{"as_of":"2026-08-06T01:51:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:194c4e8ba644f89fe355f07f1c5174eac1b6e849541c2dd5137519c1e778cd69","coverage":[{"denominator":177,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T03:38:25.069775Z","state":"measured"},{"denominator":100,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":100,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-05T06:32:48.257954+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2607.23124/citation-record","integrity":"/paper/2607.23124/integrity","json":"/paper/2607.23124/citation-record.json","paper":"/paper/2607.23124"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-01T03:38:13.496201Z","title":"ReAct: Synergizing reasoning and acting in language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:13.496201Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:bba92425db1edf59dc3ede724e4e1774448ae57363cc4a566bbf7fe1edd45a4a","observation_id":"b0b46990-f0b4-41ba-953d-f508e842a4ab","resolution":{"observed_at":"2026-08-01T03:38:13.496201Z","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":"2026-08-01T03:38:13.606154Z","title":"Toolformer: Language models can teach themselves to use tools","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:13.606154Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:1da23811723108045a91ef1e74c38e99f862b3169ce81c5524b51fcb1c01d092","observation_id":"c162e5f0-028f-48c7-b1a4-74ad6b27c090","resolution":{"observed_at":"2026-08-01T03:38:13.606154Z","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":"2026-08-01T03:38:13.770696Z","title":"ToolLLM: Facilitating large language models to master 16000+ real-world APIs","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:13.770696Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:ddf48efd05c6d782ad851945e4dd7b167fbfb8a3879f871097b77584df4a32bf","observation_id":"8b9fee35-ceaf-4190-9c58-8a1681dd2817","resolution":{"observed_at":"2026-08-01T03:38:13.770696Z","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":"2026-08-01T03:38:13.942472Z","title":"Patil, Tianjun Zhang, Xin Wang, and Joseph E","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:13.942472Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:59da9aaaee69f7b1ce69faf80978e3c1d4e1f41d69e172af2706583f690bc41b","observation_id":"72be1729-e266-4c5b-bc66-5ac7c0c6902b","resolution":{"observed_at":"2026-08-01T03:38:13.942472Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.10775","last_updated":"2023-11-15T23:50:31Z","snapshot_observed_at":"2026-07-06T16:49:13.099946Z","submitted_at":"2023-11-15T23:50:31Z","title":"ToolTalk: Evaluating Tool-Usage in a Conversational Setting","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.10775","snapshot_observed_at":"2026-08-01T03:38:14.110286Z","title":"Tooltalk: Evaluating tool-usage in a conversational setting.CoRR, abs/2311.10775, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:14.110286Z"},"links":{"cited_paper":"/paper/2311.10775","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:04ef29a6761ca9b9c4b3a17a16444fc0acde0a82128b8a94612a4457c29b81a6","observation_id":"74fe678d-b19d-406a-ad87-d5cfacca27cc","resolution":{"observed_at":"2026-08-01T03:38:14.110286Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2304.08244","last_updated":"2023-10-25T06:54:12Z","snapshot_observed_at":"2026-08-02T00:07:12.855748Z","submitted_at":"2023-04-14T14:05:32Z","title":"API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.08244","snapshot_observed_at":"2026-08-01T03:38:14.153015Z","title":"API-Bank: A comprehensive benchmark for tool-augmented LLMs.arXiv preprint arXiv:2304.08244, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:14.153015Z"},"links":{"cited_paper":"/paper/2304.08244","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:3692033629532793896b1afa55c05ae185aab00eb8c314cb8757b012af4a4d6f","observation_id":"ceb3b68c-bc60-4a3a-9cb4-8172151e9d01","resolution":{"observed_at":"2026-08-01T03:38:14.153015Z","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":"2026-08-01T03:38:14.199472Z","title":"AgentBench: Evaluating LLMs as agents","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:14.199472Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:68fc1c837e6de34291e04b5d9e9199fc7f63200dd76bd1611e0afbf5b2e56113","observation_id":"3e5136d4-e21a-434a-804b-8516150b1205","resolution":{"observed_at":"2026-08-01T03:38:14.199472Z","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":"2026-08-01T03:38:14.251202Z","title":"WebArena: A realistic web environment for building autonomous agents","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:14.251202Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:190a9e5c24c3526bdaaf481ad6f5c3bae4bdcce5b1099372b10bb38bffd570b0","observation_id":"8117e4d2-e992-46ff-b9a7-35d4df4de73f","resolution":{"observed_at":"2026-08-01T03:38:14.251202Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.14573","last_updated":"2025-04-06T20:37:50Z","snapshot_observed_at":"2026-07-06T18:18:39.093732Z","submitted_at":"2024-05-23T13:48:54Z","title":"AndroidWorld: A Dynamic Benchmarking Environment for Autonomous Agents","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.14573","snapshot_observed_at":"2026-08-01T03:38:14.426866Z","title":"AndroidWorld: A dynamic benchmarking environment for autonomous agents.arXiv preprint arXiv:2405.14573, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:14.426866Z"},"links":{"cited_paper":"/paper/2405.14573","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:4318b0e9b081f7fad00ce3e97e2f1b5403f161671aa9f70ad518275b9a962e1a","observation_id":"03565b86-67cb-4d32-8ca2-0a25ac95d186","resolution":{"observed_at":"2026-08-01T03:38:14.426866Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.07972","last_updated":"2024-05-30T08:55:12Z","snapshot_observed_at":"2026-07-06T17:59:00.124173Z","submitted_at":"2024-04-11T17:56:05Z","title":"OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.07972","snapshot_observed_at":"2026-08-01T03:38:14.544686Z","title":"OSWorld: Benchmarking multimodal agents for open-ended tasks in real computer environments.arXiv preprint arXiv:2404.07972, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:14.544686Z"},"links":{"cited_paper":"/paper/2404.07972","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:7f7e04eb3a493d63abc2b43ebfbe63db1ab44be751a98681d959d5ffae0ce84b","observation_id":"8ab59d87-cc19-4419-b918-02e7f37ce06a","resolution":{"observed_at":"2026-08-01T03:38:14.544686Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.07982","last_updated":"2025-06-09T17:52:18Z","snapshot_observed_at":"2026-07-06T21:39:13.304260Z","submitted_at":"2025-06-09T17:52:18Z","title":"$\\tau^2$-Bench: Evaluating Conversational Agents in a Dual-Control Environment","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.07982","snapshot_observed_at":"2026-08-01T03:38:14.718397Z","title":"τ 2-Bench: Evaluating Conversational Agents in a Dual-Control Environment.arXiv preprint arXiv:2506.07982, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:14.718397Z"},"links":{"cited_paper":"/paper/2506.07982","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:44fcde3ba905d420d37b28cb5d9956dc7aba3a8ce013c30b1009a410e004fa0b","observation_id":"f47bff33-85fe-4a00-9617-e374f7e86c6d","resolution":{"observed_at":"2026-08-01T03:38:14.718397Z","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":"2026-08-01T03:38:14.917091Z","title":"DeepPlanning: Benchmarking long-horizon agentic planning with verifiable constraints.arXiv preprint arXiv:2601.18137, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:14.917091Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:ef5e212f9c82163db672156b940eb14a81cfeb62043010aedd4a03b62a87e338","observation_id":"346c5fb3-7925-4d70-8c01-c90025e0a818","resolution":{"observed_at":"2026-08-01T03:38:14.917091Z","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":"2026-08-01T03:38:15.027046Z","title":"VitaBench: Benchmarking LLM agents with versatile interactive tasks in real-world applications.arXiv preprint arXiv:2509.26490, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:15.027046Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:5bae9f06013bb12c091c9cdd7922962fce2779a00677480aa3961d3c0de69522","observation_id":"e8dfb898-edcd-459f-82b7-316fe344fb7f","resolution":{"observed_at":"2026-08-01T03:38:15.027046Z","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":"2026-08-01T03:38:15.219994Z","title":"The tool decathlon: Benchmarking language agents for diverse, realistic, and long-horizon task execution.arXiv preprint arXiv:2510.25726, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:15.219994Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:a31a7837d1b6296ed536211637e66b37c1551fe6f616b3dc22309b1d2071d7df","observation_id":"ce215630-2872-4aa2-adb8-2c1cc5b5fa23","resolution":{"observed_at":"2026-08-01T03:38:15.219994Z","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":"2026-08-01T03:38:15.349911Z","title":"Gaia2: Benchmarking LLM agents on dynamic and asynchronous environments.arXiv preprint arXiv:2602.11964, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:15.349911Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:ad18ffb36f0c26555cbeed719c59cf5543be20e46ce7aca4d0797611c1e6945c","observation_id":"8e92d2a9-5bc0-41f5-b6e8-ca8469372fe7","resolution":{"observed_at":"2026-08-01T03:38:15.349911Z","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":"2026-08-01T03:38:15.498465Z","title":"AgentTuning: Enabling generalized agent abilities for LLMs","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:15.498465Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:cc189ab79e767d411b9a4173d121bce1c7d8325c3d6889243182bd048a988156","observation_id":"80af2bea-ba60-4879-8095-bc5b3e427a4e","resolution":{"observed_at":"2026-08-01T03:38:15.498465Z","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":"2026-08-01T03:38:15.997564Z","title":"AgentSkiller: Scaling generalist agent intelligence through semantically integrated cross-domain data synthesis.arXiv preprint arXiv:2602.09372, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:15.997564Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:00e294b8439cca37e2a104b0f5b2a906895aafcd22398df082d494c4e07d5ac5","observation_id":"44eaf730-8743-45e2-82bb-d3dd4f6c71e6","resolution":{"observed_at":"2026-08-01T03:38:15.997564Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_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},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2604.18292","snapshot_observed_at":"2026-08-01T03:38:16.200638Z","title":"Agent-World: Scaling real-world environment synthesis for evolving general agent intelligence.arXiv preprint arXiv:2604.18292, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:16.200638Z"},"links":{"cited_paper":"/paper/2604.18292","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:afd5619bfd7ddc351fc7dba43cb31941ededd2b71b251768310afcf593c71d62","observation_id":"a627f7a2-73de-40ac-8c8a-79704e0755bc","resolution":{"observed_at":"2026-08-01T03:38:16.200638Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2602.10090","last_updated":"2026-05-22T21:39:46Z","snapshot_observed_at":"2026-08-03T01:21:05.435139Z","submitted_at":"2026-02-10T18:55:41Z","title":"Agent World Model: Infinity Synthetic Environments for Agentic Reinforcement Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2602.10090","snapshot_observed_at":"2026-08-01T03:38:16.385740Z","title":"Agent world model: Infinity synthetic environments for agentic reinforcement learning.arXiv preprint arXiv:2602.10090, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:16.385740Z"},"links":{"cited_paper":"/paper/2602.10090","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:acb3b2555050897d824983f2a6149cd1b8f36fa53cdf7b9667fe8c518d63f403","observation_id":"0f5d9833-9ae2-41bf-a710-9838f18f47cd","resolution":{"observed_at":"2026-08-01T03:38:16.385740Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2606.30616","last_updated":"2026-07-13T13:01:42Z","snapshot_observed_at":"2026-08-05T00:51:27.916299Z","submitted_at":"2026-06-29T17:50:54Z","title":"Scaling the Horizon, Not the Parameters: Reaching Trillion-Parameter Performance with a 35B Agent","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2606.30616","snapshot_observed_at":"2026-08-01T03:38:16.523167Z","title":"Scaling the horizon, not the parameters: Reaching trillion-parameter performance with a 35b agent.arXiv preprint arXiv:2606.30616, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:16.523167Z"},"links":{"cited_paper":"/paper/2606.30616","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:4878115749a78f260aae70d1c06ca6f04985a2f62b145c03c1efa1750a8d8956","observation_id":"be09b4b2-6c86-462a-96d8-d929d5ace596","resolution":{"observed_at":"2026-08-01T03:38:16.523167Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2607.14989","last_updated":"2026-07-16T13:38:07Z","snapshot_observed_at":"2026-08-05T12:38:48.104565Z","submitted_at":"2026-07-16T13:38:07Z","title":"OmniaBench: Benchmarking General AI Agents Across Diverse Scenarios","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2607.14989","snapshot_observed_at":"2026-08-01T03:38:16.681296Z","title":"Omniabench: Benchmarking general ai agents across diverse scenarios, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:16.681296Z"},"links":{"cited_paper":"/paper/2607.14989","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:8b2a27c6006de1c75d708dfe0d81a196a696600c17d8e1dd1bb758305a9e6e17","observation_id":"2bc7ea6a-b277-4acd-9cb3-21332b85d0d0","resolution":{"observed_at":"2026-08-01T03:38:16.681296Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.18901","last_updated":"2024-07-26T17:55:45Z","snapshot_observed_at":"2026-08-01T20:19:05.798018Z","submitted_at":"2024-07-26T17:55:45Z","title":"AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.18901","snapshot_observed_at":"2026-08-01T03:38:16.855650Z","title":"AppWorld: A controllable world of apps and people for benchmarking interactive coding agents.arXiv preprint arXiv:2407.18901, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:16.855650Z"},"links":{"cited_paper":"/paper/2407.18901","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:c0510573a428e107d2e9ae3e2f1487b49544f07b0005987f5aaf3f435220a4dd","observation_id":"f48550a9-3235-439c-aa50-415a3e26592b","resolution":{"observed_at":"2026-08-01T03:38:16.855650Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.07718","last_updated":"2024-07-23T06:19:28Z","snapshot_observed_at":"2026-08-02T12:09:24.340284Z","submitted_at":"2024-03-12T14:58:45Z","title":"WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.07718","snapshot_observed_at":"2026-08-01T03:38:16.973028Z","title":"Laradji, Manuel Del Verme, Tom Marty, Léo Boisvert, Megh Thakkar, Quentin Cappart, David Vazquez, Nicolas Chapados, and Alexandre Lacoste","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:16.973028Z"},"links":{"cited_paper":"/paper/2403.07718","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:4e72f5180fed1ead254f64e679dfafbc89c1c2097b11bfbbaaa0cc186b38f265","observation_id":"3b0b79ad-c691-4f48-90d0-8aa8eded79ea","resolution":{"observed_at":"2026-08-01T03:38:16.973028Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.02305","last_updated":"2025-02-16T17:16:38Z","snapshot_observed_at":"2026-08-03T02:25:36.179919Z","submitted_at":"2024-11-04T17:30:51Z","title":"CRMArena: Understanding the Capacity of LLM Agents to Perform Professional CRM Tasks in Realistic Environments","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.02305","snapshot_observed_at":"2026-08-01T03:38:17.064902Z","title":"CRMArena: Understanding the capacity of LLM agents to perform professional CRM