{"as_of":"2026-08-18T08:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:432e6198c8000fb86c79a44df335788ded541f2904ce6ab1d522f42c472c3259","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":15,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":15,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":15,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":15,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T14:23:51.585374Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-01T13:45:45.815030Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2601.22758","last_updated":"2026-08-10T06:51:25Z","snapshot_observed_at":"2026-08-18T04:14:55.458259Z","submitted_at":"2026-01-30T09:33:30Z","title":"AutoRefine: Compiling Trajectories into Validated Typed Agent Artifacts","version":2},"cited_work":{"arxiv_id":"2601.22758","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2601.22758","snapshot_observed_at":"2026-08-11T03:20:49.077381Z","title":"Autorefine: From trajectories to reusable expertise for continual llm agent refinement","venue":null,"work_id":"00a3a850-94c3-40c7-bc56-56001745f1cb","year":2026},"citing_paper":{"arxiv_id":"2604.15097","last_updated":"2026-06-02T07:26:24Z","snapshot_observed_at":"2026-08-12T13:14:39.498030Z","submitted_at":"2026-04-16T14:55:49Z","title":"From Procedural Skills to Strategy Genes: Towards Experience-Driven Test-Time Evolution","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-05-10T10:52:47.760627Z"},"links":{"cited_paper":"/paper/2601.22758","citing_paper":"/paper/2604.15097"},"observation_digest":"sha256:e3327f2d8f9a33c13287e211cf48942b449130f88be5246a391bc9e88ddf193a","observation_id":"c9331e94-b416-45ad-9b68-76562fee84ea","resolution":{"observed_at":"2026-08-11T03:20:49.077381Z","resolver_source":"arxiv_id","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"}},{"citation":{"cited_paper":{"arxiv_id":"2601.22758","last_updated":"2026-08-10T06:51:25Z","snapshot_observed_at":"2026-08-18T04:14:55.458259Z","submitted_at":"2026-01-30T09:33:30Z","title":"AutoRefine: Compiling Trajectories into Validated Typed Agent Artifacts","version":2},"cited_work":{"arxiv_id":"2601.22758","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2601.22758","snapshot_observed_at":"2026-08-11T03:20:49.077381Z","title":"Autorefine: From trajectories to reusable expertise for continual llm agent refinement","venue":null,"work_id":"00a3a850-94c3-40c7-bc56-56001745f1cb","year":2026},"citing_paper":{"arxiv_id":"2604.27660","last_updated":"2026-07-27T14:08:20Z","snapshot_observed_at":"2026-08-16T12:00:23.928463Z","submitted_at":"2026-04-30T09:53:15Z","title":"From Context to Skills: Can Language Models Learn from Context Skillfully?","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-05-07T06:31:51.474951Z"},"links":{"cited_paper":"/paper/2601.22758","citing_paper":"/paper/2604.27660"},"observation_digest":"sha256:a9bb4026360d12bb5b8518bf674840fad1cca48f3a9c4e305f826e72e414c04d","observation_id":"906304c4-3b87-4f63-8ece-9428871cf544","resolution":{"observed_at":"2026-08-11T03:20:49.077381Z","resolver_source":"arxiv_id","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"}},{"citation":{"cited_paper":{"arxiv_id":"2601.22758","last_updated":"2026-08-10T06:51:25Z","snapshot_observed_at":"2026-08-18T04:14:55.458259Z","submitted_at":"2026-01-30T09:33:30Z","title":"AutoRefine: Compiling Trajectories into Validated Typed Agent Artifacts","version":2},"cited_work":{"arxiv_id":"2601.22758","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2601.22758","snapshot_observed_at":"2026-08-11T03:20:49.077381Z","title":"Autorefine: From trajectories to reusable expertise for continual llm agent refinement","venue":null,"work_id":"00a3a850-94c3-40c7-bc56-56001745f1cb","year":2026},"citing_paper":{"arxiv_id":"2605.02913","last_updated":"2026-04-08T00:53:29Z","snapshot_observed_at":"2026-08-13T10:36:16.468710Z","submitted_at":"2026-04-08T00:53:29Z","title":"Generate, Filter, Control, Replay: A Comprehensive Survey of Rollout Strategies for LLM Reinforcement