{"as_of":"2026-08-08T08:47:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9db9749923844a2d6a6e5d8f0778a19aedd1a077313e037253d2d05b7c5957ec","coverage":[{"denominator":30,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":30,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-06T18:29:36.837135Z","state":"measured"},{"denominator":34,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":34,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-08T06:32:00.761636+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T00:14:57.067936Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-07-02T02:16:26.560954Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2507.08270","last_updated":"2025-07-11T02:34:16Z","snapshot_observed_at":"2026-08-07T01:31:51.955348Z","submitted_at":"2025-07-11T02:34:16Z","title":"Agent Safety Alignment via Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.08270","snapshot_observed_at":"2026-08-05T18:12:35.170680Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.15068","last_updated":"2025-08-20T21:08:29Z","snapshot_observed_at":"2026-08-07T05:02:51.529077Z","submitted_at":"2025-08-20T21:08:29Z","title":"S3LoRA: Safe Spectral Sharpness-Guided Pruning in Adaptation of Agent Planner","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-05T18:12:35.170680Z"},"links":{"cited_paper":"/paper/2507.08270","citing_paper":"/paper/2508.15068"},"observation_digest":"sha256:99d4ff667d5401eb734c3b4b3a31742a3f759787f370f2b623e6c343767b41bc","observation_id":"4e0b924d-c7a3-4880-af77-73a752de6b5f","resolution":{"observed_at":"2026-08-05T18:12:35.170680Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.08270","last_updated":"2025-07-11T02:34:16Z","snapshot_observed_at":"2026-08-07T01:31:51.955348Z","submitted_at":"2025-07-11T02:34:16Z","title":"Agent Safety Alignment via Reinforcement Learning","version":1},"cited_work":{"arxiv_id":"2507.08270","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2507.08270","snapshot_observed_at":"2026-07-02T02:16:26.560954Z","title":"Yangjun Ruan, Honghua Dong, Andrew Wang, Sil- viu Pitis, Yongchao Zhou, Jimmy Ba, Yann Dubois, Chris J","venue":null,"work_id":"489220e3-35d2-4f08-927c-d4c9b902a63a","year":2024},"citing_paper":{"arxiv_id":"2606.04051","last_updated":"2026-06-02T09:02:14Z","snapshot_observed_at":"2026-07-06T23:44:14.261541Z","submitted_at":"2026-06-02T09:02:14Z","title":"RUBAS: Rubric-Based Reinforcement Learning for Agent Safety","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-06-28T11:07:47.814115Z"},"links":{"cited_paper":"/paper/2507.08270","citing_paper":"/paper/2606.04051"},"observation_digest":"sha256:d61d0284cf16c603d1933ef315fa727bb0bcf0ec29a93fa24d71001f4e9ebf1e","observation_id":"df93920d-a25d-43be-90bb-e4a56a80e3e5","resolution":{"observed_at":"2026-07-02T02:16:26.562656Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2507.08270","last_updated":"2025-07-11T02:34:16Z","snapshot_observed_at":"2026-08-07T01:31:51.955348Z","submitted_at":"2025-07-11T02:34:16Z","title":"Agent Safety Alignment via Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.08270","snapshot_observed_at":"2026-08-02T04:49:08.700306Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2607.13594","last_updated":"2026-07-15T08:38:16Z","snapshot_observed_at":"2026-08-03T04:36:41.120672Z","submitted_at":"2026-07-15T08:38:16Z","title":"SAFETY SENTRY: Context-Aware Human Intervention via EXECUTE-ASK-REFUSE Routing","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-02T04:49:08.700306Z"},"links":{"cited_paper":"/paper/2507.08270","citing_paper":"/paper/2607.13594"},"observation_digest":"sha256:2d21dacc7de8b37fa4377b1310335fab45f091f312e729dc4b50a55cdff7c489","observation_id":"302533e2-3be7-4e66-9313-72f4e359b5d8","resolution":{"observed_at":"2026-08-02T04:49:08.700306Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.08270","last_updated":"2025-07-11T02:34:16Z","snapshot_observed_at":"2026-08-07T01:31:51.955348Z","submitted_at":"2025-07-11T02:34:16Z","title":"Agent Safety Alignment via