tasks in realistic environments.arXiv preprint arXiv:2411.02305, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:17.064902Z"},"links":{"cited_paper":"/paper/2411.02305","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:542c6976cc7b4474b4c083e4c1d5e7a8b17ee44e1259b6f47c705d025c73b9a1","observation_id":"c8eb2476-a876-4d4f-95e6-f9b0399c63aa","resolution":{"observed_at":"2026-08-01T03:38:17.064902Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2510.04374","last_updated":"2025-10-05T21:36:43Z","snapshot_observed_at":"2026-07-06T22:31:44.964774Z","submitted_at":"2025-10-05T21:36:43Z","title":"GDPval: Evaluating AI Model Performance on Real-World Economically Valuable Tasks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2510.04374","snapshot_observed_at":"2026-08-01T03:38:17.216965Z","title":"Gdpval: Evaluating ai model performance on real-world economically valuable tasks.arXiv preprint arXiv:2510.04374, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:17.216965Z"},"links":{"cited_paper":"/paper/2510.04374","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:02db9737831b9ccad67c0725ac881e9afffddd00efe745c6dda26beee6f2365f","observation_id":"01b64d77-1f89-4df4-bee8-bb8f6d0802f3","resolution":{"observed_at":"2026-08-01T03:38:17.216965Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2407.19056","last_updated":"2024-07-26T19:27:17Z","snapshot_observed_at":"2026-08-03T10:35:13.470915Z","submitted_at":"2024-07-26T19:27:17Z","title":"OfficeBench: Benchmarking Language Agents across Multiple Applications for Office Automation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.19056","snapshot_observed_at":"2026-08-01T03:38:17.347721Z","title":"OfficeBench: Benchmarking language agents across multiple applications for office automation.arXiv preprint arXiv:2407.19056, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:17.347721Z"},"links":{"cited_paper":"/paper/2407.19056","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:180577550108f79cd8b5ff7de604c7a21a812ec1a0e3a4bc721ac06ce6001e30","observation_id":"afff5545-726f-45fd-b48d-709ba2dcfc7c","resolution":{"observed_at":"2026-08-01T03:38:17.347721Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.14991","last_updated":"2024-10-17T07:23:23Z","snapshot_observed_at":"2026-07-06T18:34:47.155846Z","submitted_at":"2024-06-21T09:06:45Z","title":"SpreadsheetBench: Towards Challenging Real World Spreadsheet Manipulation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.14991","snapshot_observed_at":"2026-08-01T03:38:17.484022Z","title":"SpreadsheetBench: Towards challenging real world spreadsheet manipulation.arXiv preprint arXiv:2406.14991, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:17.484022Z"},"links":{"cited_paper":"/paper/2406.14991","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:c3df3531d89527ad3fa0fe1f75fec45a953c7dbc763b1a63a7e407f28019110c","observation_id":"a742f357-078a-4605-9334-f3c07a1bafd2","resolution":{"observed_at":"2026-08-01T03:38:17.484022Z","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":"2026-08-01T03:38:17.556821Z","title":"Agentsynth: Scalable task generation for generalist computer-use agents.arXiv (Cornell University), abs/2506.14205, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:17.556821Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:e40320ab0c139b29acdfe4e9c27975cd3c8a2e9f4e8dd950f232a9c602c5473f","observation_id":"dcf146c0-347d-498b-9e88-49c6e859863d","resolution":{"observed_at":"2026-08-01T03:38:17.556821Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2601.05808","last_updated":"2026-04-17T17:24:06Z","snapshot_observed_at":"2026-08-02T19:53:18.484273Z","submitted_at":"2026-01-09T14:32:06Z","title":"EnvScaler: Scaling Tool-Interactive Environments for LLM Agent via Programmatic Synthesis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2601.05808","snapshot_observed_at":"2026-08-01T03:38:17.639212Z","title":"Envscaler: Scaling tool- interactive environments for llm agent via programmatic synthesis, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:17.639212Z"},"links":{"cited_paper":"/paper/2601.05808","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:97d6d1fa4f15886556b1918057b7fcbff1d9fe0aea223098bced1337221416cb","observation_id":"8456650f-93fc-4216-ae8d-4fd70d04dc28","resolution":{"observed_at":"2026-08-01T03:38:17.639212Z","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":"2026-08-01T03:38:17.756270Z","title":"Genenv: Difficulty-aligned co-evolution between LLM agents and environment simulators.CoRR, abs/2512.19682, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:17.756270Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:6a0f4a42b23dc03be11a83d0c80593db7e1b210ccace7f99df23dfe89a9290b8","observation_id":"9c1dfdd8-e6a3-4b99-8a1b-2ba7d0f56717","resolution":{"observed_at":"2026-08-01T03:38:17.756270Z","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":"2026-08-01T03:38:17.867436Z","title":"Graph2eval: Automatic multimodal task generation for agents via knowledge graphs.CoRR, abs/2510.00507,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:17.867436Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:1f22c5f6713f6a602d08b0c7609dbb3fbd4712bafdec6b933779ac776844a0c1","observation_id":"86e028c6-c714-43f2-bf1d-30298431ad08","resolution":{"observed_at":"2026-08-01T03:38:17.867436Z","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":"2026-08-01T03:38:18.030301Z","title":"Close the loop: Synthesizing infinite tool-use data via multi-agent role-playing.arXiv preprint arXiv:2512.23611, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:18.030301Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:1ff5b970ad2ab84da38a1fb3868345092eb2de2bb33133a81374529ce510f428","observation_id":"41372fac-44ca-4f1e-8b00-6793d416097a","resolution":{"observed_at":"2026-08-01T03:38:18.030301Z","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":"2026-08-01T03:38:17.949905Z","title":"URLhttps://doi.org/10.48550/arXiv.2510.00507","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:17.949905Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:832937a740695ca1aa348c1a8008da8b58fad3e6f221a40b22ee0a08d387d1e9","observation_id":"59e699cd-a0e1-41a2-9172-f486aff1133b","resolution":{"observed_at":"2026-08-01T03:38:17.949905Z","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":"2026-08-01T03:38:18.260403Z","title":"Reasoning with language model is planning with world model","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:18.260403Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:0e940efe636db9125a998359de0da563ea5dff3139bba02dad475fb3881c30fe","observation_id":"48851208-f0aa-4801-ac9d-96c8f695195a","resolution":{"observed_at":"2026-08-01T03:38:18.260403Z","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":"2026-08-01T03:38:18.123309Z","title":"The false promise of imitating proprietary language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:18.123309Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:a12d8a0884d8ef184cc7ca78a4299d08c9dc7ca0778825ba0c459c1381381c7e","observation_id":"c2137ae4-8cd9-4130-9e2b-8fff699f522f","resolution":{"observed_at":"2026-08-01T03:38:18.123309Z","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":"2026-08-01T03:38:18.427087Z","title":"Qwen3-30B-A3B-Thinking-2507","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:18.427087Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:acc9aed4056ea56f9f526e1f546ced3ef6d3065f7ca469fe39092c2761f7b78c","observation_id":"ce1a7cbe-9b13-46be-83eb-785e2466843c","resolution":{"observed_at":"2026-08-01T03:38:18.427087Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.09388","last_updated":"2025-05-14T13:41:34Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-05-14T13:41:34Z","title":"Qwen3 Technical Report","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.09388","snapshot_observed_at":"2026-08-01T03:38:18.341721Z","title":"Qwen3 