Learning","version":1},"reference_index":94,"source":"arxiv_source","source_observed_at":"2026-05-10T19:15:27.406778Z"},"links":{"cited_paper":"/paper/2601.22758","citing_paper":"/paper/2605.02913"},"observation_digest":"sha256:f82867f6b068e5fbe9006e055ed12bbd7dcee7b18d67f12a27265a2fe605d280","observation_id":"06163675-5218-408d-9db5-b0acae6bbf17","resolution":{"observed_at":"2026-08-11T03:20:49.077381Z","resolver_source":"arxiv_id","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"}},{"citation":{"cited_paper":{"arxiv_id":"2601.22758","last_updated":"2026-08-10T06:51:25Z","snapshot_observed_at":"2026-08-18T04:14:55.458259Z","submitted_at":"2026-01-30T09:33:30Z","title":"AutoRefine: Compiling Trajectories into Validated Typed Agent Artifacts","version":2},"cited_work":{"arxiv_id":"2601.22758","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2601.22758","snapshot_observed_at":"2026-08-11T03:20:49.077381Z","title":"Autorefine: From trajectories to reusable expertise for continual llm agent refinement","venue":null,"work_id":"00a3a850-94c3-40c7-bc56-56001745f1cb","year":2026},"citing_paper":{"arxiv_id":"2605.07358","last_updated":"2026-05-26T05:21:04Z","snapshot_observed_at":"2026-07-06T23:19:46.018236Z","submitted_at":"2026-05-08T07:10:26Z","title":"A Comprehensive Survey on Agent Skills: Taxonomy, Techniques, and Applications","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-05-11T01:47:39.926540Z"},"links":{"cited_paper":"/paper/2601.22758","citing_paper":"/paper/2605.07358"},"observation_digest":"sha256:2ab25be264420fb6d8a35fbef516d826dd805b98d42b934237f0e44a43e69f6e","observation_id":"4a51f0ca-0317-446d-bf76-89900003a927","resolution":{"observed_at":"2026-08-11T03:20:49.077381Z","resolver_source":"arxiv_id","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"}},{"citation":{"cited_paper":{"arxiv_id":"2601.22758","last_updated":"2026-08-10T06:51:25Z","snapshot_observed_at":"2026-08-18T04:14:55.458259Z","submitted_at":"2026-01-30T09:33:30Z","title":"AutoRefine: Compiling Trajectories into Validated Typed Agent Artifacts","version":2},"cited_work":{"arxiv_id":"2601.22758","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2601.22758","snapshot_observed_at":"2026-08-11T03:20:49.077381Z","title":"Autorefine: From trajectories to reusable expertise for continual llm agent refinement","venue":null,"work_id":"00a3a850-94c3-40c7-bc56-56001745f1cb","year":2026},"citing_paper":{"arxiv_id":"2605.07358","last_updated":"2026-05-26T05:21:04Z","snapshot_observed_at":"2026-07-06T23:19:46.018236Z","submitted_at":"2026-05-08T07:10:26Z","title":"A Comprehensive Survey on Agent Skills: Taxonomy, Techniques, and Applications","version":2},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-05-20T23:15:44.550045Z"},"links":{"cited_paper":"/paper/2601.22758","citing_paper":"/paper/2605.07358"},"observation_digest":"sha256:258b3cb1d8d599fd925b79a8b7377e5c59945b66ba64c6bf859d8d6e23719fd0","observation_id":"06921339-eb96-4c38-8824-8871be3dda3c","resolution":{"observed_at":"2026-08-11T03:20:49.077381Z","resolver_source":"arxiv_id","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"}},{"citation":{"cited_paper":{"arxiv_id":"2601.22758","last_updated":"2026-08-10T06:51:25Z","snapshot_observed_at":"2026-08-18T04:14:55.458259Z","submitted_at":"2026-01-30T09:33:30Z","title":"AutoRefine: Compiling Trajectories into Validated Typed Agent Artifacts","version":2},"cited_work":{"arxiv_id":"2601.22758","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2601.22758","snapshot_observed_at":"2026-08-11T03:20:49.077381Z","title":"Autorefine: From trajectories to reusable expertise for continual llm agent refinement","venue":null,"work_id":"00a3a850-94c3-40c7-bc56-56001745f1cb","year":2026},"citing_paper":{"arxiv_id":"2605.07358","last_updated":"2026-05-26T05:21:04Z","snapshot_observed_at":"2026-07-06T23:19:46.018236Z","submitted_at":"2026-05-08T07:10:26Z","title":"A Comprehensive Survey on Agent Skills: Taxonomy, Techniques, and