{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:6IUX27RTTXN5DZIOGFOPUM5Q2R","short_pith_number":"pith:6IUX27RT","canonical_record":{"source":{"id":"2606.08755","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-07T17:55:55Z","cross_cats_sorted":[],"title_canon_sha256":"f7ecb9f0116c26872483ef8eee1c3fb39fe8e32147827db5ad579262c5c5a4b1","abstract_canon_sha256":"79d14ac2386c2d2bf8ebb1aa497ac13a0188571b956fdc3dd4f332b710aa7604"},"schema_version":"1.0"},"canonical_sha256":"f2297d7e339ddbd1e50e315cfa33b0d465e8285a0f3c74508afec8b2b0443961","source":{"kind":"arxiv","id":"2606.08755","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.08755","created_at":"2026-06-09T02:07:37Z"},{"alias_kind":"arxiv_version","alias_value":"2606.08755v1","created_at":"2026-06-09T02:07:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.08755","created_at":"2026-06-09T02:07:37Z"},{"alias_kind":"pith_short_12","alias_value":"6IUX27RTTXN5","created_at":"2026-06-09T02:07:37Z"},{"alias_kind":"pith_short_16","alias_value":"6IUX27RTTXN5DZIO","created_at":"2026-06-09T02:07:37Z"},{"alias_kind":"pith_short_8","alias_value":"6IUX27RT","created_at":"2026-06-09T02:07:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:6IUX27RTTXN5DZIOGFOPUM5Q2R","target":"record","payload":{"canonical_record":{"source":{"id":"2606.08755","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-07T17:55:55Z","cross_cats_sorted":[],"title_canon_sha256":"f7ecb9f0116c26872483ef8eee1c3fb39fe8e32147827db5ad579262c5c5a4b1","abstract_canon_sha256":"79d14ac2386c2d2bf8ebb1aa497ac13a0188571b956fdc3dd4f332b710aa7604"},"schema_version":"1.0"},"canonical_sha256":"f2297d7e339ddbd1e50e315cfa33b0d465e8285a0f3c74508afec8b2b0443961","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-09T02:07:37.565781Z","signature_b64":"2OEA9OcEfAdpJRGJp65Ejh4dD9NHEm5nd98g9KUUS37N5iiwTYbUpIRGJl+At/7XoJWp/H8M5F4pI1GKCAMaBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f2297d7e339ddbd1e50e315cfa33b0d465e8285a0f3c74508afec8b2b0443961","last_reissued_at":"2026-06-09T02:07:37.564910Z","signature_status":"signed_v1","first_computed_at":"2026-06-09T02:07:37.564910Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.08755","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-09T02:07:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BHhYLGt6npBIVmA8LOrJRRyERoXxb9+lKbwMaD17GJ5ZWcqXpbKX+bDAYY94FViVbpifY0bRfVhQmAeyIdf2Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-28T01:35:28.801459Z"},"content_sha256":"19ae24f489c13a40119d11590032fc0fcf069ee92452ad44e2fbb00de2434336","schema_version":"1.0","event_id":"sha256:19ae24f489c13a40119d11590032fc0fcf069ee92452ad44e2fbb00de2434336"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:6IUX27RTTXN5DZIOGFOPUM5Q2R","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Co-Evolving Skill Generation and Policy Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Fenglong Ma, Linlin Wu, Nikki Lijing Kuang, Songtao Liu, Xiaomin Li, Yudi Lin, Zhiwei Zhang","submitted_at":"2026-06-07T17:55:55Z","abstract_excerpt":"Skill-augmented reinforcement learning improves language agents by storing reusable procedural knowledge acquired from past experience. Existing methods typically use strong language models to analyze trajectories, generate skills, and update a retrievable skill bank during online training. However, they rarely assess whether a newly generated skill is useful before it is stored and reused. We find that this assumption is unreliable: even skills generated by proprietary frontier LLMs exhibit highly mixed utility, with many providing little benefit or even degrading performance. Once such skill"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.08755","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/2606.08755/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-09T02:07:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gdsBZErvmosWAiUnbzm016t66cY+QrqvvMEtRb8G/Cxct2QvcbRHi/3YIClFhW7qLN0uJ/3+0hs8so50MTheBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-28T01:35:28.801833Z"},"content_sha256":"41da999d635085d6251b845e492290697edd3f23fdd804b8a48961db58aa0870","schema_version":"1.0","event_id":"sha256:41da999d635085d6251b845e492290697edd3f23fdd804b8a48961db58aa0870"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6IUX27RTTXN5DZIOGFOPUM5Q2R/bundle.json","state_url":"https://pith.science/pith/6IUX27RTTXN5DZIOGFOPUM5