technical report.CoRR, abs/2505.09388, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:18.341721Z"},"links":{"cited_paper":"/paper/2505.09388","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:65ca3e5ffea3a29872a4eb412e11cf431abd1fb52649d4f88132f560b437ca24","observation_id":"7bdb8e19-14cb-472a-9274-677ad70d5535","resolution":{"observed_at":"2026-08-01T03:38:18.341721Z","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":"2026-08-01T03:38:18.626242Z","title":"Weak-to-strong generalization: Eliciting strong capabilities with weak supervision","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:18.626242Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:e8632c9370a2ab0658e9704d5e06aa989ab33577ace6c30c96e0430094ecbd78","observation_id":"585af319-8973-4832-a8b6-1b105f2f3657","resolution":{"observed_at":"2026-08-01T03:38:18.626242Z","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":"2026-08-01T03:38:18.515646Z","title":"Nex-N2-mini","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:18.515646Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:3837f57fe996e9af2957263afcbba9445a6ce792576e60d696d5e76cee6dcc59","observation_id":"0217fff4-900d-4dba-b088-db438dbf0b1b","resolution":{"observed_at":"2026-08-01T03:38:18.515646Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.04151","last_updated":"2024-06-06T15:15:41Z","snapshot_observed_at":"2026-08-03T05:16:48.313052Z","submitted_at":"2024-06-06T15:15:41Z","title":"AgentGym: Evolving Large Language Model-based Agents across Diverse Environments","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.04151","snapshot_observed_at":"2026-08-01T03:38:18.754752Z","title":"AgentGym: Evolving large language model-based agents across diverse environments.arXiv preprint arXiv:2406.04151, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:18.754752Z"},"links":{"cited_paper":"/paper/2406.04151","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:2554c2a472e1a064768b4a09d82dd98312dd3234ba32a8e63031e193cb2b540f","observation_id":"8eacd945-76f1-463c-93e5-fdfb59c3d38c","resolution":{"observed_at":"2026-08-01T03:38:18.754752Z","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":"2026-08-01T03:38:18.685301Z","title":"DigiRL: Training in-the-wild device-control agents with autonomous reinforcement learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:18.685301Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:1ebe35a3787dbce316386f371b3affdffc58cbf2b3f25750c7452399bd0a4979","observation_id":"76835c3d-022c-4f24-9eb7-1d6585ad0c3d","resolution":{"observed_at":"2026-08-01T03:38:18.685301Z","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":"2026-08-01T03:38:19.201348Z","title":"Self-instruct: Aligning language models with self-generated instructions","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:19.201348Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:c451462d455b2213f02d3502ba5a4462de4388df7788e45207bfd33e6bbd56f0","observation_id":"8c6db000-1089-4673-8124-45dec2a3da51","resolution":{"observed_at":"2026-08-01T03:38:19.201348Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.18669","last_updated":"2025-08-26T04:26:29Z","snapshot_observed_at":"2026-08-05T16:22:50.856434Z","submitted_at":"2025-08-26T04:26:29Z","title":"MUA-RL: Multi-turn User-interacting Agent Reinforcement Learning for agentic tool use","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.18669","snapshot_observed_at":"2026-08-01T03:38:19.076593Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:19.076593Z"},"links":{"cited_paper":"/paper/2508.18669","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:d4ee907577dada3471765040e690904dd33ee3ae9589a71bea83b3c83c77e228","observation_id":"b1bb2712-7032-43aa-84fb-fa6502ce6bdc","resolution":{"observed_at":"2026-08-01T03:38:19.076593Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.17651","last_updated":"2023-05-25T19:13:47Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-03-30T18:30:01Z","title":"Self-Refine: Iterative Refinement with Self-Feedback","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.17651","snapshot_observed_at":"2026-08-01T03:38:19.596894Z","title":"Self-refine: Iterative refinement with self-feedback.arXiv preprint arXiv:2303.17651, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:19.596894Z"},"links":{"cited_paper":"/paper/2303.17651","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:1ed7fae787ba563650d7e1ebec51dba3e7c0dfa3175d0474236cc1b4f4213de5","observation_id":"334a7181-e49d-49e4-b66d-25f024adc106","resolution":{"observed_at":"2026-08-01T03:38:19.596894Z","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":"2026-08-01T03:38:19.314234Z","title":"Self- rewarding language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:19.314234Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:ed3f94681af40479e82fdd91158039929ab52b4fdfe62a5dd6f64f9b6c3ad593","observation_id":"b8d99ba1-4b7f-4a51-9d8a-8337277723db","resolution":{"observed_at":"2026-08-01T03:38:19.314234Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.11366","last_updated":"2023-10-10T05:21:45Z","snapshot_observed_at":"2026-07-06T15:05:53.556198Z","submitted_at":"2023-03-20T18:08:50Z","title":"Reflexion: Language Agents with Verbal Reinforcement Learning","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.11366","snapshot_observed_at":"2026-08-01T03:38:19.429471Z","title":"Reflexion: Language agents with verbal reinforcement learning.arXiv preprint arXiv:2303.11366, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:19.429471Z"},"links":{"cited_paper":"/paper/2303.11366","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:31405a324323da523526a19ec66dee06be2cd82d5e6221dce817fa4275f4baa3","observation_id":"18253b20-bf5a-4eb0-bab1-a413842c19d4","resolution":{"observed_at":"2026-08-01T03:38:19.429471Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2605.24426","last_updated":"2026-05-23T06:41:31Z","snapshot_observed_at":"2026-07-06T23:34:28.608412Z","submitted_at":"2026-05-23T06:41:31Z","title":"SEAL: Synergistic Co-Evolution of Agents and Learning Environments","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2605.24426","snapshot_observed_at":"2026-08-01T03:38:19.956424Z","title":"SEAL: Synergistic co-evolution of agents and learning environments.arXiv preprint arXiv:2605.24426, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:19.956424Z"},"links":{"cited_paper":"/paper/2605.24426","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:05709fb44caac064dcf9d1caa9eb776b660bcbfe180cc5ff0b45c9611e830f67","observation_id":"209537c7-edf2-4958-943b-a557f7aa13da","resolution":{"observed_at":"2026-08-01T03:38:19.956424Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.00533","last_updated":"2024-02-01T06:10:00Z","snapshot_observed_at":"2026-08-05T14:58:48.852459Z","submitted_at":"2023-10-01T00:52:24Z","title":"SELF: Self-Evolution with Language Feedback","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.00533","snapshot_observed_at":"2026-08-01T03:38:19.769347Z","title":"SELF: Self-evolution with language feedback.arXiv preprint arXiv:2310.00533, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:19.769347Z"},"links":{"cited_paper":"/paper/2310.00533","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:9c7588f055521c99c41e224ca7e93bc3e19f18668dc94011cadeb6b48beb230f","observation_id":"0abd943a-9804-4312-a9be-7e7980d303c5","resolution":{"observed_at":"2026-08-01T03:38:19.769347Z","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":"2026-08-01T03:38:19.886098Z","title":"AgentEvolver: Towards efficient self-evolving agent system.arXiv preprint arXiv:2511.10395, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:19.886098Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:fb0188219f4ed3511ea5c010f9a883e50a13fed81c7367dfeae7173c90ce527d","observation_id":"d5082c61-da55-4f70-a9ed-170ea7fe5c11","resolution":{"observed_at":"2026-08-01T03:38:19.886098Z","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":"2026-08-01T03:38:20.303673Z","title":"Towards