Applications","version":3},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-06-30T23:23:42.883286Z"},"links":{"cited_paper":"/paper/2601.22758","citing_paper":"/paper/2605.07358"},"observation_digest":"sha256:e3bf2d2c27b43da194e1aac57a54537397738982a5a4c76c9bb6a1660d31d884","observation_id":"f738a476-5034-43b4-ac6d-c5d09aa8fd7f","resolution":{"observed_at":"2026-08-11T03:20:49.077381Z","resolver_source":"arxiv_id","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"}},{"citation":{"cited_paper":{"arxiv_id":"2601.22758","last_updated":"2026-08-10T06:51:25Z","snapshot_observed_at":"2026-08-18T04:14:55.458259Z","submitted_at":"2026-01-30T09:33:30Z","title":"AutoRefine: Compiling Trajectories into Validated Typed Agent Artifacts","version":2},"cited_work":{"arxiv_id":"2601.22758","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2601.22758","snapshot_observed_at":"2026-08-11T03:20:49.077381Z","title":"Autorefine: From trajectories to reusable expertise for continual llm agent refinement","venue":null,"work_id":"00a3a850-94c3-40c7-bc56-56001745f1cb","year":2026},"citing_paper":{"arxiv_id":"2605.09192","last_updated":"2026-06-03T22:42:35Z","snapshot_observed_at":"2026-08-17T11:47:52.408981Z","submitted_at":"2026-05-09T22:15:13Z","title":"Evidence Over Plans: Online Trajectory Verification for Skill Distillation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-12T02:37:27.532261Z"},"links":{"cited_paper":"/paper/2601.22758","citing_paper":"/paper/2605.09192"},"observation_digest":"sha256:47b25b5d2ac50d877f6cea8096c9666c6bc255614e71e110b7112b7c9a27da73","observation_id":"603146cd-da22-4718-9a64-3bcdf445d23a","resolution":{"observed_at":"2026-08-11T03:20:49.077381Z","resolver_source":"arxiv_id","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"}},{"citation":{"cited_paper":{"arxiv_id":"2601.22758","last_updated":"2026-08-10T06:51:25Z","snapshot_observed_at":"2026-08-18T04:14:55.458259Z","submitted_at":"2026-01-30T09:33:30Z","title":"AutoRefine: Compiling Trajectories into Validated Typed Agent Artifacts","version":2},"cited_work":{"arxiv_id":"2601.22758","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2601.22758","snapshot_observed_at":"2026-08-11T03:20:49.077381Z","title":"Autorefine: From trajectories to reusable expertise for continual llm agent refinement","venue":null,"work_id":"00a3a850-94c3-40c7-bc56-56001745f1cb","year":2026},"citing_paper":{"arxiv_id":"2605.09192","last_updated":"2026-06-03T22:42:35Z","snapshot_observed_at":"2026-08-17T11:47:52.408981Z","submitted_at":"2026-05-09T22:15:13Z","title":"Evidence Over Plans: Online Trajectory Verification for Skill Distillation","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-30T22:49:45.965536Z"},"links":{"cited_paper":"/paper/2601.22758","citing_paper":"/paper/2605.09192"},"observation_digest":"sha256:1494056a4b373c12e1e256246bd9b5b9ad902bca3f95038964529a0ba4749e4f","observation_id":"23e4b742-c8ff-441d-961e-a553d1290493","resolution":{"observed_at":"2026-08-11T03:20:49.077381Z","resolver_source":"arxiv_id","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"}},{"citation":{"cited_paper":{"arxiv_id":"2601.22758","last_updated":"2026-08-10T06:51:25Z","snapshot_observed_at":"2026-08-18T04:14:55.458259Z","submitted_at":"2026-01-30T09:33:30Z","title":"AutoRefine: Compiling Trajectories into Validated Typed Agent Artifacts","version":2},"cited_work":{"arxiv_id":"2601.22758","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2601.22758","snapshot_observed_at":"2026-08-11T03:20:49.077381Z","title":"Autorefine: From trajectories to reusable expertise for continual llm agent refinement","venue":null,"work_id":"00a3a850-94c3-40c7-bc56-56001745f1cb","year":2026},"citing_paper":{"arxiv_id":"2605.14477","last_updated":"2026-07-14T21:30:34Z","snapshot_observed_at":"2026-08-12T13:34:59.214274Z","submitted_at":"2026-05-14T07:18:12Z","title":"Test-Time Learning with an Evolving Library","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-05-15T01:44:16.865595Z"},"links":{"cited_paper":"/paper/2601.22758","citing_paper":"/paper/2605.14477"},"observation_digest":"sha256:2fb183be1ebc2cbba72862581d1a4c59e2c73f6fe53c7348e7715ac11aba9ed9","observation_id":"f647e368-8670-4f50-b91a-7edcd993c035","resolution":{"observed_at":"2026-08-11T03:20:49.077381Z","resolver_source":"arxiv_id","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"}},{"citation":{"cited_paper":{"arxiv_id":"2601.22758","last_updated":"2026-08-10T06:51:25Z","snapshot_observed_at":"2026-08-18T04:14:55.458259Z","submitted_at":"2026-01-30T09:33:30Z","title":"AutoRefine: Compiling Trajectories into Validated Typed Agent Artifacts","version":2},"cited_work":{"arxiv_id":"2601.22758","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2601.22758","snapshot_observed_at":"2026-08-11T03:20:49.077381Z","title":"Autorefine: From trajectories to reusable expertise for continual llm agent refinement","venue":null,"work_id":"00a3a850-94c3-40c7-bc56-56001745f1cb","year":2026},"citing_paper":{"arxiv_id":"2605.23899","last_updated":"2026-05-22T17:59:12Z","snapshot_observed_at":"2026-08-15T12:06:11.970472Z","submitted_at":"2026-05-22T17:59:12Z","title":"From Raw Experience to Skill Consumption: A Systematic Study of Model-Generated Agent Skills","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-05-25T03:53:56.643075Z"},"links":{"cited_paper":"/paper/2601.22758","citing_paper":"/paper/2605.23899"},"observation_digest":"sha256:4c8ae17f86614b438900951884db2179479a89616fd2ae676d00afd803914339","observation_id":"343db574-d642-4a6b-b835-cc3d0f7a56fa","resolution":{"observed_at":"2026-08-11T03:20:49.077381Z","resolver_source":"arxiv_id","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"}},{"citation":{"cited_paper":{"arxiv_id":"2601.22758","last_updated":"2026-08-10T06:51:25Z","snapshot_observed_at":"2026-08-18T04:14:55.458259Z","submitted_at":"2026-01-30T09:33:30Z","title":"AutoRefine: Compiling Trajectories into Validated Typed Agent Artifacts","version":2},"cited_work":{"arxiv_id":"2601.22758","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2601.22758","snapshot_observed_at":"2026-08-11T03:20:49.077381Z","title":"Autorefine: From trajectories to reusable expertise for continual llm agent refinement","venue":null,"work_id":"00a3a850-94c3-40c7-bc56-56001745f1cb","year":2026},"citing_paper":{"arxiv_id":"2605.23904","last_updated":"2026-05-25T17:58:16Z","snapshot_observed_at":"2026-08-17T09:03:05.421842Z","submitted_at":"2026-05-22T17:59:50Z","title":"SkillOpt: Executive Strategy for Self-Evolving Agent Skills","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-05-25T03:51:49.812710Z"},"links":{"cited_paper":"/paper/2601.22758","citing_paper":"/paper/2605.23904"},"observation_digest":"sha256:4495b5cd7526521b319b320ee4eefe50967b0912bf64e8f9728ae525bda06c2c","observation_id":"d5e663c1-1a37-4632-94ea-6147ef53e69d","resolution":{"observed_at":"2026-08-11T03:20:49.077381Z","resolver_source":"arxiv_id","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"}},{"citation":{"cited_paper":{"arxiv_id":"2601.22758","last_updated":"2026-08-10T06:51:25Z","snapshot_observed_at":"2026-08-18T04:14:55.458259Z","submitted_at":"2026-01-30T09:33:30Z","title":"AutoRefine: Compiling Trajectories into Validated Typed Agent Artifacts","version":2},"cited_work":{"arxiv_id":"2601.22758","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2601.22758","snapshot_observed_at":"2026-08-11T03:20:49.077381Z","title":"Autorefine: From trajectories to reusable expertise for continual llm agent refinement","venue":null,"work_id":"00a3a850-94c3-40c7-bc56-56001745f1cb","year":2026},"citing_paper":{"arxiv_id":"2605.23904","last_updated":"2026-05-25T17:58:16Z","snapshot_observed_at":"2026-08-17T09:03:05.421842Z","submitted_at":"2026-05-22T17:59:50Z","title":"SkillOpt: Executive Strategy for Self-Evolving Agent Skills","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-06-30T16:26:23.130418Z"},"links":{"cited_paper":"/paper/2601.22758","citing_paper":"/paper/2605.23904"},"observation_digest":"sha256:c63a8f99d2e0caf0042a9b57aaea83d71c546c71c3726a3c74ed543a953e8723","observation_id":"1cee8527-bceb-491a-bd7b-9affaf57b4d7","resolution":{"observed_at":"2026-08-11T03:20:49.077381Z","resolver_source":"arxiv_id","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"}},{"citation":{"cited_paper":{"arxiv_id":"2601.22758","last_updated":"2026-08-10T06:51:25Z","snapshot_observed_at":"2026-08-18T04:14:55.458259Z","submitted_at":"2026-01-30T09:33:30Z","title":"AutoRefine: Compiling Trajectories into Validated Typed Agent Artifacts","version":2},"cited_work":{"arxiv_id":"2601.22758","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2601.22758","snapshot_observed_at":"2026-08-11T03:20:49.077381Z","title":"Autorefine: From trajectories to reusable expertise for continual llm agent refinement","venue":null,"work_id":"00a3a850-94c3-40c7-bc56-56001745f1cb","year":2026},"citing_paper":{"arxiv_id":"2606.29315","last_updated":"2026-06-28T10:21:55Z","snapshot_observed_at":"2026-08-05T11:59:47.223958Z","submitted_at":"2026-06-28T10:21:55Z","title":"Hierarchical Experimentalist Agents","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-06-30T07:29:24.656530Z"},"links":{"cited_paper":"/paper/2601.22758","citing_paper":"/paper/2606.29315"},"observation_digest":"sha256:9f8b8fe5f6baa07f842600e8d3f1e295888a383676ea8d9436d8d74a0c75bf58","observation_id":"01ac3430-533a-4f0d-a069-693f0114e2ee","resolution":{"observed_at":"2026-08-11T03:20:49.077381Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"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":"2601.22758","last_updated":"2026-08-10T06:51:25Z","snapshot_observed_at":"2026-08-18T04:14:55.458259Z","submitted_at":"2026-01-30T09:33:30Z","title":"AutoRefine: Compiling Trajectories into Validated Typed Agent Artifacts","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2601.22758","snapshot_observed_at":"2026-08-14T04:13:56.687582Z","title":"Autorefine: From trajectories to reusable expertise for continual llm agent refinement.arXiv preprint arXiv:2601.22758,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.10319","last_updated":"2026-08-15T06:04:23Z","snapshot_observed_at":"2026-08-18T08:11:58.454109Z","submitted_at":"2026-08-10T23:41:02Z","title":"Do Personalized Skills Help Coding Agents? An Empirical Study of Developer Interaction Histories","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T04:13:56.687582Z"},"links":{"cited_paper":"/paper/2601.22758","citing_paper":"/paper/2608.10319"},"observation_digest":"sha256:385335540ab42a7b26116b96fb6c7f78ab026a37216d725cbd86e7841a95e429","observation_id":"5f592681-4399-49b4-be51-8a776eb79376","resolution":{"observed_at":"2026-08-14T04:13:56.687582Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2601.22758","last_updated":"2026-08-10T06:51:25Z","snapshot_observed_at":"2026-08-18T04:14:55.458259Z","submitted_at":"2026-01-30T09:33:30Z","title":"AutoRefine: Compiling Trajectories into Validated Typed Agent Artifacts","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2601.22758","snapshot_observed_at":"2026-08-15T14:23:51.585374Z","title":"arXiv:2601.22758","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.10538","last_updated":"2026-08-14T06:10:28Z","snapshot_observed_at":"2026-08-18T08:09:38.175444Z","submitted_at":"2026-08-11T06:22:02Z","title":"SKILLER: Language-Level Reinforcement Learning for Reusable Skill Extraction in Small Language Models","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T14:23:51.585374Z"},"links":{"cited_paper":"/paper/2601.22758","citing_paper":"/paper/2608.10538"},"observation_digest":"sha256:a822f44113f98fd866220f5a69dee1f2da06a05ac5b51659423924150aedcac6","observation_id":"0137beb0-40c8-45a8-bdf0-efd4ed5e437e","resolution":{"observed_at":"2026-08-15T14:23:51.585374Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2601.22758/citation-record","integrity":"/paper/2601.22758/integrity","json":"/paper/2601.22758/citation-record.json","paper":"/paper/2601.22758"},"outbound":[],"paper":{"arxiv_id":"2601.22758","last_updated":"2026-08-10T06:51:25Z","latest_version":2,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-18T04:14:55.458259Z","submitted_at":"2026-01-30T09:33:30Z","title":"AutoRefine: Compiling Trajectories into Validated Typed Agent Artifacts"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"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 0 of 0 outbound references and 15 inbound Pith citation observations for arXiv:2601.22758."}