{"as_of":"2026-08-19T01:47:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:86ef97a4dde0956dc07abb3fac9d3ede5efdaeb8439bdf29487c6f1b1833b2a3","coverage":[{"denominator":210,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T23:57:38.586633Z","state":"measured"},{"denominator":117,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":117,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":17,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":17,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-04T23:55:37.907213Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-07-10T12:15:01.137692Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.13167","snapshot_observed_at":"2026-08-04T23:55:37.907213Z","title":"Chain-of-agents: End-to-end agent foundation models via multi-agent distillation and agentic rl.arXiv preprint arXiv:2508.13167, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.06283","last_updated":"2025-09-09T02:30:02Z","snapshot_observed_at":"2026-08-16T22:44:39.259309Z","submitted_at":"2025-09-08T02:07:09Z","title":"SFR-DeepResearch: Towards Effective Reinforcement Learning for Autonomously Reasoning Single Agents","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-04T23:55:37.907213Z"},"links":{"cited_paper":"/paper/2508.13167","citing_paper":"/paper/2509.06283"},"observation_digest":"sha256:59d2ebda1f252013a127753160b058f09ade2c472837c4776ccb9d3f42e2036e","observation_id":"dcdc812e-5f22-4df7-b1b8-81b57d114aa0","resolution":{"observed_at":"2026-08-04T23:55:37.907213Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"cited_work":{"arxiv_id":"2508.13167","doi":"10.48550/arxiv.2508.13167","metadata_source":"pith","pith_arxiv_id":"2508.13167","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Chain-of-agents: End-to-endagentfoundationmodelsviamulti-agentdistillation andagenticRL.arXivpreprint","venue":"cs.AI","work_id":"141bc3ea-6f2e-439d-ada6-ffa0902bf68f","year":2025},"citing_paper":{"arxiv_id":"2509.08827","last_updated":"2025-10-09T17:08:52Z","snapshot_observed_at":"2026-08-06T15:38:05.011922Z","submitted_at":"2025-09-10T17:59:43Z","title":"A Survey of Reinforcement Learning for Large Reasoning Models","version":3},"reference_index":283,"source":"arxiv_source","source_observed_at":"2026-05-18T00:02:24.352947Z"},"links":{"cited_paper":"/paper/2508.13167","citing_paper":"/paper/2509.08827"},"observation_digest":"sha256:b1acc71496aa4dc3526adb1efb5bb0088dafc6c0af0492ef8aca2dbf99587cba","observation_id":"1a7eaf6c-332a-4b96-a4a8-af5f03ecb7d2","resolution":{"observed_at":"2026-05-18T00:02:24.747920Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.13167","snapshot_observed_at":"2026-08-04T17:57:52.383322Z","title":"Chain-of-agents: End-to-end agent foundation models via multi-agent distillation and agentic rl","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2509.14257","last_updated":"2026-07-23T12:32:53Z","snapshot_observed_at":"2026-08-13T06:10:20.853148Z","submitted_at":"2025-09-12T15:34:07Z","title":"Student-Centered Distillation Narrows the Agentic Gap Between Small and Large LLMs","version":3},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-04T17:57:52.383322Z"},"links":{"cited_paper":"/paper/2508.13167","citing_paper":"/paper/2509.14257"},"observation_digest":"sha256:c33e3b1a91120498f8a4ca3c4423bd4e1110fe196327abddd41dc2787928c0da","observation_id":"f01437bd-012f-4fca-8e25-4f829e0cd832","resolution":{"observed_at":"2026-08-04T17:57:52.383322Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"cited_work":{"arxiv_id":"2508.13167","doi":"10.48550/arxiv.2508.13167","metadata_source":"pith","pith_arxiv_id":"2508.13167","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Chain-of-agents: End-to-endagentfoundationmodelsviamulti-agentdistillation andagenticRL.arXivpreprint","venue":"cs.AI","work_id":"141bc3ea-6f2e-439d-ada6-ffa0902bf68f","year":2025},"citing_paper":{"arxiv_id":"2511.11793","last_updated":"2026-04-21T16:02:11Z","snapshot_observed_at":"2026-08-14T05:17:40.668746Z","submitted_at":"2025-11-14T18:52:07Z","title":"MiroThinker: Pushing the Performance Boundaries of Open-Source Research Agents via Model, Context, and Interactive Scaling","version":3},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-05-17T21:44:18.744201Z"},"links":{"cited_paper":"/paper/2508.13167","citing_paper":"/paper/2511.11793"},"observation_digest":"sha256:797dddab972777ee3818b05e7374e634e6f53c82c5736f59f60d6d1bbfcd5299","observation_id":"e54dbf78-fc94-44f5-8dbc-82d62a9e7f40","resolution":{"observed_at":"2026-05-17T21:45:17.898438Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"cited_work":{"arxiv_id":"2508.13167","doi":"10.48550/arxiv.2508.13167","metadata_source":"pith","pith_arxiv_id":"2508.13167","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Chain-of-agents: End-to-endagentfoundationmodelsviamulti-agentdistillation andagenticRL.arXivpreprint","venue":"cs.AI","work_id":"141bc3ea-6f2e-439d-ada6-ffa0902bf68f","year":2025},"citing_paper":{"arxiv_id":"2604.06170","last_updated":"2026-04-07T17:59:58Z","snapshot_observed_at":"2026-08-12T18:12:09.712936Z","submitted_at":"2026-04-07T17:59:58Z","title":"Paper Circle: An Open-source Multi-agent Research Discovery and Analysis Framework","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-05-10T18:35:29.920327Z"},"links":{"cited_paper":"/paper/2508.13167","citing_paper":"/paper/2604.06170"},"observation_digest":"sha256:f12e902d46497989ad9ad97d50c26a64ebbf8c83c982366e8ebff9085b524b4b","observation_id":"fcd7cc45-b888-4cd7-b8ac-6651321df2f3","resolution":{"observed_at":"2026-05-11T00:20:53.524753Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"cited_work":{"arxiv_id":"2508.13167","doi":"10.48550/arxiv.2508.13167","metadata_source":"pith","pith_arxiv_id":"2508.13167","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Chain-of-agents: End-to-endagentfoundationmodelsviamulti-agentdistillation andagenticRL.arXivpreprint","venue":"cs.AI","work_id":"141bc3ea-6f2e-439d-ada6-ffa0902bf68f","year":2025},"citing_paper":{"arxiv_id":"2604.17931","last_updated":"2026-07-01T16:44:57Z","snapshot_observed_at":"2026-08-16T15:24:48.776160Z","submitted_at":"2026-04-20T08:11:09Z","title":"LiteResearcher: A Scalable Agentic RL Training Framework for Deep Research Agent","version":4},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-07-05T15:03:50.420072Z"},"links":{"cited_paper":"/paper/2508.13167","citing_paper":"/paper/2604.17931"},"observation_digest":"sha256:f885377d6672a16d7121e89cfa5fed3f64941e1b0513986c194656de8f3ddcf6","observation_id":"45d13e58-7e23-4823-89be-325b8f3bb308","resolution":{"observed_at":"2026-07-05T15:11:10.916430Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"cited_work":{"arxiv_id":"2508.13167","doi":"10.48550/arxiv.2508.13167","metadata_source":"pith","pith_arxiv_id":"2508.13167","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Chain-of-agents: End-to-endagentfoundationmodelsviamulti-agentdistillation andagenticRL.arXivpreprint","venue":"cs.AI","work_id":"141bc3ea-6f2e-439d-ada6-ffa0902bf68f","year":2025},"citing_paper":{"arxiv_id":"2604.18292","last_updated":"2026-04-20T14:01:10Z","snapshot_observed_at":"2026-08-12T18:52:05.625394Z","submitted_at":"2026-04-20T14:01:10Z","title":"Agent-World: Scaling Real-World Environment Synthesis for Evolving General Agent Intelligence","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-05-10T05:24:00.503836Z"},"links":{"cited_paper":"/paper/2508.13167","citing_paper":"/paper/2604.18292"},"observation_digest":"sha256:e9ada31058e7537ec66d1051933e74f0ecb4b9662446a1b29fc10b14c9ae56f5","observation_id":"3c6f6425-b17e-4b45-aa4c-911cbb85099b","resolution":{"observed_at":"2026-05-10T05:25:54.213509Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"cited_work":{"arxiv_id":"2508.13167","doi":"10.48550/arxiv.2508.13167","metadata_source":"pith","pith_arxiv_id":"2508.13167","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Chain-of-agents: End-to-endagentfoundationmodelsviamulti-agentdistillation andagenticRL.arXivpreprint","venue":"cs.AI","work_id":"141bc3ea-6f2e-439d-ada6-ffa0902bf68f","year":2025},"citing_paper":{"arxiv_id":"2605.01347","last_updated":"2026-05-02T09:41:37Z","snapshot_observed_at":"2026-08-15T15:07:23.646536Z","submitted_at":"2026-05-02T09:41:37Z","title":"MAD-OPD: Breaking the Ceiling in On-Policy Distillation via Multi-Agent Debate","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-05-09T15:18:39.844534Z"},"links":{"cited_paper":"/paper/2508.13167","citing_paper":"/paper/2605.01347"},"observation_digest":"sha256:da390565f36a990beb904e8358d20fcd02673a4e84b393fa6ed380a5316af610","observation_id":"61c22ce0-a9eb-4916-b90d-779314c5c6a1","resolution":{"observed_at":"2026-05-11T16:41:20.540725Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"cited_work":{"arxiv_id":"2508.13167","doi":"10.48550/arxiv.2508.13167","metadata_source":"pith","pith_arxiv_id":"2508.13167","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Chain-of-agents: End-to-endagentfoundationmodelsviamulti-agentdistillation andagenticRL.arXivpreprint","venue":"cs.AI","work_id":"141bc3ea-6f2e-439d-ada6-ffa0902bf68f","year":2025},"citing_paper":{"arxiv_id":"2605.04496","last_updated":"2026-05-06T04:55:59Z","snapshot_observed_at":"2026-08-11T17:25:33.075825Z","submitted_at":"2026-05-06T04:55:59Z","title":"SCOUT: Active Information Foraging for Long-Text Understanding with Decoupled Epistemic States","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-05-08T17:20:53.540362Z"},"links":{"cited_paper":"/paper/2508.13167","citing_paper":"/paper/2605.04496"},"observation_digest":"sha256:fe589352b8fac5ed8108f22d9127995300b27c4939dfe55ba6e668af62c22bde","observation_id":"0c4865a5-0294-4186-b4c5-e2124955073d","resolution":{"observed_at":"2026-05-11T17:41:06.769144Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"cited_work":{"arxiv_id":"2508.13167","doi":"10.48550/arxiv.2508.13167","metadata_source":"pith","pith_arxiv_id":"2508.13167","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Chain-of-agents: End-to-endagentfoundationmodelsviamulti-agentdistillation andagenticRL.arXivpreprint","venue":"cs.AI","work_id":"141bc3ea-6f2e-439d-ada6-ffa0902bf68f","year":2025},"citing_paper":{"arxiv_id":"2605.07725","last_updated":"2026-08-18T09:21:07Z","snapshot_observed_at":"2026-08-19T01:29:57.491908Z","submitted_at":"2026-05-08T13:30:42Z","title":"SOD: Step-wise On-policy Distillation for Small Language Model Agents","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-05-11T02:25:59.056181Z"},"links":{"cited_paper":"/paper/2508.13167","citing_paper":"/paper/2605.07725"},"observation_digest":"sha256:c3a91c59ef1b1fc08fc842a11e406c03331bcaf4f8bc6895b4475f6f1f99d145","observation_id":"1e23cc51-8da2-4836-a10b-219e68569a74","resolution":{"observed_at":"2026-05-11T03:40:54.388059Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.13167","snapshot_observed_at":"2026-08-04T05:20:45.013251Z","title":"Chain-of-agents: End-to-end agent foundation models via multi-agent distillation and agentic rl.arXiv preprint arXiv:2508.13167, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.07725","last_updated":"2026-08-18T09:21:07Z","snapshot_observed_at":"2026-08-19T01:29:57.491908Z","submitted_at":"2026-05-08T13:30:42Z","title":"SOD: Step-wise On-policy Distillation for Small Language Model Agents","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-04T05:20:45.013251Z"},"links":{"cited_paper":"/paper/2508.13167","citing_paper":"/paper/2605.07725"},"observation_digest":"sha256:afd9779b0349f17752b1c2762535bc72b7b2cb15932b1281b4f1ecc8f5e2c395","observation_id":"ee513b98-3b27-4c01-8302-c504f50af3bd","resolution":{"observed_at":"2026-08-04T05:20:45.013251Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"cited_work":{"arxiv_id":"2508.13167","doi":"10.48550/arxiv.2508.13167","metadata_source":"pith","pith_arxiv_id":"2508.13167","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Chain-of-agents: End-to-endagentfoundationmodelsviamulti-agentdistillation andagenticRL.arXivpreprint","venue":"cs.AI","work_id":"141bc3ea-6f2e-439d-ada6-ffa0902bf68f","year":2025},"citing_paper":{"arxiv_id":"2605.08124","last_updated":"2026-04-29T12:24:33Z","snapshot_observed_at":"2026-08-11T15:47:49.657646Z","submitted_at":"2026-04-29T12:24:33Z","title":"Scaling Mobile Agent Systems: From Capability Density to Collective Intelligence","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-05-12T00:48:57.036609Z"},"links":{"cited_paper":"/paper/2508.13167","citing_paper":"/paper/2605.08124"},"observation_digest":"sha256:9f5c748662ec91610fcd1c1b02263493fb0e7de289e971e9ebcf31c0d3da1b0e","observation_id":"d7b11d48-501a-4101-8a2e-0be63c69dd0f","resolution":{"observed_at":"2026-05-12T08:41:24.624276Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"cited_work":{"arxiv_id":"2508.13167","doi":"10.48550/arxiv.2508.13167","metadata_source":"pith","pith_arxiv_id":"2508.13167","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Chain-of-agents: End-to-endagentfoundationmodelsviamulti-agentdistillation andagenticRL.arXivpreprint","venue":"cs.AI","work_id":"141bc3ea-6f2e-439d-ada6-ffa0902bf68f","year":2025},"citing_paper":{"arxiv_id":"2605.15224","last_updated":"2026-05-13T08:50:05Z","snapshot_observed_at":"2026-08-08T09:54:59.992034Z","submitted_at":"2026-05-13T08:50:05Z","title":"ICRL: Learning to Internalize Self-Critique with Reinforcement Learning","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-05-19T17:58:05.817581Z"},"links":{"cited_paper":"/paper/2508.13167","citing_paper":"/paper/2605.15224"},"observation_digest":"sha256:3eb8e6cf5b7c51c6b56b2bddae5734409ddb31ee43d5124377f4cb09e0626fd7","observation_id":"f1c0f03c-60f7-42d7-9d7c-12631d1e739a","resolution":{"observed_at":"2026-05-19T18:02:42.426375Z","resolver_source":"arxiv_id","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"cited_work":{"arxiv_id":"2508.13167","doi":"10.48550/arxiv.2508.13167","metadata_source":"pith","pith_arxiv_id":"2508.13167","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Chain-of-agents: End-to-endagentfoundationmodelsviamulti-agentdistillation andagenticRL.arXivpreprint","venue":"cs.AI","work_id":"141bc3ea-6f2e-439d-ada6-ffa0902bf68f","year":2025},"citing_paper":{"arxiv_id":"2605.22138","last_updated":"2026-05-21T08:11:54Z","snapshot_observed_at":"2026-07-06T23:32:30.578213Z","submitted_at":"2026-05-21T08:11:54Z","title":"Efficient Agentic Reasoning Through Self-Regulated Simulative Planning","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-05-22T06:33:36.846345Z"},"links":{"cited_paper":"/paper/2508.13167","citing_paper":"/paper/2605.22138"},"observation_digest":"sha256:00b3a4f78129ca2e2a471fbcdd54bdfbfc67c8b265cb92c82a03641d09b52ed5","observation_id":"d80bed98-4296-410e-bd21-44b6724818ba","resolution":{"observed_at":"2026-05-22T06:34:41.023546Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"cited_work":{"arxiv_id":"2508.13167","doi":"10.48550/arxiv.2508.13167","metadata_source":"pith","pith_arxiv_id":"2508.13167","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Chain-of-agents: End-to-endagentfoundationmodelsviamulti-agentdistillation andagenticRL.arXivpreprint","venue":"cs.AI","work_id":"141bc3ea-6f2e-439d-ada6-ffa0902bf68f","year":2025},"citing_paper":{"arxiv_id":"2606.09138","last_updated":"2026-06-08T07:35:18Z","snapshot_observed_at":"2026-07-06T23:48:30.569726Z","submitted_at":"2026-06-08T07:35:18Z","title":"Claw-R1: A Step-Level Data Middleware System for Agentic Reinforcement Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-06-27T17:28:58.574865Z"},"links":{"cited_paper":"/paper/2508.13167","citing_paper":"/paper/2606.09138"},"observation_digest":"sha256:f613d0f3eff3cb0db73347a4bec59410503bc257a415792158992c40eed726a5","observation_id":"7dae01a7-445c-4944-b479-069ec03d4324","resolution":{"observed_at":"2026-07-03T00:07:28.234364Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"cited_work":{"arxiv_id":"2508.13167","doi":"10.48550/arxiv.2508.13167","metadata_source":"pith","pith_arxiv_id":"2508.13167","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Chain-of-agents: End-to-endagentfoundationmodelsviamulti-agentdistillation andagenticRL.arXivpreprint","venue":"cs.AI","work_id":"141bc3ea-6f2e-439d-ada6-ffa0902bf68f","year":2025},"citing_paper":{"arxiv_id":"2606.12191","last_updated":"2026-06-10T15:15:01Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-06-10T15:15:01Z","title":"Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application","version":1},"reference_index":262,"source":"pdf_text","source_observed_at":"2026-06-27T09:46:30.702256Z"},"links":{"cited_paper":"/paper/2508.13167","citing_paper":"/paper/2606.12191"},"observation_digest":"sha256:99998998fd854cf4b494b807f8dc34b13852c7aa9bd390f6465610c01da16f71","observation_id":"9aa5ba37-f362-4f0e-8428-9d85470c07af","resolution":{"observed_at":"2026-06-27T09:50:48.446656Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"cited_work":{"arxiv_id":"2508.13167","doi":"10.48550/arxiv.2508.13167","metadata_source":"pith","pith_arxiv_id":"2508.13167","snapshot_observed_at":"2026-07-10T12:15:01.137692Z","title":"Chain-of-agents: End-to-endagentfoundationmodelsviamulti-agentdistillation andagenticRL.arXivpreprint","venue":"cs.AI","work_id":"141bc3ea-6f2e-439d-ada6-ffa0902bf68f","year":2025},"citing_paper":{"arxiv_id":"2607.06935","last_updated":"2026-07-08T02:57:22Z","snapshot_observed_at":"2026-08-12T22:38:38.255404Z","submitted_at":"2026-07-08T02:57:22Z","title":"Mathematical methods of reinforcement learning","version":1},"reference_index":121,"source":"pdf_text","source_observed_at":"2026-07-09T22:47:51.676289Z"},"links":{"cited_paper":"/paper/2508.13167","citing_paper":"/paper/2607.06935"},"observation_digest":"sha256:ed1adfbbbc3c9010f5c966109d25e552bc4fd26102df595bcc284674c3b758c4","observation_id":"b1b9892c-b075-46dc-8e03-96c7a9adf5cc","resolution":{"observed_at":"2026-07-09T22:56:37.743625Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2508.13167/citation-record","integrity":"/paper/2508.13167/integrity","json":"/paper/2508.13167/citation-record.json","paper":"/paper/2508.13167"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2505.24480","last_updated":"2025-05-30T11:30:18Z","snapshot_observed_at":"2026-08-16T19:00:49.578371Z","submitted_at":"2025-05-30T11:30:18Z","title":"Towards Effective Code-Integrated Reasoning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.24480","snapshot_observed_at":"2026-08-05T23:57:31.528964Z","title":"Towards effective code-integrated reasoning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:31.528964Z"},"links":{"cited_paper":"/paper/2505.24480","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:4d09929c4def9082c59af9bc23b5936585a36e08703fd6c36a8d27bbb0c57165","observation_id":"03a0143b-e32f-42b7-8b9a-3f79642a9777","resolution":{"observed_at":"2026-08-05T23:57:31.528964Z","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":"2026-08-05T23:57:31.628867Z","title":"Multi-agent reinforcement learning: A review of challenges and applications","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:31.628867Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:8c3c57aefb9b486979a62f5ac37d5ab3845494437a4a3aad40705bf0a60c7cef","observation_id":"aec18274-6b2c-4ac8-8320-231110c0218b","resolution":{"observed_at":"2026-08-05T23:57:31.628867Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.19470","last_updated":"2025-09-23T03:45:42Z","snapshot_observed_at":"2026-08-17T01:02:33.957223Z","submitted_at":"2025-03-25T09:00:58Z","title":"ReSearch: Learning to Reason with Search for LLMs via Reinforcement Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.19470","snapshot_observed_at":"2026-08-05T23:57:31.713985Z","title":"Learning to reason with search for llms via reinforcement