general agentic intelligence via environment scaling","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:20.303673Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:31a6894eff90eba3432c6841e903e1b7e4c7f1d1d7d9f5e874fc0ff43122c05e","observation_id":"0a105275-95f9-411f-a631-721dad76a4fb","resolution":{"observed_at":"2026-08-01T03:38:20.303673Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2606.06741","last_updated":"2026-06-04T21:55:48Z","snapshot_observed_at":"2026-07-06T23:46:28.942186Z","submitted_at":"2026-06-04T21:55:48Z","title":"OpenSkill: Open-World Self-Evolution for LLM Agents","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2606.06741","snapshot_observed_at":"2026-08-01T03:38:20.065815Z","title":"Yu, Ran Xu, Xiang Li, and Lichao Sun","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:20.065815Z"},"links":{"cited_paper":"/paper/2606.06741","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:1c26281c3268266bf00a4e13f64261b1b7d669cf12488cbedab0949e8635a22b","observation_id":"59420964-1d3e-47cc-ae8a-bc09c3c3abae","resolution":{"observed_at":"2026-08-01T03:38:20.065815Z","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":"2026-08-01T03:38:20.209285Z","title":"Autoforge: Automated environment synthesis for agentic reinforcement learning.arXiv preprint arXiv:2512.22857, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:20.209285Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:81b5a7de451abb047797530f8f8958c50e1b2d4787cdae5293e7ad8d72d698b1","observation_id":"adcffc99-241e-48fb-906a-1595971cb7e4","resolution":{"observed_at":"2026-08-01T03:38:20.209285Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2605.11556","last_updated":"2026-05-12T05:41:00Z","snapshot_observed_at":"2026-07-06T23:23:21.034839Z","submitted_at":"2026-05-12T05:41:00Z","title":"Hindsight Hint Distillation: Scaffolded Reasoning for SWE Agents from CoT-free Answers","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2605.11556","snapshot_observed_at":"2026-08-01T03:38:20.507514Z","title":"Hindsight hint distillation: Scaffolded reasoning for swe agents from cot-free answers, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:20.507514Z"},"links":{"cited_paper":"/paper/2605.11556","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:abaae422f75ae179e5835434a5849534a97ed9ca56f003b0b6a6e0833dc9544b","observation_id":"4ae3ba75-c77d-4a70-b1b5-4f1b98544de4","resolution":{"observed_at":"2026-08-01T03:38:20.507514Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2601.18734","last_updated":"2026-03-20T15:40:19Z","snapshot_observed_at":"2026-08-05T07:13:28.092224Z","submitted_at":"2026-01-26T17:56:50Z","title":"Self-Distilled Reasoner: On-Policy Self-Distillation for Large Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2601.18734","snapshot_observed_at":"2026-08-01T03:38:20.360761Z","title":"Self-distilled reasoner: On-policy self-distillation for large language models, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:20.360761Z"},"links":{"cited_paper":"/paper/2601.18734","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:f4fa4edb2a1b87c72157a7f56ec5ddc920cfb5fe1f52096e6f06f61996434107","observation_id":"7037e72a-afae-4e29-a103-9b047dfadae7","resolution":{"observed_at":"2026-08-01T03:38:20.360761Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2605.23493","last_updated":"2026-05-22T10:55:15Z","snapshot_observed_at":"2026-08-02T05:38:30.674952Z","submitted_at":"2026-05-22T10:55:15Z","title":"EDGE-OPD: Internalizing Privileged Context with Evidence Guided On-Policy Distillation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2605.23493","snapshot_observed_at":"2026-08-01T03:38:20.430907Z","title":"Edge-opd: Internalizing privileged context with evidence guided on-policy distillation, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:20.430907Z"},"links":{"cited_paper":"/paper/2605.23493","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:90002b464764e049bd5cc285c4277dc4ca1bddd9fcde25a32a743f2bbe527b39","observation_id":"7bc54ac1-4cbe-4f58-b605-8775741ba252","resolution":{"observed_at":"2026-08-01T03:38:20.430907Z","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":"2026-08-01T03:38:20.865271Z","title":"Let it flow: Agentic crafting on rock and roll, building the rome model within an open agentic learning ecosystem.arXiv preprint arXiv:2512.24873, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:20.865271Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:4a17d80a2963b1cecfd6d52cf3af114f06dd05819b5b998c07d83e5834a15523","observation_id":"d49ec8bb-2568-4669-84d7-ecc02e36ebe4","resolution":{"observed_at":"2026-08-01T03:38:20.865271Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.10429","last_updated":"2023-11-21T02:01:53Z","snapshot_observed_at":"2026-07-06T15:28:49.253125Z","submitted_at":"2023-05-17T17:58:13Z","title":"DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.10429","snapshot_observed_at":"2026-08-01T03:38:20.625282Z","title":"Le, Tengyu Ma, and Adams Wei Yu","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:20.625282Z"},"links":{"cited_paper":"/paper/2305.10429","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:31f7580b7f414a3e73a6ee187d9fce1706c1a1b2b259166bd981b9aef7762a69","observation_id":"1fc5d40f-f130-4a86-9e41-b585abd7572d","resolution":{"observed_at":"2026-08-01T03:38:20.625282Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.11953","last_updated":"2025-08-16T07:28:39Z","snapshot_observed_at":"2026-08-05T19:39:29.759828Z","submitted_at":"2025-08-16T07:28:39Z","title":"Data Mixing Optimization for Supervised Fine-Tuning of Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.11953","snapshot_observed_at":"2026-08-01T03:38:20.733283Z","title":"Data mixing optimization for supervised fine-tuning of large language models, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:20.733283Z"},"links":{"cited_paper":"/paper/2508.11953","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:8aea395aa0425d877eb4fb2b89bcb4c053ceb2137aeb7c1d330ee9e2c77d633c","observation_id":"59aa6f80-54ff-4a90-8364-1a9dd8dc955f","resolution":{"observed_at":"2026-08-01T03:38:20.733283Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2606.26300","last_updated":"2026-06-29T08:21:30Z","snapshot_observed_at":"2026-07-07T00:00:41.502665Z","submitted_at":"2026-06-24T18:45:03Z","title":"The Verification Horizon: No Silver Bullet for Coding Agent Rewards","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2606.26300","snapshot_observed_at":"2026-08-01T03:38:21.186892Z","title":"The verification horizon: No silver bullet for coding agent rewards.arXiv preprint arXiv:2606.26300, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:21.186892Z"},"links":{"cited_paper":"/paper/2606.26300","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:3c374b706064f9abccc0d246b0622b8317c05e6704a5985af0559e1ce4196bdf","observation_id":"6b45b8d9-83f7-4e13-8e04-eb09843a4dd2","resolution":{"observed_at":"2026-08-01T03:38:21.186892Z","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":"2026-08-01T03:38:20.980602Z","title":"A survey on llm-as-a-judge.The Innovation, 7(6), 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:20.980602Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:6aee4c06dd8ad5740c562ab947500b6228fc9bc37ace116a2e3aba810c7fccf2","observation_id":"b045e45c-a13b-4d2c-aa64-f9ffe4eb635d","resolution":{"observed_at":"2026-08-01T03:38:20.980602Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2604.09459","last_updated":"2026-04-13T12:08:22Z","snapshot_observed_at":"2026-07-06T22:58:21.624968Z","submitted_at":"2026-04-10T16:17:44Z","title":"From Reasoning to Agentic: Credit Assignment in Reinforcement Learning