Q2R/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6IUX27RTTXN5DZIOGFOPUM5Q2R/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":"2026-06-28T01:35:28Z","links":{"resolver":"https://pith.science/pith/6IUX27RTTXN5DZIOGFOPUM5Q2R","bundle":"https://pith.science/pith/6IUX27RTTXN5DZIOGFOPUM5Q2R/bundle.json","state":"https://pith.science/pith/6IUX27RTTXN5DZIOGFOPUM5Q2R/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6IUX27RTTXN5DZIOGFOPUM5Q2R/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:6IUX27RTTXN5DZIOGFOPUM5Q2R","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":"79d14ac2386c2d2bf8ebb1aa497ac13a0188571b956fdc3dd4f332b710aa7604","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-07T17:55:55Z","title_canon_sha256":"f7ecb9f0116c26872483ef8eee1c3fb39fe8e32147827db5ad579262c5c5a4b1"},"schema_version":"1.0","source":{"id":"2606.08755","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.08755","created_at":"2026-06-09T02:07:37Z"},{"alias_kind":"arxiv_version","alias_value":"2606.08755v1","created_at":"2026-06-09T02:07:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.08755","created_at":"2026-06-09T02:07:37Z"},{"alias_kind":"pith_short_12","alias_value":"6IUX27RTTXN5","created_at":"2026-06-09T02:07:37Z"},{"alias_kind":"pith_short_16","alias_value":"6IUX27RTTXN5DZIO","created_at":"2026-06-09T02:07:37Z"},{"alias_kind":"pith_short_8","alias_value":"6IUX27RT","created_at":"2026-06-09T02:07:37Z"}],"graph_snapshots":[{"event_id":"sha256:41da999d635085d6251b845e492290697edd3f23fdd804b8a48961db58aa0870","target":"graph","created_at":"2026-06-09T02:07:37Z","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":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2606.08755/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Skill-augmented reinforcement learning improves language agents by storing reusable procedural knowledge acquired from past experience. Existing methods typically use strong language models to analyze trajectories, generate skills, and update a retrievable skill bank during online training. However, they rarely assess whether a newly generated skill is useful before it is stored and reused. We find that this assumption is unreliable: even skills generated by proprietary frontier LLMs exhibit highly mixed utility, with many providing little benefit or even degrading performance. Once such skill","authors_text":"Fenglong Ma, Linlin Wu, Nikki Lijing Kuang, Songtao Liu, Xiaomin Li, Yudi Lin, Zhiwei Zhang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-07T17:55:55Z","title":"Co-Evolving Skill Generation and Policy Optimization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.08755","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:19ae24f489c13a40119d11590032fc0fcf069ee92452ad44e2fbb00de2434336","target":"record","created_at":"2026-06-09T02:07:37Z","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":"79d14ac2386c2d2bf8ebb1aa497ac13a0188571b956fdc3dd4f332b710aa7604","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-07T17:55:55Z","title_canon_sha256":"f7ecb9f0116c26872483ef8eee1c3fb39fe8e32147827db5ad579262c5c5a4b1"},"schema_version":"1.0","source":{"id":"2606.08755","kind":"arxiv","version":1}},"canonical_sha256":"f2297d7e339ddbd1e50e315cfa33b0d465e8285a0f3c74508afec8b2b0443961","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f2297d7e339ddbd1e50e315cfa33b0d465e8285a0f3c74508afec8b2b0443961","first_computed_at":"2026-06-09T02:07:37.564910Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-09T02:07:37.564910Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2OEA9OcEfAdpJRGJp65Ejh4dD9NHEm5nd98g9KUUS37N5iiwTYbUpIRGJl+At/7XoJWp/H8M5F4pI1GKCAMaBQ==","signature_status":"signed_v1","signed_at":"2026-06-09T02:07:37.565781Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.08755","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:19ae24f489c13a40119d11590032fc0fcf069ee92452ad44e2fbb00de2434336","sha256:41da999d635085d6251b845e492290697edd3f23fdd804b8a48961db58aa0870"],"state_sha256":"fe4f8421ba4def9fbe2865603962ea77b500c848edc709df180b4d8c47023d17"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"joiAy+/SkuedGVMBgcdrpNqYpOU7SLBkJ+R6hel/+4rZiCjRvH/YwRZq6nAzcDA2sbZ1z78vkhICWACKSstEAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-28T01:35:28.803784Z","bundle_sha256":"759bbc560068df76dc039f4e83b19c0eb537ec1fa84f3192619daa8035638c6b"}}