{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:OQ64JU6F5TDZ6XRD5FHHR4LIHX","short_pith_number":"pith:OQ64JU6F","canonical_record":{"source":{"id":"2510.16079","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-10-17T12:03:16Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"d4825c1c87b5ebd256232f1f3a1cedfe6979962acbe37e9f9157ec0667035bda","abstract_canon_sha256":"3083a336ee43f983d3fe891a0a6538b55e9271247773dc43de7c0eefe69b35ac"},"schema_version":"1.0"},"canonical_sha256":"743dc4d3c5ecc79f5e23e94e78f1683dd13e96e1506f0d0918cb3c5972a866e6","source":{"kind":"arxiv","id":"2510.16079","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2510.16079","created_at":"2026-05-20T00:02:57Z"},{"alias_kind":"arxiv_version","alias_value":"2510.16079v3","created_at":"2026-05-20T00:02:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.16079","created_at":"2026-05-20T00:02:57Z"},{"alias_kind":"pith_short_12","alias_value":"OQ64JU6F5TDZ","created_at":"2026-05-20T00:02:57Z"},{"alias_kind":"pith_short_16","alias_value":"OQ64JU6F5TDZ6XRD","created_at":"2026-05-20T00:02:57Z"},{"alias_kind":"pith_short_8","alias_value":"OQ64JU6F","created_at":"2026-05-20T00:02:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:OQ64JU6F5TDZ6XRD5FHHR4LIHX","target":"record","payload":{"canonical_record":{"source":{"id":"2510.16079","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-10-17T12:03:16Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"d4825c1c87b5ebd256232f1f3a1cedfe6979962acbe37e9f9157ec0667035bda","abstract_canon_sha256":"3083a336ee43f983d3fe891a0a6538b55e9271247773dc43de7c0eefe69b35ac"},"schema_version":"1.0"},"canonical_sha256":"743dc4d3c5ecc79f5e23e94e78f1683dd13e96e1506f0d0918cb3c5972a866e6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:02:57.449476Z","signature_b64":"7usMy3X7VAsxuSey1xRKCm5ZWe9fHYOfWL/105ySGo3letLBQw9bi1vg1RC6byMJ8xobVUK32TSEgemqJUa6BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"743dc4d3c5ecc79f5e23e94e78f1683dd13e96e1506f0d0918cb3c5972a866e6","last_reissued_at":"2026-05-20T00:02:57.448614Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:02:57.448614Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2510.16079","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:02:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"U1pfmMuJEG9qbvYywl2zPXP9k+n1hloKlTD+BgMPB6yQoM7S0VeWIMqeQRH9U1K9ihmOpKgr8FRIDdQ/dJ+2Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T22:05:50.910528Z"},"content_sha256":"43b9fd78fd66f2cace58faf329c92c016da4c9c90637e51eec3c76503733bfed","schema_version":"1.0","event_id":"sha256:43b9fd78fd66f2cace58faf329c92c016da4c9c90637e51eec3c76503733bfed"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:OQ64JU6F5TDZ6XRD5FHHR4LIHX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"EvolveR: Self-Evolving LLM Agents through an Experience-Driven Lifecycle","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"EvolveR lets LLM agents self-improve by distilling their own interaction trajectories into reusable strategic principles and then reinforcing policies in a closed loop.","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Botian Shi, Cheng Yang, Daocheng Fu, Jianbiao Mei, Licheng Wen, Pinlong Cai, Rong Wu, Xiaoman Wang, Xuemeng Yang, Yufan Shen, Yuxin Wang","submitted_at":"2025-10-17T12:03:16Z","abstract_excerpt":"Current Large Language Model (LLM) agents show strong performance in tool use, but lack the crucial capability to systematically learn from their own experiences. While existing frameworks mainly focus on mitigating external knowledge gaps, they fail to address a more fundamental limitation: the inability to iteratively refine problem-solving strategies. In this work, we introduce EvolveR, a framework designed to enable agent to self-improve through a complete, closed-loop experience lifecycle. This lifecycle comprises two key stages: (1) Offline Self-Distillation, where the agent's interactio"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"We demonstrate the effectiveness of EvolveR on complex multi-hop question-answering benchmarks, where it achieves superior performance over strong agentic baselines.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That distilling raw interaction trajectories into abstract reusable strategic principles will produce guidance that generalizes across tasks and that the policy reinforcement mechanism will produce genuine iterative improvement rather than superficial or unstable changes.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"EvolveR proposes a closed-loop self-evolution system for LLM agents that distills experiences into principles offline and applies reinforcement during online task interactions to achieve better performance on multi-hop QA tasks.