for Large Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2604.09459","snapshot_observed_at":"2026-08-01T03:38:21.094327Z","title":"From reasoning to agentic: Credit assignment in reinforcement learning for large language models.arXiv preprint arXiv:2604.09459, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:21.094327Z"},"links":{"cited_paper":"/paper/2604.09459","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:96fa25ccf93ef7d89bd01771812df5019c3c529e3398709dba352a0c4e7dfc1e","observation_id":"1642f806-1407-43b7-932d-b2d76280d4d5","resolution":{"observed_at":"2026-08-01T03:38:21.094327Z","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":"2026-08-01T03:38:21.614609Z","title":"When speed kills stability: Demystifying RL collapse from the training-inference mismatch, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:21.614609Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:c54b051c3b4acd646506d3cf7539bf41fb224333f468db00e0cad984ab8e4424","observation_id":"f14d2dbb-355a-46d1-aea5-cc29494eef8d","resolution":{"observed_at":"2026-08-01T03:38:21.614609Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2606.04923","last_updated":"2026-06-03T14:18:23Z","snapshot_observed_at":"2026-07-06T23:44:57.208389Z","submitted_at":"2026-06-03T14:18:23Z","title":"Reproducing, Analyzing, and Detecting Reward Hacking in Rubric-Based Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2606.04923","snapshot_observed_at":"2026-08-01T03:38:21.327505Z","title":"Reproducing, analyzing, and detecting reward hacking in rubric-based reinforcement learning.arXiv preprint arXiv:2606.04923, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:21.327505Z"},"links":{"cited_paper":"/paper/2606.04923","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:c113ad101986fb9599a4dff089774840220e26f30135915849266c2dac581cd0","observation_id":"71c07a45-7329-4c20-9d61-7033f69c75d2","resolution":{"observed_at":"2026-08-01T03:38:21.327505Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.03300","last_updated":"2024-04-27T15:25:53Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-02-05T18:55:32Z","title":"DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.03300","snapshot_observed_at":"2026-08-01T03:38:21.472971Z","title":"Deepseekmath: Pushing the limits of mathematical reasoning in open language models.arXiv preprint arXiv:2402.03300, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:21.472971Z"},"links":{"cited_paper":"/paper/2402.03300","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:bdc0e0ecdda738d46f96739b4d4884c109f2b4a084f73460f29c9fbb26270fa7","observation_id":"d623d54c-601b-4c6f-8a44-d37fc9746f12","resolution":{"observed_at":"2026-08-01T03:38:21.472971Z","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":"2026-08-01T03:38:21.984566Z","title":"Dapo: An open-source llm reinforcement learning system at scale.Advances in Neural Information Processing Systems, 38:113222–113244, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:21.984566Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:94a25d54f7715ea181c6098c7eea17f6aa6b44e5b13423dc900dd8e6dac14368","observation_id":"ce937021-a1b1-49a4-9986-4ce4513e2750","resolution":{"observed_at":"2026-08-01T03:38:21.984566Z","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":"2026-08-01T03:38:21.755573Z","title":"Your efficient rl framework secretly brings you off-policy rl training, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:21.755573Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:76d8602ee75e5b800a87e64be98cd899782c04498c1c461c37c32a258e62582a","observation_id":"1d6b70bc-be54-4d67-ac3e-5d865921adc4","resolution":{"observed_at":"2026-08-01T03:38:21.755573Z","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":"2026-08-01T03:38:21.856470Z","title":"Stabilizing moe reinforcement learning by aligning training and inference routers.arXiv preprint arXiv:2510.11370, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:21.856470Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:939f6505ad6a15ddc9c776bbfa83ea5a34ec4c6c20298dfc41db8b581f923376","observation_id":"45f972cb-c5c8-469d-a433-3caa3928a06b","resolution":{"observed_at":"2026-08-01T03:38:21.856470Z","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":"2026-08-01T03:38:22.201149Z","title":"Skyrl gym generator tutorial","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:22.201149Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:abaa3b1131c4df4245934b4c71c9f1d70fdf8ecd4a376d4e3f33d04ff62fae29","observation_id":"71d2a21f-1f4b-4d43-8854-dbd176969f0b","resolution":{"observed_at":"2026-08-01T03:38:22.201149Z","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":"2026-08-01T03:38:22.044662Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:22.044662Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:cd3b71e0c841be95941d91bf14c7038cf825b85b7630fee6c4be15ba36ddbf11","observation_id":"1dac5146-acca-4ef0-9663-3156e108243d","resolution":{"observed_at":"2026-08-01T03:38:22.044662Z","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":"2026-08-01T03:38:22.123009Z","title":"Save, load and learn: Boosting agentic llms via rollback-based curriculum learning","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:22.123009Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:d989e27b4c65e752780fe136fb6877f486d133664233f4ff569ce1eba3a7ef7a","observation_id":"049c48b6-adf4-4adf-a08c-aef8cbed8868","resolution":{"observed_at":"2026-08-01T03:38:22.123009Z","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":"2026-08-01T03:38:22.441669Z","title":"Gemini 3.5 Flash model card","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:22.441669Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:f4621ef6eeae2519fdc858d45b256718527afc9cbd9e980f4bf9df751c78690c","observation_id":"73ceaa56-5d7d-4383-aadc-dbe1a5b1bd46","resolution":{"observed_at":"2026-08-01T03:38:22.441669Z","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":"2026-08-01T03:38:22.230205Z","title":"GPT-5.5 system card","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:22.230205Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:ae498b1c25e47f576466ce8f5d75f0dee67c9c9794a8b19ad62c3c2ac1762c8e","observation_id":"d762cc4a-975b-44be-b6d3-44d9895a98ab","resolution":{"observed_at":"2026-08-01T03:38:22.230205Z","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":"2026-08-01T03:38:22.345133Z","title":"Claude Opus 4.7 model report","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:22.345133Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:4854e08f995156f6149e4c008b93f360c64f692123a433462e3bf90a84ec65de","observation_id":"6919cec2-94fc-4609-a02b-dbaba7868672","resolution":{"observed_at":"2026-08-01T03:38:22.345133Z","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":"2026-08-01T03:38:22.713763Z","title":"Qwen3-235B-A22B-Thinking-2507","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:22.713763Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:67e67fd73a4104059762a9f5d31228e6a419f0e2e3ca4766fd561fa3693a7b94","observation_id":"c20310d6-02de-41b2-a7c0-a763af8da3a3","resolution":{"observed_at":"2026-08-01T03:38:22.713763Z","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":"2026-08-01T03:38:22.520967Z","title":"Qwen3.7: The agent frontier","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:22.520967Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:1e52b7d82e69b61da6e3c4aad3f06c195b4880455229c32f16614f7a52da5b55","observation_id":"1936f663-173b-4fa7-9287-0d0a388b17ed","resolution":{"observed_at":"2026-08-01T03:38:22.520967Z","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":"2026-08-01T03:38:22.622942Z","title":"DeepSeek-V4: Towards highly efficient million-token context