observed_at":"2026-08-10T21:01:59.011274Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2107.03374","last_updated":"2021-07-14T17:16:02Z","snapshot_observed_at":"2026-08-08T11:58:24.516369Z","submitted_at":"2021-07-07T17:41:24Z","title":"Evaluating Large Language Models Trained on Code","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.03374","snapshot_observed_at":"2026-08-10T21:01:58.803237Z","title":"”Evaluating large language models trained on code,” in arXiv preprint arXiv:2107.03374, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2501.06625","last_updated":"2025-01-11T19:21:53Z","snapshot_observed_at":"2026-08-15T11:11:55.610347Z","submitted_at":"2025-01-11T19:21:53Z","title":"Guided Code Generation with LLMs: A Multi-Agent Framework for Complex Code Tasks","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-10T21:01:58.803237Z"},"links":{"cited_paper":"/paper/2107.03374","citing_paper":"/paper/2501.06625"},"observation_digest":"sha256:1663911473c9c795c902c6c25bd3c06a906373aa4ee36c6d0de0487c1e8edf2e","observation_id":"ef8a73f2-0a09-4e4e-af8e-69c451f4fde3","resolution":{"observed_at":"2026-08-10T21:01:58.803237Z","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":"2026-08-10T21:01:58.990042Z","title":"”Chain-of-thought prompting elicits reasoning in large lan- guage models,” in Advances in neural information processing systems, vol","venue":null,"work_id":"c224a80d-75c4-453c-8442-6e8eba5c50a2","year":2022},"citing_paper":{"arxiv_id":"2501.06625","last_updated":"2025-01-11T19:21:53Z","snapshot_observed_at":"2026-08-15T11:11:55.610347Z","submitted_at":"2025-01-11T19:21:53Z","title":"Guided Code Generation with LLMs: A Multi-Agent Framework for Complex Code Tasks","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-10T21:01:58.808095Z"},"links":{"citing_paper":"/paper/2501.06625"},"observation_digest":"sha256:aaa21afdb4a055727b65d606873c9118825de43df7773200b246848b9bc33146","observation_id":"20361c06-472f-4cc5-9d6e-29bc602088d3","resolution":{"observed_at":"2026-08-10T21:01:58.996433Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2408.11857","last_updated":"2024-08-15T20:17:33Z","snapshot_observed_at":"2026-08-17T06:16:11.738131Z","submitted_at":"2024-08-15T20:17:33Z","title":"Hermes 3 Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.11857","snapshot_observed_at":"2026-08-10T21:01:58.797785Z","title":"Available: https://arxiv.org/abs/2408.11857","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2501.06625","last_updated":"2025-01-11T19:21:53Z","snapshot_observed_at":"2026-08-15T11:11:55.610347Z","submitted_at":"2025-01-11T19:21:53Z","title":"Guided Code Generation with LLMs: A Multi-Agent Framework for Complex Code Tasks","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-10T21:01:58.797785Z"},"links":{"cited_paper":"/paper/2408.11857","citing_paper":"/paper/2501.06625"},"observation_digest":"sha256:c8ef927a440935d684d605dca89740df419a26c6808441e8560b6d21d4a98d85","observation_id":"4df555d4-4658-4aa2-b3ca-e49d9dffd877","resolution":{"observed_at":"2026-08-10T21:01:58.797785Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2501.06625","last_updated":"2025-01-11T19:21:53Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-15T11:11:55.610347Z","submitted_at":"2025-01-11T19:21:53Z","title":"Guided Code Generation with LLMs: A Multi-Agent Framework for Complex Code Tasks"},"reference_resolution":{"displayed":13,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":11,"verified_exact":0,"verified_fuzzy":2},"total_outbound_references":13},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 1 inbound Pith citation observation for arXiv:2501.06625."}