Reinforcement Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.08270","snapshot_observed_at":"2026-08-07T00:14:57.067936Z","title":"arXiv preprint arXiv:2507.08270 , year=","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2608.02683","last_updated":"2026-08-03T02:06:06Z","snapshot_observed_at":"2026-08-07T23:09:53.160833Z","submitted_at":"2026-08-03T02:06:06Z","title":"$S^3$: Improving Agent Safety through Multi-Stage Defense","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-07T00:14:57.067936Z"},"links":{"cited_paper":"/paper/2507.08270","citing_paper":"/paper/2608.02683"},"observation_digest":"sha256:46632c2b7d52b27e42d9dc19112271b9d9440621a0cc1e1a5f0edad61bd772fa","observation_id":"1de972a0-65c9-483d-bc7a-6cef8dca0b61","resolution":{"observed_at":"2026-08-07T00:14:57.067936Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2507.08270/citation-record","integrity":"/paper/2507.08270/integrity","json":"/paper/2507.08270/citation-record.json","paper":"/paper/2507.08270"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:29:39.170422Z","title":"Narasimhan, and Yuan Cao","venue":null,"work_id":"51407906-f699-4eab-a0a1-8227781f6a2a","year":2023},"citing_paper":{"arxiv_id":"2507.08270","last_updated":"2025-07-11T02:34:16Z","snapshot_observed_at":"2026-08-07T01:31:51.955348Z","submitted_at":"2025-07-11T02:34:16Z","title":"Agent Safety Alignment via Reinforcement Learning","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-06T18:29:35.375189Z"},"links":{"citing_paper":"/paper/2507.08270"},"observation_digest":"sha256:8cabc597b06863d2d8ac7951f5f18d83fe7571a1f79ef57aaea1424f4887cf9c","observation_id":"180d6887-487a-4761-b359-a24be9ab44c2","resolution":{"observed_at":"2026-08-06T18:29:39.278568Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:29:38.982349Z","title":"AutoGPT: An open-source autonomous agent framework","venue":null,"work_id":"7dbed1ed-888f-48b0-aae1-73cc2e0d059f","year":2023},"citing_paper":{"arxiv_id":"2507.08270","last_updated":"2025-07-11T02:34:16Z","snapshot_observed_at":"2026-08-07T01:31:51.955348Z","submitted_at":"2025-07-11T02:34:16Z","title":"Agent Safety Alignment via Reinforcement Learning","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-06T18:29:35.487365Z"},"links":{"citing_paper":"/paper/2507.08270"},"observation_digest":"sha256:cb29be5b0808057d9ef1c4872ad676075e2f19fbdcfe8e0f5ccdaeb06516866b","observation_id":"9b8cfb93-2cd6-414b-bca4-c1d04723b526","resolution":{"observed_at":"2026-08-06T18:29:39.040807Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:29:38.791823Z","title":"BabyAGI: Experimental self-building autonomous agent","venue":null,"work_id":"519a9a66-c48d-4225-8cf4-9cac90de0e35","year":2023},"citing_paper":{"arxiv_id":"2507.08270","last_updated":"2025-07-11T02:34:16Z","snapshot_observed_at":"2026-08-07T01:31:51.955348Z","submitted_at":"2025-07-11T02:34:16Z","title":"Agent Safety Alignment via Reinforcement Learning","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-06T18:29:35.542656Z"},"links":{"citing_paper":"/paper/2507.08270"},"observation_digest":"sha256:2ac592ffa171f73f4dde89d15d84d5b9975a9f463a6fbd015bbb4d110e7539d7","observation_id":"8c747e8a-2736-4a40-863c-48cef5300cb1","resolution":{"observed_at":"2026-08-06T18:29:38.892749Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:29:38.578927Z","title":"AgentGPT: Configure and deploy autonomous ai agents","venue":null,"work_id":"0cb3c54f-fc30-4e2e-84b3-b700c65ae9da","year":2024},"citing_paper":{"arxiv_id":"2507.08270","last_updated":"2025-07-11T02:34:16Z","snapshot_observed_at":"2026-08-07T01:31:51.955348Z","submitted_at":"2025-07-11T02:34:16Z","title":"Agent Safety Alignment via Reinforcement Learning","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-06T18:29:35.591544Z"},"links":{"citing_paper":"/paper/2507.08270"},"observation_digest":"sha256:2a6b12822542f724090e75780487597bb87e4c6127080c27605c2c101ad89d45","observation_id":"30daa421-c6f6-4f62-871b-9fbce1301e31","resolution":{"observed_at":"2026-08-06T18:29:38.670365Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:29:38.349715Z","title":"AI