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"EvolveR lets LLM agents self-improve by distilling their own interaction trajectories into reusable strategic principles and then reinforcing policies in a closed loop.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"8c08547cc6b7767c84cc5eb5a20eeaab09faf2d28ed6fad4a07950615a04e308"},"source":{"id":"2510.16079","kind":"arxiv","version":3},"verdict":{"id":"4369834d-e741-4ceb-b2ee-6e07e8eeb41a","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-18T06:16:41.886766Z","strongest_claim":"We demonstrate the effectiveness of EvolveR on complex multi-hop question-answering benchmarks, where it achieves superior performance over strong agentic baselines.","one_line_summary":"EvolveR proposes a closed-loop self-evolution system for LLM agents that distills experiences into principles offline and applies reinforcement during online task interactions to achieve better performance on multi-hop QA tasks.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That distilling raw interaction trajectories into abstract reusable strategic principles will produce guidance that generalizes across tasks and that the policy reinforcement mechanism will produce genuine iterative improvement rather than superficial or unstable changes.","pith_extraction_headline":"EvolveR lets LLM agents self-improve by distilling their own interaction trajectories into reusable strategic principles and then reinforcing policies in a closed loop."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2510.16079/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":2,"snapshot_sha256":"b148cde97cdae4bd29933c7450f60cbb04aab91b26238edf48ad5f9fb7e4c39c"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"4369834d-e741-4ceb-b2ee-6e07e8eeb41a"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:02:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sNgTcS0VkvbblgflkQt0XhVDsSq8D1GLSWBDRRpoI4xNpqkoeNTQqvQhTVHF2FGe/QHIxjzHnNEmwjbtWbHZBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T22:05:50.910994Z"},"content_sha256":"b232b78ce49d61763e26fd557fe124ae18712ddc7cc07b2484ffb3a118cafe0d","schema_version":"1.0","event_id":"sha256:b232b78ce49d61763e26fd557fe124ae18712ddc7cc07b2484ffb3a118cafe0d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OQ64JU6F5TDZ6XRD5FHHR4LIHX/bundle.json","state_url":"https://pith.science/pith/OQ64JU6F5TDZ6XRD5FHHR4LIHX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OQ64JU6F5TDZ6XRD5FHHR4LIHX/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-02T22:05:50Z","links":{"resolver":"https://pith.science/pith/OQ64JU6F5TDZ6XRD5FHHR4LIHX","bundle":"https://pith.science/pith/OQ64JU6F5TDZ6XRD5FHHR4LIHX/bundle.json","state":"https://pith.science/pith/OQ64JU6F5TDZ6XRD5FHHR4LIHX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OQ64JU6F5TDZ6XRD5FHHR4LIHX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:OQ64JU6F5TDZ6XRD5FHHR4LIHX","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"3083a336ee43f983d3fe891a0a6538b55e9271247773dc43de7c0eefe69b35ac","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-10-17T12:03:16Z","title_canon_sha256":"d4825c1c87b5ebd256232f1f3a1cedfe6979962acbe37e9f9157ec0667035bda"},"schema_version":"1.0","source":{"id":"2510.16079","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2510.16079","created_at":"2026-05-20T00:02:57Z"},{"alias_kind":"arxiv_version","alias_value":"2510.16079v3","created_at":"2026-05-20T00:02:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.16079","created_at":"2026-05-20T00:02:57Z"},{"alias_kind":"pith_short_12","alias_value":"OQ64JU6F5TDZ","created_at":"2026-05-20T00:02:57Z"},{"alias_kind":"pith_short_16","alias_value":"OQ64JU6F5TDZ6XRD","created_at":"2026-05-20T00:02:57Z"},{"alias_kind":"pith_short_8","alias_value":"OQ64JU6F","created_at":"2026-05-20T00:02:57Z"}],"graph_snapshots":[{"event_id":"sha256:b232b78ce49d61763e26fd557fe124ae18712ddc7cc07b2484ffb3a118cafe0d","target":"graph","created_at":"2026-05-20T00:02:57Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"We demonstrate the effectiveness of EvolveR on complex multi-hop question-answering benchmarks, where it achieves superior performance over strong agentic baselines."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That distilling raw interaction trajectories into abstract reusable strategic principles will produce guidance that generalizes across tasks and that the policy reinforcement mechanism will produce genuine iterative improvement rather than superficial or unstable changes."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"EvolveR proposes a closed-loop self-evolution system for LLM agents that distills experiences into principles offline and applies reinforcement during online task interactions to achieve better performance on multi-hop QA tasks."