{"as_of":"2026-08-07T15:23:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:45e6fbfd0014fb05a72ac926a80b33f5f9452c0a55d428e8aa8f2f4d4f353995","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":9,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":9,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":9,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":9,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T21:01:24.803482Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":1,"source":"arxiv_reference","source_observed_at":"2026-08-05T02:28:24.338817Z","state":"measured"}],"external_citation_measurements":[{"count":2,"observed_at":"2026-08-05T02:28:24.338817Z","source":"arxiv_reference"}],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2403.12014","last_updated":"2024-07-12T17:39:19Z","snapshot_observed_at":"2026-08-06T02:16:18.368852Z","submitted_at":"2024-03-18T17:51:16Z","title":"EnvGen: Generating and Adapting Environments via LLMs for Training Embodied Agents","version":2},"cited_work":{"arxiv_id":"2403.12014","doi":"10.48550/arxiv.2403.12014","metadata_source":"arxiv_reference","pith_arxiv_id":"2403.12014","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Envgen: Generating and adapting environments via llms for training embodied agents","venue":"arXiv (Cornell University)","work_id":"0f1bcf7f-d84c-4525-ba1e-e933224a9131","year":2024},"citing_paper":{"arxiv_id":"2504.02605","last_updated":"2025-04-03T14:06:17Z","snapshot_observed_at":"2026-07-30T07:56:48.878578Z","submitted_at":"2025-04-03T14:06:17Z","title":"Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-05-16T06:48:50.416649Z"},"links":{"cited_paper":"/paper/2403.12014","citing_paper":"/paper/2504.02605"},"observation_digest":"sha256:9c8805ad34b42b16e6771981ce1fbdf415f20dcaf6348305f406723ba574eaf5","observation_id":"93b9ac54-2ea2-45c6-87d9-8ae1fce7c411","resolution":{"observed_at":"2026-05-16T06:48:50.480908Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.12014","last_updated":"2024-07-12T17:39:19Z","snapshot_observed_at":"2026-08-06T02:16:18.368852Z","submitted_at":"2024-03-18T17:51:16Z","title":"EnvGen: Generating and Adapting Environments via LLMs for Training Embodied Agents","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.12014","snapshot_observed_at":"2026-08-05T21:01:24.803482Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.09586","last_updated":"2025-08-20T07:50:49Z","snapshot_observed_at":"2026-08-07T12:33:53.736485Z","submitted_at":"2025-08-13T07:59:29Z","title":"EvoCurr: Self-evolving Curriculum with Behavior Code Generation for Complex Decision-making","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-05T21:01:24.803482Z"},"links":{"cited_paper":"/paper/2403.12014","citing_paper":"/paper/2508.09586"},"observation_digest":"sha256:6822bd7e01340e96b3feca1abf9d2b64643ab11e1c6ca8dc4e99a441d8744990","observation_id":"70bbe515-7436-4375-904f-07dd546abd96","resolution":{"observed_at":"2026-08-05T21:01:24.803482Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.12014","last_updated":"2024-07-12T17:39:19Z","snapshot_observed_at":"2026-08-06T02:16:18.368852Z","submitted_at":"2024-03-18T17:51:16Z","title":"EnvGen: Generating and Adapting Environments via LLMs for Training Embodied Agents","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.12014","snapshot_observed_at":"2026-08-05T14:49:53.402378Z","title":"Envgen: Generating and adapting environments via llms for training embodied agents, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.20818","last_updated":"2025-08-28T14:16:17Z","snapshot_observed_at":"2026-08-07T09:22:29.563628Z","submitted_at":"2025-08-28T14:16:17Z","title":"cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blending","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-05T14:49:53.402378Z"},"links":{"cited_paper":"/paper/2403.12014","citing_paper":"/paper/2508.20818"},"observation_digest":"sha256:6d7218739dbd55068b8fff107881a1409cf9819e9af77c5d6bb3f6dd40b566a4","observation_id":"b0527413-2068-46f6-847a-58ab26c5a296","resolution":{"observed_at":"2026-08-05T14:49:53.402378Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.12014","last_updated":"2024-07-12T17:39:19Z","snapshot_observed_at":"2026-08-06T02:16:18.368852Z","submitted_at":"2024-03-18T17:51:16Z","title":"EnvGen: Generating and Adapting Environments via LLMs for Training Embodied Agents","version":2},"cited_work":{"arxiv_id":"2403.12014","doi":"10.48550/arxiv.2403.12014","metadata_source":"arxiv_reference","pith_arxiv_id":"2403.12014","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Envgen: Generating and adapting environments via llms for training embodied agents","venue":"arXiv (Cornell University)","work_id":"0f1bcf7f-d84c-4525-ba1e-e933224a9131","year":2024},"citing_paper":{"arxiv_id":"2604.18292","last_updated":"2026-04-20T14:01:10Z","snapshot_observed_at":"2026-07-06T23:05:13.178333Z","submitted_at":"2026-04-20T14:01:10Z","title":"Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence","version":1},"reference_index":125,"source":"pdf_text","source_observed_at":"2026-05-10T05:24:00.503836Z"},"links":{"cited_paper":"/paper/2403.12014","citing_paper":"/paper/2604.18292"},"observation_digest":"sha256:4b1aad3a2516aa4219a64e315fc6f4b1bb7c01e0aa326f5f8c7f70e1dbf8b2b7","observation_id":"a64c9274-d4e9-4aac-81ae-9acba0f664f6","resolution":{"observed_at":"2026-05-10T05:25:54.631848Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.12014","last_updated":"2024-07-12T17:39:19Z","snapshot_observed_at":"2026-08-06T02:16:18.368852Z","submitted_at":"2024-03-18T17:51:16Z","title":"EnvGen: Generating and Adapting Environments via LLMs for Training Embodied Agents","version":2},"cited_work":{"arxiv_id":"2403.12014","doi":"10.48550/arxiv.2403.12014","metadata_source":"arxiv_reference","pith_arxiv_id":"2403.12014","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Envgen: Generating and adapting environments via llms for training embodied agents","venue":"arXiv (Cornell University)","work_id":"0f1bcf7f-d84c-4525-ba1e-e933224a9131","year":2024},"citing_paper":{"arxiv_id":"2605.09423","last_updated":"2026-05-13T08:34:40Z","snapshot_observed_at":"2026-07-31T05:53:49.791813Z","submitted_at":"2026-05-10T08:51:50Z","title":"SimWorld Studio: Automatic Environment Generation with Evolving Coding Agent for Embodied Agent Learning","version":1},"reference_index":101,"source":"pdf_text","source_observed_at":"2026-05-12T04:21:44.087943Z"},"links":{"cited_paper":"/paper/2403.12014","citing_paper":"/paper/2605.09423"},"observation_digest":"sha256:2d72740188a7963e274171e6438aa85d05e2c95e72daa284f5ae5aea00f39dfd","observation_id":"66284ac8-c86b-4115-b2a6-91eff8c20d55","resolution":{"observed_at":"2026-05-12T06:21:26.467477Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.12014","last_updated":"2024-07-12T17:39:19Z","snapshot_observed_at":"2026-08-06T02:16:18.368852Z","submitted_at":"2024-03-18T17:51:16Z","title":"EnvGen: Generating and Adapting Environments via LLMs for Training Embodied Agents","version":2},"cited_work":{"arxiv_id":"2403.12014","doi":"10.48550/arxiv.2403.12014","metadata_source":"arxiv_reference","pith_arxiv_id":"2403.12014","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Envgen: Generating and adapting environments via llms for training embodied agents","venue":"arXiv (Cornell University)","work_id":"0f1bcf7f-d84c-4525-ba1e-e933224a9131","year":2024},"citing_paper":{"arxiv_id":"2605.09423","last_updated":"2026-05-13T08:34:40Z","snapshot_observed_at":"2026-07-31T05:53:49.791813Z","submitted_at":"2026-05-10T08:51:50Z","title":"SimWorld Studio: Automatic Environment Generation with Evolving Coding Agent for Embodied Agent Learning","version":2},"reference_index":101,"source":"pdf_text","source_observed_at":"2026-05-14T21:30:42.766390Z"},"links":{"cited_paper":"/paper/2403.12014","citing_paper":"/paper/2605.09423"},"observation_digest":"sha256:7a82211ab30d64fdf7780fd9d77028d83679308dc149de967ebdfb6b70e0471a","observation_id":"fa41e90f-461b-43f4-b12c-fd2ff15fb620","resolution":{"observed_at":"2026-05-14T21:32:59.636633Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.12014","last_updated":"2024-07-12T17:39:19Z","snapshot_observed_at":"2026-08-06T02:16:18.368852Z","submitted_at":"2024-03-18T17:51:16Z","title":"EnvGen: Generating and Adapting Environments