{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:WM3QS3MDGYJ56LBLSFXWRJLNOG","short_pith_number":"pith:WM3QS3MD","schema_version":"1.0","canonical_sha256":"b337096d833613df2c2b916f68a56d71a734d341c54218b1f81786b1653657be","source":{"kind":"arxiv","id":"2605.26302","version":1},"attestation_state":"computed","paper":{"title":"Your Agents Are Aging Too: Agent Lifespan Engineering for Deployed Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.MA"],"primary_cat":"cs.AI","authors_text":"Aditya Akella, Haris Vikalo, Jianing Zhu, John Robertson, Junbo Li, Kevin Wang, Yeonju Ro, Zhangyang Wang","submitted_at":"2026-05-25T19:55:12Z","abstract_excerpt":"Long-lived AI agents are increasingly deployed as persistent operational systems, yet they are still evaluated like freshly initialized models. Day-one benchmarks miss a basic systems question: how long does an agent remain reliable after deployment? Even when model weights are frozen, an agent's effective state keeps changing as it compresses interaction history, retrieves from a growing memory store, revises facts after updates, and undergoes routine maintenance. Reliability therefore becomes a lifespan property of the full agent harness, not only a snapshot property of the base model. We in"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2605.26302","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2026-05-25T19:55:12Z","cross_cats_sorted":["cs.CL","cs.MA"],"title_canon_sha256":"66b2c7806fd434034f802c3f91990b3f4e5c850ead028bdb6b35d3c3d5158d90","abstract_canon_sha256":"22d9629a51e8a7fd0b8ab5b2a50c0d778e6507b7e255f25a1a75caf766642a2a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-27T01:05:10.895956Z","signature_b64":"oAehPy1y6sftSCDe8KYUyLRTi3h4NjRmqPMI3Qxug8Sl/jAFqCEhw3Abs+uxbOwhB7tSSwdGa7RAzxDYwLGaBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b337096d833613df2c2b916f68a56d71a734d341c54218b1f81786b1653657be","last_reissued_at":"2026-05-27T01:05:10.895171Z","signature_status":"signed_v1","first_computed_at":"2026-05-27T01:05:10.895171Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Your Agents Are Aging Too: Agent Lifespan Engineering for Deployed Systems","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.MA"],"primary_cat":"cs.AI","authors_text":"Aditya Akella, Haris Vikalo, Jianing Zhu, John Robertson, Junbo Li, Kevin Wang, Yeonju Ro, Zhangyang Wang","submitted_at":"2026-05-25T19:55:12Z","abstract_excerpt":"Long-lived AI agents are increasingly deployed as persistent operational systems, yet they are still evaluated like freshly initialized models. Day-one benchmarks miss a basic systems question: how long does an agent remain reliable after deployment? Even when model weights are frozen, an agent's effective state keeps changing as it compresses interaction history, retrieves from a growing memory store, revises facts after updates, and undergoes routine maintenance. Reliability therefore becomes a lifespan property of the full agent harness, not only a snapshot property of the base model. We in"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.26302","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.26302/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2605.26302","created_at":"2026-05-27T01:05:10.895327+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.26302v1","created_at":"2026-05-27T01:05:10.895327+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.26302","created_at":"2026-05-27T01:05:10.895327+00:00"},{"alias_kind":"pith_short_12","alias_value":"WM3QS3MDGYJ5","created_at":"2026-05-27T01:05:10.895327+00:00"},{"alias_kind":"pith_short_16","alias_value":"WM3QS3MDGYJ56LBL","created_at":"2026-05-27T01:05:10.895327+00:00"},{"alias_kind":"pith_short_8","alias_value":"WM3QS3MD","created_at":"2026-05-27T01:05:10.895327+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WM3QS3MDGYJ56LBLSFXWRJLNOG","json":"https://pith.science/pith/WM3QS3MDGYJ56LBLSFXWRJLNOG.json","graph_json":"https://pith.science/api/pith-number/WM3QS3MDGYJ56LBLSFXWRJLNOG/graph.json","events_json":"https://pith.science/api/pith-number/WM3QS3MDGYJ56LBLSFXWRJLNOG/events.json","paper":"https://pith.science/paper/WM3QS3MD"},"agent_actions":{"view_html":"https://pith.science/pith/WM3QS3MDGYJ56LBLSFXWRJLNOG","download_json":"https://pith.science/pith/WM3QS3MDGYJ56LBLSFXWRJLNOG.json","view_paper":"https://pith.science/paper/WM3QS3MD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.26302&json=true","fetch_graph":"https://pith.science/api/pith-number/WM3QS3MDGYJ56LBLSFXWRJLNOG/graph.json","fetch_events":"https://pith.science/api/pith-number/WM3QS3MDGYJ56LBLSFXWRJLNOG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WM3QS3MDGYJ56LBLSFXWRJLNOG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WM3QS3MDGYJ56LBLSFXWRJLNOG/action/storage_attestation","attest_author":"https://pith.science/pith/WM3QS3MDGYJ56LBLSFXWRJLNOG/action/author_attestation","sign_citation":"https://pith.science/pith/WM3QS3MDGYJ56LBLSFXWRJLNOG/action/citation_signature","submit_replication":"https://pith.science/pith/WM3QS3MDGYJ56LBLSFXWRJLNOG/action/replication_record"}},"created_at":"2026-05-27T01:05:10.895327+00:00","updated_at":"2026-05-27T01:05:10.895327+00:00"}