{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:OUZDRLN2APIZJFHHCCUUHTSZND","short_pith_number":"pith:OUZDRLN2","schema_version":"1.0","canonical_sha256":"753238adba03d19494e710a943ce5968e76fe1d7cd50891f815b38dea6f859c4","source":{"kind":"arxiv","id":"2606.07603","version":1},"attestation_state":"computed","paper":{"title":"MetaEvo: A Meta-Optimization Framework for Experience-Driven Agent Evolution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Bowen Ren, Heyan Huang, Yang Gao, Yinghao Li","submitted_at":"2026-05-29T09:31:39Z","abstract_excerpt":"Large language models (LLMs) exhibit strong reasoning capabilities, yet most LLM-based agents are statically deployed and unable to improve through task interactions. Existing experience-driven methods often rely on memory or heuristics without enhancing the model's ability to learn, treating it as a passive executor and leading to early performance plateaus and limited long-term improvement. To address this issue, we propose MetaEvo, a two-stage framework for continual agent evolution that focuses on improving how the model learns from tasks experience, rather than solely on what it stores. M"},"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":"2606.07603","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-05-29T09:31:39Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"03922f950ad94b9d60d58493b8cbc13033a79a41c0cdaff95db02888544e126d","abstract_canon_sha256":"316c94c2c45e617a7ee3e3f30967c5933525f35e44c21959458a79834cb62349"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-09T00:04:44.590417Z","signature_b64":"alMtUj5BHciM8JFYs+Y+YzxnZJshl4smR0Sa/A2TbKe4PtG06kb25uzFbvgFv1v2ZS+i7YQ3Ak+D/genpoRgAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"753238adba03d19494e710a943ce5968e76fe1d7cd50891f815b38dea6f859c4","last_reissued_at":"2026-06-09T00:04:44.589751Z","signature_status":"signed_v1","first_computed_at":"2026-06-09T00:04:44.589751Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MetaEvo: A Meta-Optimization Framework for Experience-Driven Agent Evolution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Bowen Ren, Heyan Huang, Yang Gao, Yinghao Li","submitted_at":"2026-05-29T09:31:39Z","abstract_excerpt":"Large language models (LLMs) exhibit strong reasoning capabilities, yet most LLM-based agents are statically deployed and unable to improve through task interactions. Existing experience-driven methods often rely on memory or heuristics without enhancing the model's ability to learn, treating it as a passive executor and leading to early performance plateaus and limited long-term improvement. To address this issue, we propose MetaEvo, a two-stage framework for continual agent evolution that focuses on improving how the model learns from tasks experience, rather than solely on what it stores. M"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.07603","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/2606.07603/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":"2606.07603","created_at":"2026-06-09T00:04:44.589853+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.07603v1","created_at":"2026-06-09T00:04:44.589853+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.07603","created_at":"2026-06-09T00:04:44.589853+00:00"},{"alias_kind":"pith_short_12","alias_value":"OUZDRLN2APIZ","created_at":"2026-06-09T00:04:44.589853+00:00"},{"alias_kind":"pith_short_16","alias_value":"OUZDRLN2APIZJFHH","created_at":"2026-06-09T00:04:44.589853+00:00"},{"alias_kind":"pith_short_8","alias_value":"OUZDRLN2","created_at":"2026-06-09T00:04:44.589853+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/OUZDRLN2APIZJFHHCCUUHTSZND","json":"https://pith.science/pith/OUZDRLN2APIZJFHHCCUUHTSZND.json","graph_json":"https://pith.science/api/pith-number/OUZDRLN2APIZJFHHCCUUHTSZND/graph.json","events_json":"https://pith.science/api/pith-number/OUZDRLN2APIZJFHHCCUUHTSZND/events.json","paper":"https://pith.science/paper/OUZDRLN2"},"agent_actions":{"view_html":"https://pith.science/pith/OUZDRLN2APIZJFHHCCUUHTSZND","download_json":"https://pith.science/pith/OUZDRLN2APIZJFHHCCUUHTSZND.json","view_paper":"https://pith.science/paper/OUZDRLN2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.07603&json=true","fetch_graph":"https://pith.science/api/pith-number/OUZDRLN2APIZJFHHCCUUHTSZND/graph.json","fetch_events":"https://pith.science/api/pith-number/OUZDRLN2APIZJFHHCCUUHTSZND/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OUZDRLN2APIZJFHHCCUUHTSZND/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OUZDRLN2APIZJFHHCCUUHTSZND/action/storage_attestation","attest_author":"https://pith.science/pith/OUZDRLN2APIZJFHHCCUUHTSZND/action/author_attestation","sign_citation":"https://pith.science/pith/OUZDRLN2APIZJFHHCCUUHTSZND/action/citation_signature","submit_replication":"https://pith.science/pith/OUZDRLN2APIZJFHHCCUUHTSZND/action/replication_record"}},"created_at":"2026-06-09T00:04:44.589853+00:00","updated_at":"2026-06-09T00:04:44.589853+00:00"}