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"EvolveR lets LLM agents self-improve by distilling their own interaction trajectories into reusable strategic principles and then reinforcing policies in a closed loop."}],"snapshot_sha256":"8c08547cc6b7767c84cc5eb5a20eeaab09faf2d28ed6fad4a07950615a04e308"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"b148cde97cdae4bd29933c7450f60cbb04aab91b26238edf48ad5f9fb7e4c39c"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2510.16079/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Current Large Language Model (LLM) agents show strong performance in tool use, but lack the crucial capability to systematically learn from their own experiences. While existing frameworks mainly focus on mitigating external knowledge gaps, they fail to address a more fundamental limitation: the inability to iteratively refine problem-solving strategies. In this work, we introduce EvolveR, a framework designed to enable agent to self-improve through a complete, closed-loop experience lifecycle. This lifecycle comprises two key stages: (1) Offline Self-Distillation, where the agent's interactio","authors_text":"Botian Shi, Cheng Yang, Daocheng Fu, Jianbiao Mei, Licheng Wen, Pinlong Cai, Rong Wu, Xiaoman Wang, Xuemeng Yang, Yufan Shen, Yuxin Wang","cross_cats":["cs.AI"],"headline":"EvolveR lets LLM agents self-improve by distilling their own interaction trajectories into reusable strategic principles and then reinforcing policies in a closed loop.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-10-17T12:03:16Z","title":"EvolveR: Self-Evolving LLM Agents through an Experience-Driven Lifecycle"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2510.16079","kind":"arxiv","version":3},"verdict":{"created_at":"2026-05-18T06:16:41.886766Z","id":"4369834d-e741-4ceb-b2ee-6e07e8eeb41a","model_set":{"reader":"grok-4.3"},"one_line_summary":"EvolveR proposes a closed-loop self-evolution system for LLM agents that distills experiences into principles offline and applies reinforcement during online task interactions to achieve better performance on multi-hop QA tasks.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"EvolveR lets LLM agents self-improve by distilling their own interaction trajectories into reusable strategic principles and then reinforcing policies in a closed loop.","strongest_claim":"We demonstrate the effectiveness of EvolveR on complex multi-hop question-answering benchmarks, where it achieves superior performance over strong agentic baselines.","weakest_assumption":"That distilling raw interaction trajectories into abstract reusable strategic principles will produce guidance that generalizes across tasks and that the policy reinforcement mechanism will produce genuine iterative improvement rather than superficial or unstable changes."}},"verdict_id":"4369834d-e741-4ceb-b2ee-6e07e8eeb41a"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:43b9fd78fd66f2cace58faf329c92c016da4c9c90637e51eec3c76503733bfed","target":"record","created_at":"2026-05-20T00:02:57Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"3083a336ee43f983d3fe891a0a6538b55e9271247773dc43de7c0eefe69b35ac","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-10-17T12:03:16Z","title_canon_sha256":"d4825c1c87b5ebd256232f1f3a1cedfe6979962acbe37e9f9157ec0667035bda"},"schema_version":"1.0","source":{"id":"2510.16079","kind":"arxiv","version":3}},"canonical_sha256":"743dc4d3c5ecc79f5e23e94e78f1683dd13e96e1506f0d0918cb3c5972a866e6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"743dc4d3c5ecc79f5e23e94e78f1683dd13e96e1506f0d0918cb3c5972a866e6","first_computed_at":"2026-05-20T00:02:57.448614Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:02:57.448614Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7usMy3X7VAsxuSey1xRKCm5ZWe9fHYOfWL/105ySGo3letLBQw9bi1vg1RC6byMJ8xobVUK32TSEgemqJUa6BA==","signature_status":"signed_v1","signed_at":"2026-05-20T00:02:57.449476Z","signed_message":"canonical_sha256_bytes"},"source_id":"2510.16079","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:43b9fd78fd66f2cace58faf329c92c016da4c9c90637e51eec3c76503733bfed","sha256:b232b78ce49d61763e26fd557fe124ae18712ddc7cc07b2484ffb3a118cafe0d"],"state_sha256":"7ab9de4064a0b6c6bbf1b904f8cb172937a03a257cdfa137717d291abda71a7d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OkvWqETzACtr2P7zNQzjId9TC60IYaYV7I26DIY6i91NQb0xw/be9zAVYNqLXJ8Wu39kVplaJQ2uZHFB6PBqAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T22:05:50.913208Z","bundle_sha256":"847da162d5663a35e062cf11c136637cef233ee190b91f66c5a0cae9ce1eb526"}}