agents under threat: A survey of key security challenges and future pathways","venue":null,"work_id":"a6593c66-518a-450d-a2e8-697639bdbbde","year":2025},"citing_paper":{"arxiv_id":"2507.08270","last_updated":"2025-07-11T02:34:16Z","snapshot_observed_at":"2026-08-07T01:31:51.955348Z","submitted_at":"2025-07-11T02:34:16Z","title":"Agent Safety Alignment via Reinforcement Learning","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-06T18:29:35.624350Z"},"links":{"citing_paper":"/paper/2507.08270"},"observation_digest":"sha256:95c6df2d04948eacebf8041828d1ad76abe1fb17b96623a874cfebbabd9aaeef","observation_id":"6d323b6e-662d-4c35-8a7b-b8dd7e2d3ac3","resolution":{"observed_at":"2026-08-06T18:29:38.493698Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2411.09523","last_updated":"2024-11-14T15:40:04Z","snapshot_observed_at":"2026-08-05T11:33:06.319013Z","submitted_at":"2024-11-14T15:40:04Z","title":"Navigating the Risks: A Survey of Security, Privacy, and Ethics Threats in LLM-Based Agents","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.09523","snapshot_observed_at":"2026-08-06T18:29:35.685754Z","title":"Navigating the risks: A survey of security, privacy, and ethics threats in llm-based agents","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.08270","last_updated":"2025-07-11T02:34:16Z","snapshot_observed_at":"2026-08-07T01:31:51.955348Z","submitted_at":"2025-07-11T02:34:16Z","title":"Agent Safety Alignment via Reinforcement Learning","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-06T18:29:35.685754Z"},"links":{"cited_paper":"/paper/2411.09523","citing_paper":"/paper/2507.08270"},"observation_digest":"sha256:f4a9aad26323893f0f9a405a7872bf2999a79f9614d713bc8f121d19a51d3ac4","observation_id":"e903261e-c05d-428b-a610-f02e4534c4f9","resolution":{"observed_at":"2026-08-06T18:29:35.685754Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:29:38.266286Z","title":"Retool: Reinforcement learning for strategic tool use in llms, 2025","venue":null,"work_id":"a722197b-311d-463c-9f80-b5fe2218d422","year":2025},"citing_paper":{"arxiv_id":"2507.08270","last_updated":"2025-07-11T02:34:16Z","snapshot_observed_at":"2026-08-07T01:31:51.955348Z","submitted_at":"2025-07-11T02:34:16Z","title":"Agent Safety Alignment via Reinforcement Learning","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T18:29:35.730506Z"},"links":{"citing_paper":"/paper/2507.08270"},"observation_digest":"sha256:04b3d352cbcaf9b4d518e6d0b89b722d1f9ab3a4e51087cc0d087e38153d4806","observation_id":"277557af-1ef4-4596-8267-f1f6d14cfb36","resolution":{"observed_at":"2026-08-06T18:29:38.295437Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2505.07903","last_updated":"2025-05-12T09:45:40Z","snapshot_observed_at":"2026-08-07T15:47:55.834808Z","submitted_at":"2025-05-12T09:45:40Z","title":"SEM: Reinforcement Learning for Search-Efficient Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.07903","snapshot_observed_at":"2026-08-06T18:29:35.794563Z","title":"SEM: reinforcement learning for search-efficient large language models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.08270","last_updated":"2025-07-11T02:34:16Z","snapshot_observed_at":"2026-08-07T01:31:51.955348Z","submitted_at":"2025-07-11T02:34:16Z","title":"Agent Safety Alignment via Reinforcement Learning","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-06T18:29:35.794563Z"},"links":{"cited_paper":"/paper/2505.07903","citing_paper":"/paper/2507.08270"},"observation_digest":"sha256:e8326edaf29ca73a862ec3b621714f81826e10a240319ffaad78955f0f39223e","observation_id":"9064441a-04bc-4858-bf9f-bd04b31bf99b","resolution":{"observed_at":"2026-08-06T18:29:35.794563Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2501.11425","last_updated":"2025-03-24T10:18:56Z","snapshot_observed_at":"2026-07-06T20:23:22.709846Z","submitted_at":"2025-01-20T11:46:04Z","title":"Agent-R: Training Language Model Agents to Reflect via Iterative Self-Training","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2501.11425","snapshot_observed_at":"2026-08-06T18:29:35.842058Z","title":"Agent-r: Training language model agents to reflect via iterative self-training","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.08270","last_updated":"2025-07-11T02:34:16Z","snapshot_observed_at":"2026-08-07T01:31:51.955348Z","submitted_at":"2025-07-11T02:34:16Z","title":"Agent Safety Alignment via Reinforcement Learning","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-06T18:29:35.842058Z"},"links":{"cited_paper":"/paper/2501.11425","citing_paper":"/paper/2507.08270"},"observation_digest":"sha256:2c108eceb0dc2d841da2e3165148426ac7c0045d8fa45a530f4c7b7e5c697590","observation_id":"11389ede-4427-4e8b-9513-85c8adb70359","resolution":{"observed_at":"2026-08-06T18:29:35.842058Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:29:38.121502Z","title":"Pan, Wen Zhang, Huajun Chen, Fan Yang, Zenan Zhou, and Weipeng Chen","venue":null,"work_id":"847ae331-219d-47ac-82bb-f2ffc44595ed","year":2025},"citing_paper":{"arxiv_id":"2507.08270","last_updated":"2025-07-11T02:34:16Z","snapshot_observed_at":"2026-08-07T01:31:51.955348Z","submitted_at":"2025-07-11T02:34:16Z","title":"Agent Safety Alignment via Reinforcement Learning","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-06T18:29:35.896986Z"},"links":{"citing_paper":"/paper/2507.08270"},"observation_digest":"sha256:0bdc66c9afd9d866ee26b35663cf8be6a392f61231a03f23a46dd7542c9cbd10","observation_id":"9db288de-ae8a-40ce-922e-1f3dbc026ce4","resolution":{"observed_at":"2026-08-06T18:29:38.183883Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:29:38.004576Z","title":"Agent security bench (ASB): formalizing and benchmarking attacks and defenses in llm-based agents","venue":null,"work_id":"2c556f4c-09c5-4eef-9bb5-e224074dd844","year":2025},"citing_paper":{"arxiv_id":"2507.08270","last_updated":"2025-07-11T02:34:16Z","snapshot_observed_at":"2026-08-07T01:31:51.955348Z","submitted_at":"2025-07-11T02:34:16Z","title":"Agent Safety Alignment via Reinforcement Learning","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-06T18:29:35.957481Z"},"links":{"citing_paper":"/paper/2507.08270"},"observation_digest":"sha256:9c45aae368fafd5fba09bbd06d2a95b9b6c15c18cf3269d59c49dfc9339158f7","observation_id":"81cbd57d-026c-4c08-b680-a2d98e1d6acf","resolution":{"observed_at":"2026-08-06T18:29:38.041081Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2412.14470","last_updated":"2025-05-20T05:58:23Z","snapshot_observed_at":"2026-08-06T12:35:19.109481Z","submitted_at":"2024-12-19T02:35:15Z","title":"Agent-SafetyBench: Evaluating the Safety of LLM Agents","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2412.14470","snapshot_observed_at":"2026-08-06T18:29:36.015210Z","title":"Agent-safetybench: Evaluating the safety of LLM agents","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.08270","last_updated":"2025-07-11T02:34:16Z","snapshot_observed_at":"2026-08-07T01:31:51.955348Z","submitted_at":"2025-07-11T02:34:16Z","title":"Agent Safety Alignment via Reinforcement Learning","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-06T18:29:36.015210Z"},"links":{"cited_paper":"/paper/2412.14470","citing_paper":"/paper/2507.08270"},"observation_digest":"sha256:aff77c46b1bf768f2c4c127491a539795cd0e1f7f1d16d206c8f1188bfe6e450","observation_id":"29aeb465-7be8-4e24-a417-a491faef2aff","resolution":{"observed_at":"2026-08-06T18:29:36.015210Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.11063","last_updated":"2026-05-26T13:29:10Z","snapshot_observed_at":"2026-08-07T15:44:46.595008Z","submitted_at":"2025-05-16T10:00:15Z","title":"Think Twice Before You Act: Enhancing Agent Behavioral Safety with Thought Correction","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.11063","snapshot_observed_at":"2026-08-06T18:29:36.076016Z","title":"Think twice before you act: Enhancing agent behavioral safety with thought correction","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.08270","last_updated":"2025-07-11T02:34:16Z","snapshot_observed_at":"2026-08-07T01:31:51.955348Z","submitted_at":"2025-07-11T02:34:16Z","title":"Agent