via LLMs for Training Embodied Agents","version":2},"cited_work":{"arxiv_id":"2403.12014","doi":"10.48550/arxiv.2403.12014","metadata_source":"arxiv_reference","pith_arxiv_id":"2403.12014","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Envgen: Generating and adapting environments via llms for training embodied agents","venue":"arXiv (Cornell University)","work_id":"0f1bcf7f-d84c-4525-ba1e-e933224a9131","year":2024},"citing_paper":{"arxiv_id":"2605.29486","last_updated":"2026-05-28T07:14:15Z","snapshot_observed_at":"2026-07-06T23:38:56.014434Z","submitted_at":"2026-05-28T07:14:15Z","title":"PhoneWorld: Scaling Phone-Use Agent Environments","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-06-29T07:41:20.001845Z"},"links":{"cited_paper":"/paper/2403.12014","citing_paper":"/paper/2605.29486"},"observation_digest":"sha256:65f28edf8d2c12b24084342a850da5840f2328fcd05b126a776a84ebc63aeca3","observation_id":"7899d0f8-2023-40ab-896d-4d2f44f7a3fe","resolution":{"observed_at":"2026-06-29T07:43:13.450816Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.12014","last_updated":"2024-07-12T17:39:19Z","snapshot_observed_at":"2026-08-06T02:16:18.368852Z","submitted_at":"2024-03-18T17:51:16Z","title":"EnvGen: Generating and Adapting Environments via LLMs for Training Embodied Agents","version":2},"cited_work":{"arxiv_id":"2403.12014","doi":"10.48550/arxiv.2403.12014","metadata_source":"arxiv_reference","pith_arxiv_id":"2403.12014","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Envgen: Generating and adapting environments via llms for training embodied agents","venue":"arXiv (Cornell University)","work_id":"0f1bcf7f-d84c-4525-ba1e-e933224a9131","year":2024},"citing_paper":{"arxiv_id":"2606.12191","last_updated":"2026-06-10T15:15:01Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-06-10T15:15:01Z","title":"Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application","version":1},"reference_index":175,"source":"pdf_text","source_observed_at":"2026-06-27T09:46:30.702256Z"},"links":{"cited_paper":"/paper/2403.12014","citing_paper":"/paper/2606.12191"},"observation_digest":"sha256:4995e6bcc996eb6280a73bf04dcbdc90af3c3bd7153b9941a338d9ad9648e67e","observation_id":"a8e166e6-e4ad-4897-8dd6-1a3a5dafdf44","resolution":{"observed_at":"2026-06-27T09:50:48.420169Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2403.12014","last_updated":"2024-07-12T17:39:19Z","snapshot_observed_at":"2026-08-06T02:16:18.368852Z","submitted_at":"2024-03-18T17:51:16Z","title":"EnvGen: Generating and Adapting Environments via LLMs for Training Embodied Agents","version":2},"cited_work":{"arxiv_id":"2403.12014","doi":"10.48550/arxiv.2403.12014","metadata_source":"arxiv_reference","pith_arxiv_id":"2403.12014","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Envgen: Generating and adapting environments via llms for training embodied agents","venue":"arXiv (Cornell University)","work_id":"0f1bcf7f-d84c-4525-ba1e-e933224a9131","year":2024},"citing_paper":{"arxiv_id":"2606.23049","last_updated":"2026-06-24T02:20:35Z","snapshot_observed_at":"2026-07-06T23:57:53.101659Z","submitted_at":"2026-06-22T08:57:54Z","title":"PhoneBuddy: Training Open Models for Agentic Phone Use","version":2},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-06-26T08:15:49.428124Z"},"links":{"cited_paper":"/paper/2403.12014","citing_paper":"/paper/2606.23049"},"observation_digest":"sha256:2662dd9e8bbc16b8abcd506f6dbc20313ee470e2ad11fbdcd08a651eb61e413f","observation_id":"373f323e-71ed-47fe-b1ef-ce260a5f940a","resolution":{"observed_at":"2026-07-04T10:59:46.541174Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2403.12014/citation-record","integrity":"/paper/2403.12014/integrity","json":"/paper/2403.12014/citation-record.json","paper":"/paper/2403.12014"},"outbound":[],"paper":{"arxiv_id":"2403.12014","last_updated":"2024-07-12T17:39:19Z","latest_version":2,"primary_category":"cs.CL","snapshot_observed_at":"2026-08-06T02:16:18.368852Z","submitted_at":"2024-03-18T17:51:16Z","title":"EnvGen: Generating and Adapting Environments via LLMs for Training Embodied Agents"},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2403.12014."}