learning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:31.713985Z"},"links":{"cited_paper":"/paper/2503.19470","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:038d3af5c7ff242e21ec5c758e58e0d41d4820b033b346e683bd338d66a457ac","observation_id":"5d9241b2-eeb8-4ea6-b3a8-f5ced24ab1b6","resolution":{"observed_at":"2026-08-05T23:57:31.713985Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2502.01456","last_updated":"2025-09-26T09:25:31Z","snapshot_observed_at":"2026-08-17T04:59:59.434643Z","submitted_at":"2025-02-03T15:43:48Z","title":"Process Reinforcement through Implicit Rewards","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2502.01456","snapshot_observed_at":"2026-08-05T23:57:31.801869Z","title":"Process reinforcement through implicit rewards","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:31.801869Z"},"links":{"cited_paper":"/paper/2502.01456","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:1e0f6d55d2a2c49f7e7d34ec8b547be373eb9b505b7179356ce9bbccc6460d53","observation_id":"78de714f-8f74-4c57-b429-3147a784ba24","resolution":{"observed_at":"2026-08-05T23:57:31.801869Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.16410","last_updated":"2025-05-22T09:00:19Z","snapshot_observed_at":"2026-08-17T08:52:56.038060Z","submitted_at":"2025-05-22T09:00:19Z","title":"Tool-Star: Empowering LLM-Brained Multi-Tool Reasoner via Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.16410","snapshot_observed_at":"2026-08-05T23:57:31.860107Z","title":"Tool-star: Empowering llm-brained multi-tool reasoner via reinforcement learning.arXiv preprint arXiv:2505.16410, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:31.860107Z"},"links":{"cited_paper":"/paper/2505.16410","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:8aa2238bc77c9a125278189d8558227f686022f6e9b2b2b6e90710c343ab0a9d","observation_id":"6d12bb68-7528-4280-9e78-e7c7d7d238ba","resolution":{"observed_at":"2026-08-05T23:57:31.860107Z","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":"2026-08-05T23:57:31.976176Z","title":"Multi-agent systems: A survey","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:31.976176Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:ce9adcc3db5b3f7d4257fd1973ff164dcf8a436b2d9926e4812fca7d6ab02a6c","observation_id":"f938d6e4-c9ab-470b-bbbe-fa16b4fd35a4","resolution":{"observed_at":"2026-08-05T23:57:31.976176Z","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":"2026-08-05T23:57:32.038564Z","title":"Retool: Reinforcement learning for strategic tool use in llms","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:32.038564Z"},"links":{"cited_paper":"/paper/2504.11536","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:c6a732457423747d27d2645762d69aeff39ecf9596402783d17394f94113ef5b","observation_id":"b2afc82b-2a1a-495a-a462-851c017dea1c","resolution":{"observed_at":"2026-08-05T23:57:32.038564Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.10053","last_updated":"2025-08-27T07:38:28Z","snapshot_observed_at":"2026-08-17T04:16:19.632984Z","submitted_at":"2025-01-17T09:16:13Z","title":"AirRAG: Autonomous Strategic Planning and Reasoning Steer Retrieval Augmented Generation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.10053","snapshot_observed_at":"2026-08-05T23:57:32.134517Z","title":"Airrag: Activating intrinsic reasoning for retrieval augmented generation via tree-based search","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:32.134517Z"},"links":{"cited_paper":"/paper/2501.10053","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:fb59b84fab4b1982abe1efe47a48d918c88300aff794b99c70d0cc926a2f69b9","observation_id":"d7c323eb-4fe7-4925-8e5c-cd209bf84eb6","resolution":{"observed_at":"2026-08-05T23:57:32.134517Z","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":"2026-08-15T12:33:55.451951Z","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":"2026-08-05T23:57:32.221011Z","title":"Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:32.221011Z"},"links":{"cited_paper":"/paper/2501.12948","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:1947bb87b372725c0138b920e6d8bcf4ec32fccba47b974f070ca356d3ec74c0","observation_id":"1ebec6f0-8c81-4910-8bcf-7fee5a73e4fa","resolution":{"observed_at":"2026-08-05T23:57:32.221011Z","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":"2026-08-05T23:57:32.315489Z","title":"How we built our multi-agent research system","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:32.315489Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:77231c131d90e86e98a3434eee851b9377ee050f0a89f5b49c0915887f93dce4","observation_id":"446c71b1-1cc7-47ac-bd20-23c2c81e9da5","resolution":{"observed_at":"2026-08-05T23:57:32.315489Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2402.14008","last_updated":"2024-06-06T13:19:44Z","snapshot_observed_at":"2026-08-03T03:39:09.398343Z","submitted_at":"2024-02-21T18:49:26Z","title":"OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.14008","snapshot_observed_at":"2026-08-05T23:57:32.437149Z","title":"Olympiadbench: A challenging benchmark for promoting agi with olympiad-level bilingual multimodal scientific problems","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:32.437149Z"},"links":{"cited_paper":"/paper/2402.14008","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:b6eec069cc3b6aef47426beafa5ea7060035bcc0ebe1dc3ebd44696c9875fb77","observation_id":"94b0014d-7d06-4d5c-aa20-005f30600bee","resolution":{"observed_at":"2026-08-05T23:57:32.437149Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.22312","last_updated":"2025-05-29T09:07:33Z","snapshot_observed_at":"2026-08-15T23:24:42.542733Z","submitted_at":"2025-05-28T12:56:04Z","title":"Skywork Open Reasoner 1 Technical Report","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.22312","snapshot_observed_at":"2026-08-05T23:57:32.471543Z","title":"Skywork open reasoner 1 technical report","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:32.471543Z"},"links":{"cited_paper":"/paper/2505.22312","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:7b8f4890887b51793ada218a1196718f32c4fdea78fa24f1ec67f74481687671","observation_id":"d13b30e1-d8ff-4535-975a-5062af49d861","resolution":{"observed_at":"2026-08-05T23:57:32.471543Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2011.01060","last_updated":"2020-11-12T07:47:48Z","snapshot_observed_at":"2026-08-14T05:50:17.249446Z","submitted_at":"2020-11-02T15:42:40Z","title":"Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2011.01060","snapshot_observed_at":"2026-08-05T23:57:32.528248Z","title":"Constructing a multi-hop qa dataset for comprehensive evaluation of reasoning steps","venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:32.528248Z"},"links":{"cited_paper":"/paper/2011.01060","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:a21201a6fb88768fcd847f51e22aedaf97a3c7095f581a8021f54e17a6f4c344","observation_id":"b85af062-d1d0-499d-b050-0154e419afa7","resolution":{"observed_at":"2026-08-05T23:57:32.528248Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.23885","last_updated":"2025-06-11T01:42:53Z","snapshot_observed_at":"2026-08-15T07:48:24.532787Z","submitted_at":"2025-05-29T17:51:58Z","title":"OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.23885","snapshot_observed_at":"2026-08-05T23:57:32.626007Z","title":"Owl: Optimized workforce learning for general multi-agent assistance in real-world task automation, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:32.626007Z"},"links":{"cited_paper":"/paper/2505.23885","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:530c731cf52eb39d0bcd1401556609700ef17ef14bd4b88227adf77a9abd9a97","observation_id":"c6f539eb-1a58-4bed-a208-5a9535f3dc05","resolution":{"observed_at":"2026-08-05T23:57:32.626007Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2312.13010","last_updated":"2024-05-24T11:47:24Z","snapshot_observed_at":"2026-08-14T19:02:11.924748Z","submitted_at":"2023-12-20T13:22:41Z","title":"AgentCoder: Multi-Agent-based Code Generation with Iterative Testing and Optimisation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2312.13010","snapshot_observed_at":"2026-08-05T23:57:32.689715Z","title":"Agentcoder: Multi-agent-based code generation with iterative testing and optimisation","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:32.689715Z"},"links":{"cited_paper":"/paper/2312.13010","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:3d4ca843460d0b423f3f50d68ae62617c6b6c2e243e2ba440904086ea12a50d2","observation_id":"f0fcd6a6-61df-4133-afe5-f7e729f6796b","resolution":{"observed_at":"2026-08-05T23:57:32.689715Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.12186","last_updated":"2024-11-12T13:24:25Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-09-18T17:57:57Z","title":"Qwen2.5-Coder Technical Report","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.12186","snapshot_observed_at":"2026-08-05T23:57:32.723395Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:32.723395Z"},"links":{"cited_paper":"/paper/2409.12186","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:9cadcbfdd5e041433298391ab2a429b27b86f08796e1313749bdb44b8f92ba12","observation_id":"569bcbb2-9b69-42af-8c16-e24c18f1e10a","resolution":{"observed_at":"2026-08-05T23:57:32.723395Z","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":"2026-08-05T23:57:32.791294Z","title":"Ashraful Islam, Mohammed Eunus Ali, and Md Rizwan Parvez","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:32.791294Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:8ef6bd85054e37a151dca268f158185f439bb597b5d3367f9d279f776c9fb7ae","observation_id":"5ba5575f-73c2-4c8c-b042-4dddfe5900d2","resolution":{"observed_at":"2026-08-05T23:57:32.791294Z","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":"2026-08-05T23:57:32.820793Z","title":"Ashraful Islam, Mohammed Eunus Ali, and Md Rizwan