Safety Alignment via Reinforcement Learning","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-06T18:29:36.076016Z"},"links":{"cited_paper":"/paper/2505.11063","citing_paper":"/paper/2507.08270"},"observation_digest":"sha256:d89c6aa7b0f3ffb7f5809675c73b32e94c000de817780da88a62e4a9c5b59528","observation_id":"f0ee323a-2ed7-4e0d-bd10-97e50f9bb5ae","resolution":{"observed_at":"2026-08-06T18:29:36.076016Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.23020","last_updated":"2025-05-29T03:02:18Z","snapshot_observed_at":"2026-08-08T01:17:46.684635Z","submitted_at":"2025-05-29T03:02:18Z","title":"AgentAlign: Navigating Safety Alignment in the Shift from Informative to Agentic Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.23020","snapshot_observed_at":"2026-08-06T18:29:36.127202Z","title":"Agentalign: Navigating safety alignment in the shift from informative to agentic large language models","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.08270","last_updated":"2025-07-11T02:34:16Z","snapshot_observed_at":"2026-08-07T01:31:51.955348Z","submitted_at":"2025-07-11T02:34:16Z","title":"Agent Safety Alignment via Reinforcement Learning","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-06T18:29:36.127202Z"},"links":{"cited_paper":"/paper/2505.23020","citing_paper":"/paper/2507.08270"},"observation_digest":"sha256:771e111f61d5b016da07f52e412d7803b3ee0047f4127f168b4ca005f00a06b2","observation_id":"810eec96-44f3-4628-8fde-c766f6a55667","resolution":{"observed_at":"2026-08-06T18:29:36.127202Z","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":"2026-08-06T18:29:36.164795Z","title":"Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.08270","last_updated":"2025-07-11T02:34:16Z","snapshot_observed_at":"2026-08-07T01:31:51.955348Z","submitted_at":"2025-07-11T02:34:16Z","title":"Agent Safety Alignment via Reinforcement Learning","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-06T18:29:36.164795Z"},"links":{"citing_paper":"/paper/2507.08270"},"observation_digest":"sha256:2026412200d0a9c8fa3da9cde633a6f0d6ef7cdb30d881ded3d8b086b96d8f25","observation_id":"80905211-b7a7-441a-a286-19e9f3ddb51a","resolution":{"observed_at":"2026-08-06T18:29:36.164795Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:29:37.837034Z","title":"Injecagent: Benchmarking indirect prompt injections in tool-integrated large language model agents","venue":null,"work_id":"a336e99b-3459-44c2-8d8d-4bea3fcc3ab1","year":2024},"citing_paper":{"arxiv_id":"2507.08270","last_updated":"2025-07-11T02:34:16Z","snapshot_observed_at":"2026-08-07T01:31:51.955348Z","submitted_at":"2025-07-11T02:34:16Z","title":"Agent Safety Alignment via Reinforcement Learning","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-06T18:29:36.202956Z"},"links":{"citing_paper":"/paper/2507.08270"},"observation_digest":"sha256:9fad742fc8ab0d5033a61b66bd72afc8dfe19bdf9fd92817f4bcb1facf7c2703","observation_id":"17a08604-0c36-4cad-8775-c863bc923e5b","resolution":{"observed_at":"2026-08-06T18:29:37.906726Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:29:37.681594Z","title":"Patil, Huanzhi Mao, Charlie Cheng-Jie Ji, Fanjia Yan, Vishnu Suresh, Ion Stoica, and Joseph E","venue":null,"work_id":"636eff7c-6e26-47cb-92da-91676791db25","year":2025},"citing_paper":{"arxiv_id":"2507.08270","last_updated":"2025-07-11T02:34:16Z","snapshot_observed_at":"2026-08-07T01:31:51.955348Z","submitted_at":"2025-07-11T02:34:16Z","title":"Agent Safety Alignment via Reinforcement Learning","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-06T18:29:36.248215Z"},"links":{"citing_paper":"/paper/2507.08270"},"observation_digest":"sha256:8eb19eb8f580cdbdbb9ae42942e97a6d26e341f277033c80d37fe1d918c639a1","observation_id":"e0617b99-43ab-43ee-ad94-10026b7b850b","resolution":{"observed_at":"2026-08-06T18:29:37.752821Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.21460","last_updated":"2025-03-27T12:50:17Z","snapshot_observed_at":"2026-07-06T20:59:35.694800Z","submitted_at":"2025-03-27T12:50:17Z","title":"Large