intelligence, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:22.622942Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:0b96b6e4ca104b07ecbc1fc7e045f7468da81ade71831c29cfbb5f61f3e00bb7","observation_id":"4f7ac7fd-cb34-4c22-ac17-d0d7e60d119c","resolution":{"observed_at":"2026-08-01T03:38:22.622942Z","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":"2026-08-01T03:38:23.028748Z","title":"Nex-N1: Agentic models trained via a unified ecosystem for large-scale environment construction.arXiv preprint arXiv:2512.04987, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:23.028748Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:666abc1ed1ed040657321b0262177b17a5950aeacbe66104d7d21f065c70e91f","observation_id":"aaea734f-b5ec-46b2-a9fb-99fbe2f44502","resolution":{"observed_at":"2026-08-01T03:38:23.028748Z","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":"2026-08-01T03:38:22.833799Z","title":"Qwen3.5: Towards native multimodal agents, February 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:22.833799Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:bcb3d619c69c2d3bf9c02701dbd719daeb20317700ffa568b6f5ab39d15937f0","observation_id":"517845ba-380c-4cea-81c9-70074976db40","resolution":{"observed_at":"2026-08-01T03:38:22.833799Z","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":"2026-08-01T03:38:22.943979Z","title":"Qwen3.6-35B-A3B: Agentic coding power, now open to all, April 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:22.943979Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:67d27422768b20da025fa0d98ba88e0e1415a92e1646ef314ea45901744280f8","observation_id":"01eec249-9f14-4d82-b16e-0ba0b0bd6b48","resolution":{"observed_at":"2026-08-01T03:38:22.943979Z","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":"2026-08-01T03:38:23.329682Z","title":"Large language models are zero-shot reasoners","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:23.329682Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:213fa0408d3f8b52d570a4fdd9aad3fa1fac7e343e6f2d3b671b84a1c6f3e3fe","observation_id":"39305907-6306-4c25-b190-45003a673ea5","resolution":{"observed_at":"2026-08-01T03:38:23.329682Z","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":"2026-08-01T03:38:23.142866Z","title":"ScaleEnv: Scaling environment synthesis from scratch for generalist interactive tool-use agent training.arXiv preprint arXiv:2602.06820, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:23.142866Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:4bb923776dda997e3b01f550c2591cc598efd5207eecc4ababc13a132ad04261","observation_id":"50763220-3970-4cbc-b5f6-6a717be71f7c","resolution":{"observed_at":"2026-08-01T03:38:23.142866Z","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":"2026-08-01T03:38:23.259458Z","title":"Chain-of-thought prompting elicits reasoning in large language models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:23.259458Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:5ea1ef20ff6b5e8b9c22a53f8c5c9f7f716da266058d3d832d0ffb2166c729ef","observation_id":"812280a8-01c6-4a44-96f6-462f8c2f0e9c","resolution":{"observed_at":"2026-08-01T03:38:23.259458Z","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":"2026-08-01T03:38:23.650158Z","title":"A survey on large language model based autonomous agents.Frontiers of Computer Science, 18(6):186345, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:23.650158Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:e684210f39623d91d5e49f2b5018df56ff3100a4fba425ff6b3655df4ba1141c","observation_id":"565a8b08-9384-422e-ad3b-a35d686578ef","resolution":{"observed_at":"2026-08-01T03:38:23.650158Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2110.14168","last_updated":"2021-11-18T00:23:45Z","snapshot_observed_at":"2026-08-04T15:46:25.710484Z","submitted_at":"2021-10-27T04:49:45Z","title":"Training Verifiers to Solve Math Word Problems","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2110.14168","snapshot_observed_at":"2026-08-01T03:38:23.470230Z","title":"Training verifiers to solve math word problems","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:23.470230Z"},"links":{"cited_paper":"/paper/2110.14168","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:cbd1c7ce89738e9fed2c627124e923e809db277f489c6fb3d0701b9a2baff37b","observation_id":"539346c2-1d56-43f4-a67b-77659e158fa4","resolution":{"observed_at":"2026-08-01T03:38:23.470230Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.17306","last_updated":"2023-05-26T23:46:42Z","snapshot_observed_at":"2026-07-06T15:34:13.017210Z","submitted_at":"2023-05-26T23:46:42Z","title":"Chain-of-Thought Hub: A Continuous Effort to Measure Large Language Models' Reasoning Performance","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.17306","snapshot_observed_at":"2026-08-01T03:38:23.547518Z","title":"Chain-of-thought hub: A continuous effort to measure large language models’ reasoning performance.CoRR, abs/2305.17306, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:23.547518Z"},"links":{"cited_paper":"/paper/2305.17306","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:40e656f84d70f37c283067dcd35a234115c1c526acee5e39cb30f993c5b393db","observation_id":"ed1aab7e-681f-4ae5-a243-7c29b2de7278","resolution":{"observed_at":"2026-08-01T03:38:23.547518Z","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":"2026-08-01T03:38:23.967520Z","title":"Mind2Web: Towards a generalist agent for the web","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:23.967520Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:bc6ab3805aa18a8bb85d3099e6efcdd5c45ac9e73e46c18863db10212a4bbe3c","observation_id":"e55a1835-c145-4bb4-a7dd-eb52d2e2b00c","resolution":{"observed_at":"2026-08-01T03:38:23.967520Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.07864","last_updated":"2023-09-19T08:29:18Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-09-14T17:12:03Z","title":"The Rise and Potential of Large Language Model Based Agents: A Survey","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.07864","snapshot_observed_at":"2026-08-01T03:38:23.751972Z","title":"The rise and potential of large language model based agents: A survey.arXiv preprint arXiv:2309.07864, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:23.751972Z"},"links":{"cited_paper":"/paper/2309.07864","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:5de2776aec18dafb97d89c3fa36fe7b52c172ebf446fdd4e0b63efbdc9aac6f5","observation_id":"1439f165-08f4-4164-ae8c-f9b30856d0b9","resolution":{"observed_at":"2026-08-01T03:38:23.751972Z","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":"2026-08-01T03:38:23.823649Z","title":"WebShop: Towards scalable real-world web interaction with grounded language agents","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:23.823649Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:da38634943750cf068da22fa45778e31320117b6122586c9ff397326c7001246","observation_id":"e1eb5563-8512-41b4-a39d-15e0fd76cc29","resolution":{"observed_at":"2026-08-01T03:38:23.823649Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.12045","last_updated":"2024-06-17T19:33:08Z","snapshot_observed_at":"2026-08-02T22:19:29.043854Z","submitted_at":"2024-06-17T19:33:08Z","title":"$\\tau$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.12045","snapshot_observed_at":"2026-08-01T03:38:24.233099Z","title":"τ-bench: A benchmark for tool-agent-user interaction in real-world domains.arXiv preprint arXiv:2406.12045, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:24.233099Z"},"links":{"cited_paper":"/paper/2406.12045","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:fa9dc3417edce1016f649cc71195e54681e8ed77caeee856f894de87377fefb4","observation_id":"f5372a40-724e-4a77-b7b8-3f229399fa68","resolution":{"observed_at":"2026-08-01T03:38:24.233099Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.13649","last_updated":"2024-06-06T02:01:09Z","snapshot_observed_at":"2026-08-05T16:01:54.847394Z","submitted_at":"2024-01-24T18:35:21Z","title":"VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.13649","snapshot_observed_at":"2026-08-01T03:38:24.033281Z","title":"VisualWebArena: Evaluating multimodal agents on realistic visual web tasks.arXiv preprint arXiv:2401.13649, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:24.033281Z"},"links":{"cited_paper":"/paper/2401.13649","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:063dc5281af721e622e4456acbdd56b199160483993faed30ab82f8aed97bdf0","observation_id":"9a24fb7e-a20d-429f-ad71-cfe29319e4e2","resolution":{"observed_at":"2026-08-01T03:38:24.033281Z","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":"2026-08-01T03:38:24.126811Z","title":"SWE-bench: Can language models resolve real-world GitHub issues? InThe Twelfth International Conference on Learning Representations, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:24.126811Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:b384459740c600d68e997b6bd73b4bc0ad126749e8d13e02c194e1c1153bbf0c","observation_id":"8f22ee88-a561-4cf2-b1c8-a8a810309a7c","resolution":{"observed_at":"2026-08-01T03:38:24.126811Z","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":"2026-08-01T03:38:24.533446Z","title":"Apigen: Automated pipeline for generating verifiable and diverse function-calling datasets.Advances in Neural Information Processing Systems, 37:54463–54482, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:24.533446Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:396ee6c9e758deb4ce7b8895d472e7801cec3c9d7ef6e76a133b866d0b8e5d8a","observation_id":"a507565d-4f33-4990-8bea-37749c6dbdec","resolution":{"observed_at":"2026-08-01T03:38:24.533446Z","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":"2026-08-01T03:38:24.324952Z","title":"Berkeley function-calling leaderboard","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:24.324952Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:1d1dbeb3d13d13d01947d77bd6857de8c473288fc90cd9b6fee85857d016162e","observation_id":"12c4df0b-504b-4e39-9fdb-cfa386ec6143","resolution":{"observed_at":"2026-08-01T03:38:24.324952Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.05301","last_updated":"2023-09-07T12:20:45Z","snapshot_observed_at":"2026-07-06T15:40:20.267344Z","submitted_at":"2023-06-08T15:46:32Z","title":"ToolAlpaca: Generalized Tool Learning for Language Models with 3000 Simulated Cases","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.05301","snapshot_observed_at":"2026-08-01T03:38:24.402366Z","title":"Toolalpaca: Generalized tool learning for language models with 3000 simulated cases.arXiv preprint arXiv:2306.05301, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:24.402366Z"},"links":{"cited_paper":"/paper/2306.05301","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:4f95b42c87a4613dded48a1970e7cdafb93e93fc8899ec788b5edf27f10787aa","observation_id":"fb24fb35-4630-4374-b4ac-65dc984e9a53","resolution":{"observed_at":"2026-08-01T03:38:24.402366Z","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":"2026-08-01T03:38:24.815851Z","title":"Dive: Scaling diversity in agentic task synthesis for generalizable tool use.arXiv preprint arXiv:2603.11076, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:24.815851Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:de03f6220cbdd027416d638925a1c1492c27ef335aef5807c4bc7209397999e3","observation_id":"bb950eb9-0a9c-4159-b89a-c75b664dd962","resolution":{"observed_at":"2026-08-01T03:38:24.815851Z","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":"2026-08-01T03:38:24.644166Z","title":"Toolace: Winning the points of llm function calling","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:24.644166Z"},"links":{"citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:f89ce54caed074b3f0f6ee6356facdb1328fac434f7af9bde122c2d1cb456073","observation_id":"03eaa09c-825a-43cb-86a4-46d96c0616e0","resolution":{"observed_at":"2026-08-01T03:38:24.644166Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2605.18703","last_updated":"2026-05-18T17:37:40Z","snapshot_observed_at":"2026-07-06T23:29:33.702647Z","submitted_at":"2026-05-18T17:37:40Z","title":"EnvFactory: Scaling Tool-Use Agents via Executable Environments Synthesis and Robust RL","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2605.18703","snapshot_observed_at":"2026-08-01T03:38:24.740632Z","title":"Envfactory: Scaling tool-use agents via executable environments synthesis and robust rl.arXiv preprint arXiv:2605.18703, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:24.740632Z"},"links":{"cited_paper":"/paper/2605.18703","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:187bba4dc9ec56962f42144964c608d4a3d6ff6a44ca34400b7ab89e361e3993","observation_id":"a85bb155-9246-44e6-b63f-b37966f4c4e4","resolution":{"observed_at":"2026-08-01T03:38:24.740632Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.13958","last_updated":"2025-04-16T21:45:32Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-16T21:45:32Z","title":"ToolRL: Reward is All Tool Learning Needs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.13958","snapshot_observed_at":"2026-08-01T03:38:25.069775Z","title":"Toolrl: Reward is all tool learning needs, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:25.069775Z"},"links":{"cited_paper":"/paper/2504.13958","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:018412690826e27e9d893b2df9940f5b83ee02781fa2757915ea8549ca042538","observation_id":"2766b60f-5bd6-4d97-89bf-b8b2e19eea6d","resolution":{"observed_at":"2026-08-01T03:38:25.069775Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.12948","last_updated":"2026-01-04T03:57:36Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-22T15:19:35Z","title":"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.12948","snapshot_observed_at":"2026-08-01T03:38:24.888398Z","title":"Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning.arXiv preprint arXiv:2501.12948, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":101,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:24.888398Z"},"links":{"cited_paper":"/paper/2501.12948","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:6fc2d93324d496f849339a0806652391b8ec0de6e9f2f7dbdd740975354ce8e7","observation_id":"4d0f22f7-6959-45aa-88ee-69e804db4ee0","resolution":{"observed_at":"2026-08-01T03:38:24.888398Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.11536","last_updated":"2025-04-17T16:46:07Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-15T18:10:22Z","title":"ReTool: Reinforcement Learning for Strategic Tool Use in LLMs","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.11536","snapshot_observed_at":"2026-08-01T03:38:24.974260Z","title":"Retool: Reinforcement learning for strategic tool use in llms, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications","version":1},"reference_index":102,"source":"pdf_text","source_observed_at":"2026-08-01T03:38:24.974260Z"},"links":{"cited_paper":"/paper/2504.11536","citing_paper":"/paper/2607.23124"},"observation_digest":"sha256:13a5089a94772ba2ad3b3de0cf0de81629a68de2463f9c70e3bb441e9f70ce41","observation_id":"d448022d-1539-4953-a0b3-593549b6988a","resolution":{"observed_at":"2026-08-01T03:38:24.974260Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2607.23124","last_updated":"2026-07-25T09:58:24Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-01T21:13:06.083655Z","submitted_at":"2026-07-25T09:58:24Z","title":"AgentOmnia: Scaling Agentic Models for Full-Scenario Applications"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":100,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":177},"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":"2026-08-05T06:32:48.257954+00:00","source":"crossref"},{"observed_at":"2026-08-05T06:32:44.755628+00:00","source":"retraction_watch"}],"thesis":"As of 6 August 2026, this Paper Citation Record lists 100 of 177 outbound references and 0 inbound Pith citation observations for arXiv:2607.23124."}