Parvez","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:32.820793Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:7a4ffd7329ccf91acb88f47fec115acd451c346bd27fef290b66ff92e6f0b8a3","observation_id":"ab7cd166-2d4f-424c-b04a-ff638f752448","resolution":{"observed_at":"2026-08-05T23:57:32.820793Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.07974","last_updated":"2024-06-06T17:41:21Z","snapshot_observed_at":"2026-08-16T07:05:57.323612Z","submitted_at":"2024-03-12T17:58:04Z","title":"LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.07974","snapshot_observed_at":"2026-08-05T23:57:32.881563Z","title":"Livecodebench: Holistic and contamination free evaluation of large language models for code","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:32.881563Z"},"links":{"cited_paper":"/paper/2403.07974","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:1545c42389375add8165d8605121ab085cf5f67b6c181d64a09e8fba696834f7","observation_id":"56debdf8-3f47-45cf-abac-8b59149eb863","resolution":{"observed_at":"2026-08-05T23:57:32.881563Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.09516","last_updated":"2025-08-05T19:08:38Z","snapshot_observed_at":"2026-08-15T13:17:00.526689Z","submitted_at":"2025-03-12T16:26:39Z","title":"Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.09516","snapshot_observed_at":"2026-08-05T23:57:32.948738Z","title":"Search-r1: Training llms to reason and leverage search engines with reinforcement learning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:32.948738Z"},"links":{"cited_paper":"/paper/2503.09516","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:6ebf6a93ae97e05aedfb657f7aad751a73e52615bf9f72e3ae35688d560a3c1a","observation_id":"0e843709-2b3a-4e48-b8a1-38af653cd782","resolution":{"observed_at":"2026-08-05T23:57:32.948738Z","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":"2026-08-05T23:57:32.984142Z","title":"Reveal: Self-evolving code agents via iterative generation-verification, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:32.984142Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:10c9acf5e06c0b4c99a5ba7e779f578f996a082ce1191d1e56e7e346f0e10cf0","observation_id":"f375e221-b36a-49db-b778-fcbd77e91648","resolution":{"observed_at":"2026-08-05T23:57:32.984142Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1705.03551","last_updated":"2017-05-13T21:12:37Z","snapshot_observed_at":"2026-08-13T08:58:47.096400Z","submitted_at":"2017-05-09T21:35:07Z","title":"TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1705.03551","snapshot_observed_at":"2026-08-05T23:57:33.011052Z","title":"Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:33.011052Z"},"links":{"cited_paper":"/paper/1705.03551","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:31b309e9bae689c2973bd86600f401538b42339f84a68a733df8ef5ffb90b7d6","observation_id":"8b88e872-4291-459d-b147-7b7809f15a36","resolution":{"observed_at":"2026-08-05T23:57:33.011052Z","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":"2026-08-05T23:57:33.095474Z","title":"Sequence-level knowledge distillation","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:33.095474Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:0d00cb4f3496475d04de10e588e1cbb5a0bff15799f6c0563322ce9b4e584102","observation_id":"491219c8-5731-4cf3-9ead-c526dd628817","resolution":{"observed_at":"2026-08-05T23:57:33.095474Z","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":"2026-08-05T23:57:33.161310Z","title":"Natural questions: a benchmark for question answering research","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:33.161310Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:894389d4396396361a895ce3c8d2c589c8c1f0014bb96b64a5f43d30e6417f31","observation_id":"4d480276-2117-48db-bed9-e2b3747df3f8","resolution":{"observed_at":"2026-08-05T23:57:33.161310Z","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":"2026-08-05T23:57:33.222898Z","title":"Camel: Communicative agents for \"mind\" exploration of large language model society","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:33.222898Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:98a17407f8cf52bae89c4656003bb937102670496e51d49e80e365cff73019e1","observation_id":"1a6955b5-0238-40d6-8a10-fabf57a2607e","resolution":{"observed_at":"2026-08-05T23:57:33.222898Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.02592","last_updated":"2025-07-03T12:59:07Z","snapshot_observed_at":"2026-08-12T16:37:55.786203Z","submitted_at":"2025-07-03T12:59:07Z","title":"WebSailor: Navigating Super-human Reasoning for Web Agent","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.02592","snapshot_observed_at":"2026-08-05T23:57:33.317703Z","title":"Websailor: Navigating super-human reasoning for web agent, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:33.317703Z"},"links":{"cited_paper":"/paper/2507.02592","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:9f595f08e2213a4f9af392909a484bebdf0caa697e84248b9e6ac0e45b0f2bf9","observation_id":"35eff444-9688-4119-b2fc-d4120c69eaff","resolution":{"observed_at":"2026-08-05T23:57:33.317703Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.05366","last_updated":"2025-01-09T16:48:17Z","snapshot_observed_at":"2026-08-15T17:19:59.096854Z","submitted_at":"2025-01-09T16:48:17Z","title":"Search-o1: Agentic Search-Enhanced Large Reasoning Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.05366","snapshot_observed_at":"2026-08-05T23:57:33.383273Z","title":"Search-o1: Agentic search-enhanced large reasoning models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:33.383273Z"},"links":{"cited_paper":"/paper/2501.05366","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:4929ea5ec394585cd8dea84885546057f77f99f8e8d900a90d184325f0abb8d7","observation_id":"d45b9d34-84d0-4829-ab88-b378f61b7e81","resolution":{"observed_at":"2026-08-05T23:57:33.383273Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.21776","last_updated":"2025-10-13T12:40:15Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-04-30T16:25:25Z","title":"WebThinker: Empowering Large Reasoning Models with Deep Research Capability","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.21776","snapshot_observed_at":"2026-08-05T23:57:33.450894Z","title":"Webthinker: Empowering large reasoning models with deep research capability","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:33.450894Z"},"links":{"cited_paper":"/paper/2504.21776","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:bae4d92b78e1c7f4a44b9931ae8d104644a052d9d57f1e390f451b4e2a593a64","observation_id":"b7497d46-9b24-4489-a3a3-658154dbf6b9","resolution":{"observed_at":"2026-08-05T23:57:33.450894Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.23383","last_updated":"2025-03-30T10:16:25Z","snapshot_observed_at":"2026-08-18T07:08:35.277799Z","submitted_at":"2025-03-30T10:16:25Z","title":"ToRL: Scaling Tool-Integrated RL","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.23383","snapshot_observed_at":"2026-08-05T23:57:33.485908Z","title":"Torl: Scaling tool-integrated rl","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:33.485908Z"},"links":{"cited_paper":"/paper/2503.23383","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:277d40df74de7ec9b00cb2b8440d63f1f4e6888401c3ca22ed0ea6bbf7f5d3a8","observation_id":"0824de45-a673-4ba2-a2a3-3a113de407a4","resolution":{"observed_at":"2026-08-05T23:57:33.485908Z","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":"2026-08-05T23:57:33.551858Z","title":"Competition-level code generation with alphacode","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:33.551858Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:709cf32bec2fc4cfb3a9ebc33588ac54c848c917f0da6d23ea0e61d038f1d5b7","observation_id":"d780e1ec-75b0-4e6b-a57a-888b514102d9","resolution":{"observed_at":"2026-08-05T23:57:33.551858Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.20050","last_updated":"2023-05-31T17:24:00Z","snapshot_observed_at":"2026-08-17T09:42:34.746112Z","submitted_at":"2023-05-31T17:24:00Z","title":"Let's Verify Step by Step","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.20050","snapshot_observed_at":"2026-08-05T23:57:33.617991Z","title":"Let’s verify step by step","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:33.617991Z"},"links":{"cited_paper":"/paper/2305.20050","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:db88765a242007af5bbcbd0756fc0cd5b6781fac2fa575ab3641ed66a9166e73","observation_id":"9af51948-67fd-470a-bb5f-24d2555930fa","resolution":{"observed_at":"2026-08-05T23:57:33.617991Z","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":"2026-08-05T23:57:33.681349Z","title":"Inference-time scaling for generalist reward modeling","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:33.681349Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:56fd1d2d4a2f6358c81af1816f4759fe7f068471e91838b5ba21afeb37fc4a4a","observation_id":"56168054-a414-4018-b0d5-23cf642948e2","resolution":{"observed_at":"2026-08-05T23:57:33.681349Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.05101","last_updated":"2019-01-04T21:01:49Z","snapshot_observed_at":"2026-08-14T20:13:52.872565Z","submitted_at":"2017-11-14T14:24:06Z","title":"Decoupled Weight Decay Regularization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.05101","snapshot_observed_at":"2026-08-05T23:57:33.741732Z","title":"Decoupled weight decay regularization","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:33.741732Z"},"links":{"cited_paper":"/paper/1711.05101","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:57efb61cde057f50ce05a34df0d2520e28a473c2c156f9763d91d0820d20d0c6","observation_id":"daeccf5a-4e54-4b1d-99e2-96006c7d481b","resolution":{"observed_at":"2026-08-05T23:57:33.741732Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.07773","last_updated":"2025-08-20T12:20:55Z","snapshot_observed_at":"2026-08-16T03:27:31.121559Z","submitted_at":"2025-05-12T17:23:34Z","title":"Agent RL Scaling Law: Agent RL with Spontaneous Code Execution for Mathematical Problem Solving","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.07773","snapshot_observed_at":"2026-08-05T23:57:33.803769Z","title":"Agent rl scaling law: Agent rl with spontaneous code execution for mathematical problem solving","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:33.803769Z"},"links":{"cited_paper":"/paper/2505.07773","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:8dcd54f4c8b8f29fdc07fd87b1ca672c3bb5def31e79e5bde4760747ccdae8a7","observation_id":"82b74efe-b908-4be7-8a9d-f20ce2219763","resolution":{"observed_at":"2026-08-05T23:57:33.803769Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.10511","last_updated":"2023-07-02T07:21:59Z","snapshot_observed_at":"2026-07-06T14:33:08.041820Z","submitted_at":"2022-12-20T18:30:15Z","title":"When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.10511","snapshot_observed_at":"2026-08-05T23:57:33.871288Z","title":"When not to trust language models: Investigating effectiveness of parametric and non-parametric memories","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:33.871288Z"},"links":{"cited_paper":"/paper/2212.10511","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:63029f0178756c25a911e0d30d909a129a2eac7574c0ae16398f7af6b87aba51","observation_id":"c9526066-f0ec-4fa6-9041-eb7abad91742","resolution":{"observed_at":"2026-08-05T23:57:33.871288Z","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":"2026-08-05T23:57:33.961254Z","title":"Gaia: a benchmark for general ai assistants","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:33.961254Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:1baa41ad126634e0cc3e3e6ab1708a7ef33cb5a24d1ba7d84365b45d8eee7d9f","observation_id":"c3dda23f-b55c-45f0-a2ee-e71e1aa333ff","resolution":{"observed_at":"2026-08-05T23:57:33.961254Z","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":"2026-08-05T23:57:33.994732Z","title":"American invitational mathematics examination (aime) 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:33.994732Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:326f54943deea9fe8437d961ff7dab5577e745c7db6d8d4e0e58b255ce330532","observation_id":"12f83a6e-a7c5-4102-801f-ee5284d61ecb","resolution":{"observed_at":"2026-08-05T23:57:33.994732Z","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":"2026-08-05T23:57:34.028802Z","title":"American invitational mathematics examination (aime) 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:34.028802Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:3897139cbaa62537c6dbfacc5d6e50111ed62e5734e58cdf03c57ef528ded230","observation_id":"71e45e29-ac31-4ee9-a15a-c1cf165fd34a","resolution":{"observed_at":"2026-08-05T23:57:34.028802Z","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":"2026-08-05T23:57:34.061528Z","title":"Codeforces","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:34.061528Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:1e1c5f112b5770e7c1c6bf0d0f3bf1f35a7eaec6eb8da756fb1583790d1a25be","observation_id":"dd56ec39-9d2e-4dd0-9d70-fd0e6f2ab48b","resolution":{"observed_at":"2026-08-05T23:57:34.061528Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.14249","last_updated":"2026-02-20T04:23:01Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-01-24T05:27:46Z","title":"Humanity's Last Exam","version":10},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.14249","snapshot_observed_at":"2026-08-05T23:57:34.124997Z","title":"Humanity’s last exam","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:34.124997Z"},"links":{"cited_paper":"/paper/2501.14249","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:6199f2eabe499be80f43bd97f6d9192a31eefc39e07adda1f63d1022cbd6c15a","observation_id":"57664f53-66ae-40ee-bf40-62956e47ff95","resolution":{"observed_at":"2026-08-05T23:57:34.124997Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2210.03350","last_updated":"2023-10-17T18:57:17Z","snapshot_observed_at":"2026-08-15T17:25:43.175408Z","submitted_at":"2022-10-07T06:50:23Z","title":"Measuring and Narrowing the Compositionality Gap in Language Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2210.03350","snapshot_observed_at":"2026-08-05T23:57:34.183382Z","title":"Measuring and narrowing the compositionality gap in language models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:34.183382Z"},"links":{"cited_paper":"/paper/2210.03350","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:1f11320d6b94161ed1bc1d469cc3c4e292732038cbee7304caa18e3945257b60","observation_id":"ce665dea-d3f5-4d73-860c-e3fdff5debb1","resolution":{"observed_at":"2026-08-05T23:57:34.183382Z","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":"2026-08-15T04:58:53.543603Z","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":"2026-08-05T23:57:34.215415Z","title":"Toolrl: Reward is all tool learning needs","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:34.215415Z"},"links":{"cited_paper":"/paper/2504.13958","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:fe1f0a02daa6ec98c7f702d5541a3d46d04673745b4968ec08bd40cd120bc677","observation_id":"9f7c568a-a605-4617-984f-dbfdab6eaa81","resolution":{"observed_at":"2026-08-05T23:57:34.215415Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.20286","last_updated":"2025-05-26T17:58:53Z","snapshot_observed_at":"2026-08-17T12:00:38.078645Z","submitted_at":"2025-05-26T17:58:53Z","title":"Alita: Generalist Agent Enabling Scalable Agentic Reasoning with Minimal Predefinition and Maximal Self-Evolution","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.20286","snapshot_observed_at":"2026-08-05T23:57:34.272995Z","title":"Alita: Generalist agent enabling scalable agentic reasoning with minimal predefinition and maximal self-evolution","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:34.272995Z"},"links":{"cited_paper":"/paper/2505.20286","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:09959cbc681bd8f5931759fbfdad3d9dec73f5c8558c2b4ec650290d4b9e72b1","observation_id":"bc2447ea-9700-4597-8a65-008dda813d42","resolution":{"observed_at":"2026-08-05T23:57:34.272995Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2412.15115","last_updated":"2025-01-03T02:18:21Z","snapshot_observed_at":"2026-08-17T18:50:07.059564Z","submitted_at":"2024-12-19T17:56:09Z","title":"Qwen2.5 Technical Report","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.15115","snapshot_observed_at":"2026-08-05T23:57:34.308026Z","title":"Qwen2.5 technical report, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:34.308026Z"},"links":{"cited_paper":"/paper/2412.15115","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:5d7c211fd43fc450bf29e5b72cb4a9b06dbdd5c624c600c5b1d9b14cef34f931","observation_id":"05afe5d6-a392-4423-9300-045722a72b91","resolution":{"observed_at":"2026-08-05T23:57:34.308026Z","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":"2026-08-05T23:57:34.367351Z","title":"‘smolagents‘: a smol library to build great agentic systems.https://github.com/huggingface/smolagents, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:34.367351Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:a2a6b95df8b97c690c281e21f07ccb65dc6c7ad993896fc6256dfd8ea99c5068","observation_id":"4f0b6d47-7d03-47a1-b67e-6530e49c7431","resolution":{"observed_at":"2026-08-05T23:57:34.367351Z","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":"2026-08-06T14:58:42.911363Z","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":"2026-08-05T23:57:34.486689Z","title":"Deepseekmath: Pushing the limits of mathematical reasoning in open language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:34.486689Z"},"links":{"cited_paper":"/paper/2402.03300","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:ec690fcdc761c197d4b06ca215011c9b66f25806612854d489231d4cd31d3174","observation_id":"3067b16b-3f58-4d30-8537-c6cba9e32084","resolution":{"observed_at":"2026-08-05T23:57:34.486689Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.19256","last_updated":"2024-10-02T04:01:47Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-09-28T06:20:03Z","title":"HybridFlow: A Flexible and Efficient RLHF Framework","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.19256","snapshot_observed_at":"2026-08-05T23:57:34.579121Z","title":"Hybridflow: A flexible and efficient rlhf framework","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:34.579121Z"},"links":{"cited_paper":"/paper/2409.19256","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:8e9116705ec008b0d3f4b7dbd9cf2f9a364eea3d51a60376b44aa70ebc79927f","observation_id":"d21dd24e-8f78-4877-99fa-aac28be7692b","resolution":{"observed_at":"2026-08-05T23:57:34.579121Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.10055","last_updated":"2025-06-17T15:19:26Z","snapshot_observed_at":"2026-08-15T23:44:32.992872Z","submitted_at":"2025-06-11T17:58:14Z","title":"TaskCraft: Automated Generation of Agentic Tasks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.10055","snapshot_observed_at":"2026-08-05T23:57:34.611121Z","title":"Taskcraft: Automated generation of agentic tasks","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:34.611121Z"},"links":{"cited_paper":"/paper/2506.10055","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:beddfc474246f8ffa26b6fbbf4392075d6bd3e9b3ddf9a1891a35fcd5655c12b","observation_id":"6813e7ef-1462-4ad8-84b5-999c92740970","resolution":{"observed_at":"2026-08-05T23:57:34.611121Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.05592","last_updated":"2025-03-18T08:32:24Z","snapshot_observed_at":"2026-08-15T04:19:26.319323Z","submitted_at":"2025-03-07T17:14:44Z","title":"R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.05592","snapshot_observed_at":"2026-08-05T23:57:34.684171Z","title":"R1-searcher: Incentivizing the search capability in llms via reinforcement learning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:34.684171Z"},"links":{"cited_paper":"/paper/2503.05592","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:b01581c549e9e648c52ed7b6682358a6cc5224b1e096bc29739acc76ea9dbca3","observation_id":"ad93aa0b-f9c9-45de-b47f-7ae4d9c8160f","resolution":{"observed_at":"2026-08-05T23:57:34.684171Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.04588","last_updated":"2026-05-19T11:51:42Z","snapshot_observed_at":"2026-08-04T18:36:41.979541Z","submitted_at":"2025-05-07T17:30:22Z","title":"ZeroSearch: Incentivize the Search Capability of LLMs without Searching","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.04588","snapshot_observed_at":"2026-08-05T23:57:34.744588Z","title":"Zerosearch: Incentivize the search capability of llms without searching","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:34.744588Z"},"links":{"cited_paper":"/paper/2505.04588","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:b62c6b42c03b7abb2220c5213b85576084347c64ba684a02edddd8a81cd70718","observation_id":"5d5138e2-3aec-4a06-852f-36d9df1b15c8","resolution":{"observed_at":"2026-08-05T23:57:34.744588Z","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":"2026-08-05T23:57:34.819776Z","title":"Simpledeepsearcher: Deep information seeking via web-powered reasoning trajectory synthesis","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:34.819776Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:ee6f657ebddb1ca20c99e3103d05fdd547c2baf8dad9df51a81facf7b31bfa54","observation_id":"b0e531ab-4bc4-429e-996f-fb502cc01dd8","resolution":{"observed_at":"2026-08-05T23:57:34.819776Z","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":"2026-08-05T23:57:34.891354Z","title":"Agent kb: Leveraging cross-domain experience for agentic problem solving","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:34.891354Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:9a03c786d98c91cc2ae19d18d2b56f01e78532bb683d8872c4aaab72e7c73b4f","observation_id":"2b63bb7a-bc4c-493c-a671-6eb18354746d","resolution":{"observed_at":"2026-08-05T23:57:34.891354Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.15061","last_updated":"2025-07-20T17:53:37Z","snapshot_observed_at":"2026-08-14T03:54:33.678437Z","submitted_at":"2025-07-20T17:53:37Z","title":"WebShaper: Agentically Data Synthesizing via Information-Seeking Formalization","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.15061","snapshot_observed_at":"2026-08-05T23:57:35.000344Z","title":"Webshaper: Agentically data synthesizing via information-seeking formalization, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:35.000344Z"},"links":{"cited_paper":"/paper/2507.15061","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:ac0167795f2cda80b89dcfdc3ff266ad90d38c200d04434ef2b15963daa157ac","observation_id":"a0fc01ef-b2d6-40a4-bf48-752f4cd7f1bf","resolution":{"observed_at":"2026-08-05T23:57:35.000344Z","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":"2026-08-05T23:57:35.101608Z","title":"Qwq: Reflect deeply on the boundaries of the unknown, November 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:35.101608Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:427709cd5cd61b50a68963f31671e1284b28763759f401631ba314ce3ad99a93","observation_id":"997d1e1f-332b-40b3-8ef6-f4b208246bd0","resolution":{"observed_at":"2026-08-05T23:57:35.101608Z","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":"2026-08-05T23:57:35.157650Z","title":"Verl-tool: A version of verl to support tool use, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:35.157650Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:7a295e8219fdf1f7ba8df9969ffbe182728cab71664d169a5bba42c6714d47e7","observation_id":"7e68af04-f22e-4ed1-9c4a-d3c81c4ded6d","resolution":{"observed_at":"2026-08-05T23:57:35.157650Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2212.10509","last_updated":"2023-06-23T00:59:13Z","snapshot_observed_at":"2026-08-15T17:29:02.957931Z","submitted_at":"2022-12-20T18:26:34Z","title":"Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2212.10509","snapshot_observed_at":"2026-08-05T23:57:35.272023Z","title":"Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questions","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:35.272023Z"},"links":{"cited_paper":"/paper/2212.10509","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:126aab1e9d085a1ae2466a5a7b806cc5bf450b783beefed155b9275618f08451","observation_id":"dd42928b-5df2-4c13-9dca-a8e7060461e5","resolution":{"observed_at":"2026-08-05T23:57:35.272023Z","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":"2026-08-05T23:57:35.312642Z","title":"Musique: Multihop questions via single-hop question composition","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:35.312642Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:24369c3b488ba2b201d4f355850cfc2be2d22f114e6eb4d9ec7eede3fc31e470","observation_id":"978d58b2-0f67-45da-9586-f4a534701f94","resolution":{"observed_at":"2026-08-05T23:57:35.312642Z","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":"2026-08-05T23:57:35.336906Z","title":"Otc: Optimal tool calls via reinforcement learning","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:35.336906Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:ef8fb14df7beb9a5e832c88d9f7dbc7008642f3a24cfa0facb7b9a42acd23792","observation_id":"d91c3373-7184-4626-9b61-c92451bd1110","resolution":{"observed_at":"2026-08-05T23:57:35.336906Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.15107","last_updated":"2025-05-26T04:44:21Z","snapshot_observed_at":"2026-08-16T11:26:30.047401Z","submitted_at":"2025-05-21T05:01:31Z","title":"StepSearch: Igniting LLMs Search Ability via Step-Wise Proximal Policy Optimization","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.15107","snapshot_observed_at":"2026-08-05T23:57:35.446112Z","title":"Stepsearch: Igniting llms search ability via step-wise proximal policy optimization","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:35.446112Z"},"links":{"cited_paper":"/paper/2505.15107","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:c7f267ba783f0e3f4d499fc40b949fadd853223e5345a0793b27bafbb2e336a5","observation_id":"47a1e444-233c-42af-bd0b-a92e31b51857","resolution":{"observed_at":"2026-08-05T23:57:35.446112Z","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":"2026-08-05T23:57:35.521369Z","title":"Corag: A cost-constrained retrieval optimization system for retrieval-augmented generation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:35.521369Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:ca62509598fdf5ba538bd8c9f0902f702f45e53c0f995b290c2fead0da8ac505","observation_id":"ff451199-ebf8-41dd-a9cf-d0eae2555210","resolution":{"observed_at":"2026-08-05T23:57:35.521369Z","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":"2026-08-05T23:57:35.594243Z","title":"Chain-of-thought prompting elicits reasoning in large language models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:35.594243Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:049fa4fda64eb54345284b742bfc45dfcc232be12cb7ef392755ecbce54284ff","observation_id":"81e9dadf-6486-4031-b452-4f976bd78665","resolution":{"observed_at":"2026-08-05T23:57:35.594243Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.12516","last_updated":"2025-04-16T22:27:45Z","snapshot_observed_at":"2026-08-13T03:19:29.377723Z","submitted_at":"2025-04-16T22:27:45Z","title":"BrowseComp: A Simple Yet Challenging Benchmark for Browsing Agents","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.12516","snapshot_observed_at":"2026-08-05T23:57:35.713967Z","title":"Browsecomp: A simple yet challenging benchmark for browsing agents, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:35.713967Z"},"links":{"cited_paper":"/paper/2504.12516","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:e6160897d1641b53b3030cc2f1187a3d2ae3241f063705d70bcdbf21d41d80ce","observation_id":"c4d45d17-24ac-4d7b-9ab3-b79be03868be","resolution":{"observed_at":"2026-08-05T23:57:35.713967Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.21836","last_updated":"2025-07-29T14:12:28Z","snapshot_observed_at":"2026-08-17T18:27:46.120184Z","submitted_at":"2025-07-29T14:12:28Z","title":"AutoTIR: Autonomous Tools Integrated Reasoning via Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.21836","snapshot_observed_at":"2026-08-05T23:57:35.839876Z","title":"Autotir: Autonomous tools integrated reasoning via reinforcement learning, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:35.839876Z"},"links":{"cited_paper":"/paper/2507.21836","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:54da19906f7a84f023db003493ca30a778ff980d29b1e0967b0de4a74c8fdeaa","observation_id":"be2f730c-502f-49ba-afd5-8c9677e23336","resolution":{"observed_at":"2026-08-05T23:57:35.839876Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.22648","last_updated":"2025-08-10T06:05:46Z","snapshot_observed_at":"2026-08-07T13:00:07.473210Z","submitted_at":"2025-05-28T17:57:07Z","title":"WebDancer: Towards Autonomous Information Seeking Agency","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.22648","snapshot_observed_at":"2026-08-05T23:57:35.940452Z","title":"Webdancer: Towards autonomous information seeking agency","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:35.940452Z"},"links":{"cited_paper":"/paper/2505.22648","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:948c997e9e9b3fbb5da25b73f46e7e060307ddeb7d75cf60d47185523a6e3d75","observation_id":"e5141165-c921-4a66-8436-6ff4afbefcc0","resolution":{"observed_at":"2026-08-05T23:57:35.940452Z","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":"2026-08-05T23:57:36.051067Z","title":"Simpletir: End-to-end reinforcement learning for multi-turn tool-integrated reasoning.https://simpletir.notion.site/report, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:36.051067Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:9ac2bfd74c3181b4ba32620b8e97f5b1bd064c61e50572a24e5b01e32cd4ea0c","observation_id":"ad09509b-22a5-476b-9da2-a04b43cfa779","resolution":{"observed_at":"2026-08-05T23:57:36.051067Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1809.09600","last_updated":"2018-09-25T17:28:20Z","snapshot_observed_at":"2026-08-17T01:54:11.006899Z","submitted_at":"2018-09-25T17:28:20Z","title":"HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1809.09600","snapshot_observed_at":"2026-08-05T23:57:36.137902Z","title":"Hotpotqa: A dataset for diverse, explainable multi-hop question answering","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:36.137902Z"},"links":{"cited_paper":"/paper/1809.09600","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:2f44b2da49195dcd42b275de030f58f7137e7633618f512e42a5565b3319f7ec","observation_id":"265c1ce6-2293-4363-80f6-774048b00e63","resolution":{"observed_at":"2026-08-05T23:57:36.137902Z","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":"2026-08-05T23:57:36.262430Z","title":"React: Synergizing reasoning and acting in language models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:36.262430Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:7468cc6b0bd53b380329bc73a0176ba2da23de25c864b80a483ac93166643a22","observation_id":"b4dd1e80-a7f9-410c-8c91-6a50e0b59175","resolution":{"observed_at":"2026-08-05T23:57:36.262430Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.14476","last_updated":"2025-05-20T01:37:34Z","snapshot_observed_at":"2026-08-18T05:01:20.543826Z","submitted_at":"2025-03-18T17:49:06Z","title":"DAPO: An Open-Source LLM Reinforcement Learning System at Scale","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.14476","snapshot_observed_at":"2026-08-05T23:57:36.346541Z","title":"Dapo: An open-source llm reinforcement learning system at scale","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:36.346541Z"},"links":{"cited_paper":"/paper/2503.14476","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:f0b4e00ae9f4405bacfc199aaaaaaf28b6b8c170decf5520aa792f33a658e974","observation_id":"a7267780-6bf5-4d8c-954b-797bca6902e2","resolution":{"observed_at":"2026-08-05T23:57:36.346541Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.19443","last_updated":"2024-11-29T03:01:05Z","snapshot_observed_at":"2026-08-16T20:12:47.860991Z","submitted_at":"2024-11-29T03:01:05Z","title":"Auto-RAG: Autonomous Retrieval-Augmented Generation for Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.19443","snapshot_observed_at":"2026-08-05T23:57:36.480706Z","title":"Auto-rag: Autonomous retrieval-augmented generation for large language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:36.480706Z"},"links":{"cited_paper":"/paper/2411.19443","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:d2dbf76bc36799e975f5dbc4d59d90ca6d996d58a0c32fbebe3ad7522885cae8","observation_id":"55570e4e-d987-4a9d-890f-b5f8c7a3f39c","resolution":{"observed_at":"2026-08-05T23:57:36.480706Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2503.18892","last_updated":"2025-08-06T08:42:32Z","snapshot_observed_at":"2026-07-06T20:57:57.039376Z","submitted_at":"2025-03-24T17:06:10Z","title":"SimpleRL-Zoo: Investigating and Taming Zero Reinforcement Learning for Open Base Models in the Wild","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.18892","snapshot_observed_at":"2026-08-05T23:57:36.557480Z","title":"Simplerl-zoo: Investigating and taming zero reinforcement learning for open base models in the wild","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:36.557480Z"},"links":{"cited_paper":"/paper/2503.18892","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:b8b5608d4ab20bddf771e93072b6ab6ef59f4642617d078309c51132e64ad659","observation_id":"5acf09dd-76c4-4cb9-8a9a-2a43bec7bf88","resolution":{"observed_at":"2026-08-05T23:57:36.557480Z","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":"2026-08-05T23:57:36.657481Z","title":"Flowmind: automatic workflow generation with llms","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:36.657481Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:383db77bff7bc26610fe974dd4c185cb158dc7404aae245e728a81426438d204","observation_id":"976deefd-64fb-4a82-a025-15911957bbc9","resolution":{"observed_at":"2026-08-05T23:57:36.657481Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.22501","last_updated":"2025-05-28T15:50:48Z","snapshot_observed_at":"2026-08-13T12:41:27.197094Z","submitted_at":"2025-05-28T15:50:48Z","title":"EvolveSearch: An Iterative Self-Evolving Search Agent","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.22501","snapshot_observed_at":"2026-08-05T23:57:36.739651Z","title":"Evolvesearch: An iterative self-evolving search agent","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:36.739651Z"},"links":{"cited_paper":"/paper/2505.22501","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:194dd472f1ca5a2461cec2ba239ba198d5747bdf101d045e35d6fd91f4899534","observation_id":"5cecdfa7-9a73-4d04-8df6-fbb414618969","resolution":{"observed_at":"2026-08-05T23:57:36.739651Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.10762","last_updated":"2025-04-15T02:44:55Z","snapshot_observed_at":"2026-07-06T19:33:12.610676Z","submitted_at":"2024-10-14T17:40:40Z","title":"AFlow: Automating Agentic Workflow Generation","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.10762","snapshot_observed_at":"2026-08-05T23:57:36.870589Z","title":"Aflow: Automating agentic workflow generation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:36.870589Z"},"links":{"cited_paper":"/paper/2410.10762","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:39d3b6e7d291e47e5f7a2911538812c2275005f26e1993bcae9b962a98d27c48","observation_id":"2bdecd40-5aca-4a54-9bbc-091346685d9a","resolution":{"observed_at":"2026-08-05T23:57:36.870589Z","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":"2026-08-05T23:57:37.004168Z","title":"Process vs","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:37.004168Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:f113e35740668e9feefdbd6ea8feafb20a47055d12a850d893bb81c2dd40b492","observation_id":"3e140002-0657-44be-bb0f-5d16e054be44","resolution":{"observed_at":"2026-08-05T23:57:37.004168Z","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":"2026-08-05T23:57:37.103918Z","title":"Judging llm-as-a-judge with mt-bench and chatbot arena","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:37.103918Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:7f38c710398090f202a0c3981194180e59ce22d53aaab64ba3b7c0209a04c41d","observation_id":"aab162f6-2a18-4c07-9bd7-a58fc8dc8939","resolution":{"observed_at":"2026-08-05T23:57:37.103918Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2403.13372","last_updated":"2024-06-27T22:44:48Z","snapshot_observed_at":"2026-08-17T02:51:06.474773Z","submitted_at":"2024-03-20T08:08:54Z","title":"LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2403.13372","snapshot_observed_at":"2026-08-05T23:57:37.248925Z","title":"Llamafactory: Unified efficient fine-tuning of 100+ language models","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:37.248925Z"},"links":{"cited_paper":"/paper/2403.13372","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:bb6dd891c5ff217416ed9f793fe8a21d38402c12d5bafdeaaef0566c8d1947e3","observation_id":"594b156b-bb91-45a3-a408-e3da399ca22e","resolution":{"observed_at":"2026-08-05T23:57:37.248925Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.06941","last_updated":"2024-10-28T07:51:29Z","snapshot_observed_at":"2026-08-16T13:26:42.179366Z","submitted_at":"2024-08-13T14:59:44Z","title":"OpenResearcher: Unleashing AI for Accelerated Scientific Research","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2408.06941","snapshot_observed_at":"2026-08-05T23:57:37.318790Z","title":"Openresearcher: Unleashing ai for accelerated scientific research","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:37.318790Z"},"links":{"cited_paper":"/paper/2408.06941","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:b864bf1a9063818942f166e4ec9339861673f6cb611b37a2555d66efaf94af50","observation_id":"f0758f8d-b297-46e9-8cc1-d30b5fcd370d","resolution":{"observed_at":"2026-08-05T23:57:37.318790Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.03160","last_updated":"2025-04-17T04:46:08Z","snapshot_observed_at":"2026-08-16T20:41:16.782304Z","submitted_at":"2025-04-04T04:41:28Z","title":"DeepResearcher: Scaling Deep Research via Reinforcement Learning in Real-world Environments","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.03160","snapshot_observed_at":"2026-08-05T23:57:37.396632Z","title":"Deepresearcher: Scaling deep research via reinforcement learning in real-world environments","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:37.396632Z"},"links":{"cited_paper":"/paper/2504.03160","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:ab809cf319dac3d41900e8479a6d4e125c2f60e78634149e763a9f7bcdfeb9ab","observation_id":"a1e83de9-b3f6-4826-8813-6dc0726be362","resolution":{"observed_at":"2026-08-05T23:57:37.396632Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.07870","last_updated":"2023-12-12T04:47:21Z","snapshot_observed_at":"2026-08-16T15:00:38.675548Z","submitted_at":"2023-09-14T17:18:25Z","title":"Agents: An Open-source Framework for Autonomous Language Agents","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.07870","snapshot_observed_at":"2026-08-05T23:57:37.471097Z","title":"Agents: An open-source framework for autonomous language agents","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:37.471097Z"},"links":{"cited_paper":"/paper/2309.07870","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:e2d8c492b7b1415e06dd6c402508de3f39e893ff8f1a18932e7382bedd4dca13","observation_id":"9e66a4e0-1668-4f1f-8d1c-035c6775baf3","resolution":{"observed_at":"2026-08-05T23:57:37.471097Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.18532","last_updated":"2024-06-26T17:59:18Z","snapshot_observed_at":"2026-08-18T00:31:42.941116Z","submitted_at":"2024-06-26T17:59:18Z","title":"Symbolic Learning Enables Self-Evolving Agents","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.18532","snapshot_observed_at":"2026-08-05T23:57:37.538262Z","title":"Symbolic learning enables self-evolving