Language Model Agent: A Survey on Methodology, Applications and Challenges","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2503.21460","snapshot_observed_at":"2026-08-06T18:29:36.302719Z","title":"Yu, and Ming Zhang","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.08270","last_updated":"2025-07-11T02:34:16Z","snapshot_observed_at":"2026-08-07T01:31:51.955348Z","submitted_at":"2025-07-11T02:34:16Z","title":"Agent Safety Alignment via Reinforcement Learning","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-06T18:29:36.302719Z"},"links":{"cited_paper":"/paper/2503.21460","citing_paper":"/paper/2507.08270"},"observation_digest":"sha256:5eb70a567fc93a30c368d54442cfe08463eafa193dcdfa59770aa3f8f8172c26","observation_id":"b2d897fa-1c92-4adf-9b0a-edc24e9241d6","resolution":{"observed_at":"2026-08-06T18:29:36.302719Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2309.14365","last_updated":"2023-09-23T11:25:45Z","snapshot_observed_at":"2026-07-06T16:23:26.584450Z","submitted_at":"2023-09-23T11:25:45Z","title":"An In-depth Survey of Large Language Model-based Artificial Intelligence Agents","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2309.14365","snapshot_observed_at":"2026-08-06T18:29:36.361217Z","title":"An in-depth survey of large language model-based artificial intelligence agents","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.08270","last_updated":"2025-07-11T02:34:16Z","snapshot_observed_at":"2026-08-07T01:31:51.955348Z","submitted_at":"2025-07-11T02:34:16Z","title":"Agent Safety Alignment via Reinforcement Learning","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-06T18:29:36.361217Z"},"links":{"cited_paper":"/paper/2309.14365","citing_paper":"/paper/2507.08270"},"observation_digest":"sha256:b383d4ff51d60c895c2b81217ff948d0c82df162fe57d316925e0d4973753fea","observation_id":"6e81ee76-f028-4cc3-bc71-108bc49d7958","resolution":{"observed_at":"2026-08-06T18:29:36.361217Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:29:37.532964Z","title":"Sumers, Shunyu Yao, Karthik Narasimhan, and Thomas L","venue":null,"work_id":"a4a36f44-4829-4e4b-a308-feed5459c48d","year":2024},"citing_paper":{"arxiv_id":"2507.08270","last_updated":"2025-07-11T02:34:16Z","snapshot_observed_at":"2026-08-07T01:31:51.955348Z","submitted_at":"2025-07-11T02:34:16Z","title":"Agent Safety Alignment via Reinforcement Learning","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-06T18:29:36.410757Z"},"links":{"citing_paper":"/paper/2507.08270"},"observation_digest":"sha256:9506c032a7c85f73efdc9b96f18a4861327ce48fa0b41d06774af444a1d25cde","observation_id":"f10aa2d4-cfbb-496c-82c6-09d82e7f1ce8","resolution":{"observed_at":"2026-08-06T18:29:37.597508Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:29:37.442520Z","title":"AutoAgent: A Fully-Automated and Zero-Code Frame- work for LLM Agents, 2025","venue":null,"work_id":"ce106a32-1ac8-4f52-9276-20a61bd17595","year":2025},"citing_paper":{"arxiv_id":"2507.08270","last_updated":"2025-07-11T02:34:16Z","snapshot_observed_at":"2026-08-07T01:31:51.955348Z","submitted_at":"2025-07-11T02:34:16Z","title":"Agent Safety Alignment via Reinforcement Learning","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-06T18:29:36.458385Z"},"links":{"citing_paper":"/paper/2507.08270"},"observation_digest":"sha256:b3d1e83a24bd32b4d4c392f131b5aa542b75d2c50121b2de6f4703db0f50caeb","observation_id":"a273817c-b321-431e-bd36-6a183d9853e9","resolution":{"observed_at":"2026-08-06T18:29:37.477865Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2503.19470","last_updated":"2025-09-23T03:45:42Z","snapshot_observed_at":"2026-07-06T20:58:19.305457Z","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":"2026-08-06T18:29:36.509257Z","title":"Pan, Wen Zhang, Huajun Chen, Fan Yang, Zenan Zhou, and Weipeng Chen","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.08270","last_updated":"2025-07-11T02:34:16Z","snapshot_observed_at":"2026-08-07T01:31:51.955348Z","submitted_at":"2025-07-11T02:34:16Z","title":"Agent Safety Alignment via Reinforcement Learning","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-06T18:29:36.509257Z"},"links":{"cited_paper":"/paper/2503.19470","citing_paper":"/paper/2507.08270"},"observation_digest":"sha256:f6f197a7c3428d8fef4250ae5322e5c43b154088377e64cfc2fb68546d70648d","observation_id":"dad2e0b3-a7d9-4aea-8958-819993d89801","resolution":{"observed_at":"2026-08-06T18:29:36.509257Z","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":"2026-08-06T18:29:36.552229Z","title":"Deepresearcher: Scaling deep research via reinforcement learning in real-world environments, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.08270","last_updated":"2025-07-11T02:34:16Z","snapshot_observed_at":"2026-08-07T01:31:51.955348Z","submitted_at":"2025-07-11T02:34:16Z","title":"Agent Safety Alignment via Reinforcement Learning","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T18:29:36.552229Z"},"links":{"citing_paper":"/paper/2507.08270"},"observation_digest":"sha256:15f99ffb0416ee2b2a23cfa2bd0e1a6b7b04c1e029f557526273e84255488ca9","observation_id":"17698f00-7f3f-49a3-a4d3-fc47cef63484","resolution":{"observed_at":"2026-08-06T18:29:36.552229Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2506.02040","last_updated":"2025-09-14T07:48:51Z","snapshot_observed_at":"2026-08-07T13:11:17.932068Z","submitted_at":"2025-05-31T08:01:11Z","title":"Beyond the Protocol: Unveiling Attack Vectors in the Model Context Protocol (MCP) Ecosystem","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2506.02040","snapshot_observed_at":"2026-08-06T18:29:36.599007Z","title":"Beyond the protocol: Unveiling attack vectors in the model context protocol ecosystem","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.08270","last_updated":"2025-07-11T02:34:16Z","snapshot_observed_at":"2026-08-07T01:31:51.955348Z","submitted_at":"2025-07-11T02:34:16Z","title":"Agent Safety Alignment via Reinforcement Learning","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-06T18:29:36.599007Z"},"links":{"cited_paper":"/paper/2506.02040","citing_paper":"/paper/2507.08270"},"observation_digest":"sha256:c937765351714d3398295379aa82e86485a7dcad10408e1de68309662eb7cfc2","observation_id":"c292e407-2abc-4880-9c67-6926d025440e","resolution":{"observed_at":"2026-08-06T18:29:36.599007Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2504.11703","last_updated":"2026-05-14T02:36:38Z","snapshot_observed_at":"2026-07-06T21:10:09.129714Z","submitted_at":"2025-04-16T01:58:40Z","title":"Progent: Securing AI Agents with Privilege Control","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2504.11703","snapshot_observed_at":"2026-08-06T18:29:36.649473Z","title":"Progent: Programmable privilege control for LLM agents","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.08270","last_updated":"2025-07-11T02:34:16Z","snapshot_observed_at":"2026-08-07T01:31:51.955348Z","submitted_at":"2025-07-11T02:34:16Z","title":"Agent Safety Alignment via Reinforcement Learning","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-06T18:29:36.649473Z"},"links":{"cited_paper":"/paper/2504.11703","citing_paper":"/paper/2507.08270"},"observation_digest":"sha256:0bf6899bbf647bf86d8bf4fd37f3e54f4379ce892e0ff6858801885648e8d5da","observation_id":"af481968-d1c7-4ba9-aca5-a42cdc8392d5","resolution":{"observed_at":"2026-08-06T18:29:36.649473Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.19532","last_updated":"2026-06-24T05:39:26Z","snapshot_observed_at":"2026-08-07T14:09:43.286267Z","submitted_at":"2025-05-26T05:39:35Z","title":"Fox in the Henhouse: Supply-Chain Backdoor Attacks Against Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.19532","snapshot_observed_at":"2026-08-06T18:29:36.704014Z","title":"Cullen, Paul Montague, Sarah M","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.08270","last_updated":"2025-07-11T02:34:16Z","snapshot_observed_at":"2026-08-07T01:31:51.955348Z","submitted_at":"2025-07-11T02:34:16Z","title":"Agent Safety Alignment via Reinforcement