agents","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:37.538262Z"},"links":{"cited_paper":"/paper/2406.18532","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:aee91532258e8b3c1ea5d7fa2e1bf37bf7e2f214dc84949ac22ab049e34f5a8b","observation_id":"3d81235d-624b-42cd-af34-ed281e30b754","resolution":{"observed_at":"2026-08-05T23:57:37.538262Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.15741","last_updated":"2025-06-23T09:22:39Z","snapshot_observed_at":"2026-08-14T19:04:20.534027Z","submitted_at":"2025-06-17T17:59:02Z","title":"OAgents: An Empirical Study of Building Effective Agents","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.15741","snapshot_observed_at":"2026-08-05T23:57:37.634454Z","title":"Oagents: An empirical study of building effective agents, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:37.634454Z"},"links":{"cited_paper":"/paper/2506.15741","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:f0e5bd2d9f95ef518ff7e0130ac3c90b803542ee445c58ef80520f21b9e36d80","observation_id":"bea47f24-507f-4a7c-aacb-ab5965079b03","resolution":{"observed_at":"2026-08-05T23:57:37.634454Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.12928","last_updated":"2025-06-15T17:59:47Z","snapshot_observed_at":"2026-08-14T20:05:17.894471Z","submitted_at":"2025-06-15T17:59:47Z","title":"Scaling Test-time Compute for LLM Agents","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.12928","snapshot_observed_at":"2026-08-05T23:57:37.738674Z","title":"Scaling test- time compute for llm agents, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:37.738674Z"},"links":{"cited_paper":"/paper/2506.12928","citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:b29ad5799b39e27ee0f1c5faba953e3fa56ece4842ac4cf60ccabd425337c9ff","observation_id":"4bc80da3-53d8-4761-b32d-2fd4cf67c913","resolution":{"observed_at":"2026-08-05T23:57:37.738674Z","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":"2026-08-05T23:57:37.828596Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:37.828596Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:4e5af9f4da342c053aeaec0a2ca473bae505eb8ff6387093a4d945591825cadf","observation_id":"692098ca-827b-401b-8f0c-3d73f7049b76","resolution":{"observed_at":"2026-08-05T23:57:37.828596Z","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":"2026-08-05T23:57:37.840843Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:37.840843Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:bc7fad6a42aa97b12483b80ae93432aa147fc10bda7baa4115db9c19c4726d53","observation_id":"249683ff-7b58-4757-983b-819eff462a8a","resolution":{"observed_at":"2026-08-05T23:57:37.840843Z","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":"2026-08-05T23:57:37.886039Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:37.886039Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:86abc73afcdd01db784e6496b71a20f20a649426db10eac2012702cc35f726b2","observation_id":"1cd27c36-f15d-4f28-8e14-c1db3961abfa","resolution":{"observed_at":"2026-08-05T23:57:37.886039Z","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":"2026-08-05T23:57:37.914911Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:37.914911Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:f57ba2509057aee55c9b02af50e40471bc363888fea300a5bad69146ead1e98c","observation_id":"a60e1ba7-42e8-4d3f-a083-84864fc73b46","resolution":{"observed_at":"2026-08-05T23:57:37.914911Z","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":"2026-08-05T23:57:38.027448Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:38.027448Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:0bc4a10a8f4b6eb1b8ff68bf81b680ff4d309a912fea2add3caa84f30d8a699e","observation_id":"873d6a4c-b7d7-498f-ba4f-5dfc30521fd8","resolution":{"observed_at":"2026-08-05T23:57:38.027448Z","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":"2026-08-05T23:57:38.141052Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:38.141052Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:3fe5ba44d0c0c9f2c87e223f46c7365b2efeb39df0fcdfb5042dda48bbf8f2e8","observation_id":"905295ae-3d75-4b20-9f22-39e4d96e6773","resolution":{"observed_at":"2026-08-05T23:57:38.141052Z","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":"2026-08-05T23:57:38.174023Z","title":"An archive of all existing APOD pages (current date through","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:38.174023Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:fee659a23a88d26d63ffa68d019eb50c9b638e6acd5b68b627d12c8c75d53116","observation_id":"0919bf46-5b23-41f7-90d4-74524349be45","resolution":{"observed_at":"2026-08-05T23:57:38.174023Z","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":"2026-08-05T23:57:38.207382Z","title":null,"venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:38.207382Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:67e453d348dc9d0749b837756587d48ba10320acbc0405d8e4b4e97ccddc71da","observation_id":"7e8e6feb-dc51-4b6c-aead-2bb9fbc372e6","resolution":{"observed_at":"2026-08-05T23:57:38.207382Z","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":"2026-08-05T23:57:38.234371Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:38.234371Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:04920f9c33ae3b36af84e70f9335bbea12bd4744860c53d21d0be60e330d6d3a","observation_id":"06be36e4-0a7b-4720-932d-6f6bc3e8dac4","resolution":{"observed_at":"2026-08-05T23:57:38.234371Z","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":"2026-08-05T23:57:38.271828Z","title":"1, 2015 (Credit: NASA/Bill Ingalls)","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:38.271828Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:ec9e0ca1f51ae5d474e8fe595f0159ba95f911e0bcdad21f77acb0b3139aa04c","observation_id":"2b93abf2-062e-4341-a327-adacede45e6c","resolution":{"observed_at":"2026-08-05T23:57:38.271828Z","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":"2026-08-05T23:57:38.294891Z","title":"</observation> Step 3 <think> Step 1 of the task is to identify the NASA Astronomy Picture of the Day (APOD) from the first week of August 2015 showing city lights on the horizon","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:38.294891Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:aecbb794da261818e5b0df88a78bc2fc55cac8bb7337a25d2bb8d848732c6ad0","observation_id":"e796f871-e906-41de-aff2-01168f04413e","resolution":{"observed_at":"2026-08-05T23:57:38.294891Z","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":"2026-08-05T23:57:38.340917Z","title":"Marquette had a population of 20,629 at the","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:38.340917Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:98ce1fc27f3d9e82b66ac29399abfa27ebe5dd1d0d622b22edd4a4501d756264","observation_id":"36753cd6-d66e-4f7a-bae2-1df7277ade9b","resolution":{"observed_at":"2026-08-05T23:57:38.340917Z","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":"2026-08-05T23:57:38.385756Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:38.385756Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:b54e77dc429f4a68fc1a7cccaf34aaccd050273e67d9eb4ecc78809348bcb30e","observation_id":"43afb520-cf77-4d5f-8a29-315a88ecbeff","resolution":{"observed_at":"2026-08-05T23:57:38.385756Z","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":"2026-08-05T23:57:38.424799Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:38.424799Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:eb9437e4c94d8c738af43f800789276e37eb641f5589a940b67163268875fed6","observation_id":"856fc924-0d4e-4a7a-a21b-72606be3a7a2","resolution":{"observed_at":"2026-08-05T23:57:38.424799Z","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":"2026-08-05T23:57:38.472605Z","title":"“Back in the 1600’s he set up several missions, including","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:38.472605Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:c1c1f2134f25339e5470439f35e5a92082e327f45f5fc310621534f226f342c5","observation_id":"6c50ade7-1dde-42f3-8be2-eaef972b8c75","resolution":{"observed_at":"2026-08-05T23:57:38.472605Z","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":"2026-08-05T23:57:38.516192Z","title":null,"venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:38.516192Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:ab0bfd4b49b054e867e8cc8683b2e5fd172730a6ef6e46c948baf8840cdd876c","observation_id":"51893399-7f40-4f42-8504-10392a842094","resolution":{"observed_at":"2026-08-05T23:57:38.516192Z","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":"2026-08-05T23:57:38.557553Z","title":"Completed in 1894, the Marquette Building brings Chicago’s early history to life in an artistic and elegant setting","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:38.557553Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:fe397120076303133c8a544bfbd1aa92197c3acdf5c4598ed27c684b1a0a88c3","observation_id":"06e14bd8-408b-471c-ba65-a08968c95961","resolution":{"observed_at":"2026-08-05T23:57:38.557553Z","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":"2026-08-05T23:57:38.586633Z","title":null,"venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL","version":1},"reference_index":101,"source":"pdf_text","source_observed_at":"2026-08-05T23:57:38.586633Z"},"links":{"citing_paper":"/paper/2508.13167"},"observation_digest":"sha256:b81b2e570069450dbdc8fe0f8470f523b7121aeb6e2b53ab380ed7e3037606c5","observation_id":"80c25442-ad78-4c32-9ada-b5f4885910b9","resolution":{"observed_at":"2026-08-05T23:57:38.586633Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2508.13167","last_updated":"2025-08-06T17:01:02Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-17T10:57:17.579692Z","submitted_at":"2025-08-06T17:01:02Z","title":"Chain-of-Agents: End-to-End Agent Foundation Models via Multi-Agent Distillation and Agentic RL"},"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":210},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 100 of 210 outbound references and 17 inbound Pith citation observations for arXiv:2508.13167."}