Learning","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-06T18:29:36.704014Z"},"links":{"cited_paper":"/paper/2505.19532","citing_paper":"/paper/2507.08270"},"observation_digest":"sha256:fd44add5143d929f00dae7b898b3606a63e138cede2dc1c2be6a70ed2e4a6096","observation_id":"3d8ff5a5-ea96-4e0b-91cb-22d95099f4d2","resolution":{"observed_at":"2026-08-06T18:29:36.704014Z","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":"2026-08-06T18:29:36.746281Z","title":"A practical memory injection attack against LLM agents","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2507.08270","last_updated":"2025-07-11T02:34:16Z","snapshot_observed_at":"2026-08-07T01:31:51.955348Z","submitted_at":"2025-07-11T02:34:16Z","title":"Agent Safety Alignment via Reinforcement Learning","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-06T18:29:36.746281Z"},"links":{"citing_paper":"/paper/2507.08270"},"observation_digest":"sha256:e03d267ce9bfcc90689c5cacc4aa0a4ab34be5e581752a5a6e5570e1648eeefa","observation_id":"f0adbf1b-2904-4aa7-954c-6cf7b23e3bc7","resolution":{"observed_at":"2026-08-06T18:29:36.746281Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:29:37.350012Z","title":"Agentpoison: Red- teaming LLM agents via poisoning memory or knowledge bases","venue":null,"work_id":"65f5fa8a-f324-466d-8b65-a0c4b2604678","year":2024},"citing_paper":{"arxiv_id":"2507.08270","last_updated":"2025-07-11T02:34:16Z","snapshot_observed_at":"2026-08-07T01:31:51.955348Z","submitted_at":"2025-07-11T02:34:16Z","title":"Agent Safety Alignment via Reinforcement Learning","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-06T18:29:36.793821Z"},"links":{"citing_paper":"/paper/2507.08270"},"observation_digest":"sha256:da4a93f820720dc4ffaf9b447c737a88e1f27426fd74bf7a18a0a32f3a007886","observation_id":"17663713-ff13-49cf-816d-39dcd70da839","resolution":{"observed_at":"2026-08-06T18:29:37.380765Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-06T18:29:36.837135Z","title":"Safeagentbench: A benchmark for safe task planning of embodied LLM agents","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2507.08270","last_updated":"2025-07-11T02:34:16Z","snapshot_observed_at":"2026-08-07T01:31:51.955348Z","submitted_at":"2025-07-11T02:34:16Z","title":"Agent Safety Alignment via Reinforcement Learning","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-06T18:29:36.837135Z"},"links":{"citing_paper":"/paper/2507.08270"},"observation_digest":"sha256:66b41302c22f476ba530624b365b7eb429117e07103b3cd5c7467ffcb2188eec","observation_id":"474d8d38-3066-4322-b55d-e140835d4b2f","resolution":{"observed_at":"2026-08-06T18:29:36.837135Z","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":"2026-08-06T18:29:35.441056Z","title":null,"venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2507.08270","last_updated":"2025-07-11T02:34:16Z","snapshot_observed_at":"2026-08-07T01:31:51.955348Z","submitted_at":"2025-07-11T02:34:16Z","title":"Agent Safety Alignment via Reinforcement Learning","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-06T18:29:35.441056Z"},"links":{"citing_paper":"/paper/2507.08270"},"observation_digest":"sha256:950bca40b51c63c5b72eadb951ccef29f04b84632f7118ec412af1c172673799","observation_id":"86d6065a-488b-4180-8dbc-b3fb19586d46","resolution":{"observed_at":"2026-08-06T18:29:35.441056Z","resolver_source":null,"status":"parse_uncertain"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2507.08270","last_updated":"2025-07-11T02:34:16Z","latest_version":1,"primary_category":"cs.AI","snapshot_observed_at":"2026-08-07T01:31:51.955348Z","submitted_at":"2025-07-11T02:34:16Z","title":"Agent Safety Alignment via Reinforcement Learning"},"reference_resolution":{"displayed":30,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":1,"unresolved":16,"verified_exact":0,"verified_fuzzy":13},"total_outbound_references":30},"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":"2026-08-08T06:32:00.761636+00:00","source":"crossref"},{"observed_at":"2026-08-08T06:31:55.24221+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 4 inbound Pith citation observations for arXiv:2507.08270."}