{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:T6VVSJDCEQZ6DFFPXSQ4SOKJSV","short_pith_number":"pith:T6VVSJDC","canonical_record":{"source":{"id":"2605.22205","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-21T09:12:20Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"37d01373f8f675a8eac3a8efd84fd1f5bea20bb6c7319b94005a41c37d3f6061","abstract_canon_sha256":"f91d6ce351bd74386f86226da552e1c7d28dfc696304937d933abab9e40ea972"},"schema_version":"1.0"},"canonical_sha256":"9fab5924622433e194afbca1c939499577171e3641a73f23e3edeb4c10f9bd3c","source":{"kind":"arxiv","id":"2605.22205","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.22205","created_at":"2026-05-22T01:04:32Z"},{"alias_kind":"arxiv_version","alias_value":"2605.22205v1","created_at":"2026-05-22T01:04:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.22205","created_at":"2026-05-22T01:04:32Z"},{"alias_kind":"pith_short_12","alias_value":"T6VVSJDCEQZ6","created_at":"2026-05-22T01:04:32Z"},{"alias_kind":"pith_short_16","alias_value":"T6VVSJDCEQZ6DFFP","created_at":"2026-05-22T01:04:32Z"},{"alias_kind":"pith_short_8","alias_value":"T6VVSJDC","created_at":"2026-05-22T01:04:32Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:T6VVSJDCEQZ6DFFPXSQ4SOKJSV","target":"record","payload":{"canonical_record":{"source":{"id":"2605.22205","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-21T09:12:20Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"37d01373f8f675a8eac3a8efd84fd1f5bea20bb6c7319b94005a41c37d3f6061","abstract_canon_sha256":"f91d6ce351bd74386f86226da552e1c7d28dfc696304937d933abab9e40ea972"},"schema_version":"1.0"},"canonical_sha256":"9fab5924622433e194afbca1c939499577171e3641a73f23e3edeb4c10f9bd3c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-22T01:04:32.143293Z","signature_b64":"t8QBPxagSTyOHzIOYFz2TPd0CdQyKSF2UvhjtbolvNX6ZWT/ds22PPAcg0rMGqJ62A7kuA4JlP5HD1o0Al5iDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9fab5924622433e194afbca1c939499577171e3641a73f23e3edeb4c10f9bd3c","last_reissued_at":"2026-05-22T01:04:32.142518Z","signature_status":"signed_v1","first_computed_at":"2026-05-22T01:04:32.142518Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.22205","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":"2026-05-22T01:04:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0YTLhMToMvRrHuOnqjjg7rhw1IaglOikXW4WYoHa9vLrHE9V4YWzzPbIdYG6Ypq8vDxylQrpqIDfvXaGJYcHCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T18:19:44.020231Z"},"content_sha256":"16367742295ef6f58ee1409bdf01463d8eaf5f9438d1dca0e16dbe17d635d344","schema_version":"1.0","event_id":"sha256:16367742295ef6f58ee1409bdf01463d8eaf5f9438d1dca0e16dbe17d635d344"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:T6VVSJDCEQZ6DFFPXSQ4SOKJSV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Skill Weaving: Efficient LLM Improvement via Modular Skillpacks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Guodong Du, Jiabo Zhang, Jing Li, Weijun Yao, Weiyang Guo, Yuan Zhou, Zesheng Shi, Zhuo Li","submitted_at":"2026-05-21T09:12:20Z","abstract_excerpt":"Large language models increasingly require specialization across diverse domains, yet existing approaches struggle to balance multi-domain capacities with strict memory and inference constraints. In this work, we introduce SkillWeave, a modular improvement framework that enables LLMs to specialize under fixed memory budgets. SkillWeave partitions full capabilities of a general-purpose model into skillpacks -- lightweight, domain-specific delta modules -- that reorganize and refine the model's internal knowledge. For efficient deployment, SkillWeave integrates SkillZip to compress skillpacks in"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.22205","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/2605.22205/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":"2026-05-22T01:04:32Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iydd4r/4B3dqAB91P9zMHwGWrQoXnYup5vQo75nmlVmfla/QtHXlS9qIAAQj3jGOxIpOeEAVDMCuUUB+o03ABw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T18:19:44.021027Z"},"content_sha256":"0814003c5bad98ff68ca1d0c70a999d4a4c1dc9c185b7bfef45f522bf204842c","schema_version":"1.0","event_id":"sha256:0814003c5bad98ff68ca1d0c70a999d4a4c1dc9c185b7bfef45f522bf204842c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/T6VVSJDCEQZ6DFFPXSQ4SOKJSV/bundle.json","state_url":"https://pith.science/pith/T6VVSJDCEQZ6DFFPXSQ4SOKJSV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/T6VVSJDCEQZ6DFFPXSQ4SOKJSV/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":"2026-05-25T18:19:44Z","links":{"resolver":"https://pith.science/pith/T6VVSJDCEQZ6DFFPXSQ4SOKJSV","bundle":"https://pith.science/pith/T6VVSJDCEQZ6DFFPXSQ4SOKJSV/bundle.json","state":"https://pith.science/pith/T6VVSJDCEQZ6DFFPXSQ4SOKJSV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/T6VVSJDCEQZ6DFFPXSQ4SOKJSV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:T6VVSJDCEQZ6DFFPXSQ4SOKJSV","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":"f91d6ce351bd74386f86226da552e1c7d28dfc696304937d933abab9e40ea972","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-21T09:12:20Z","title_canon_sha256":"37d01373f8f675a8eac3a8efd84fd1f5bea20bb6c7319b94005a41c37d3f6061"},"schema_version":"1.0","source":{"id":"2605.22205","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.22205","created_at":"2026-05-22T01:04:32Z"},{"alias_kind":"arxiv_version","alias_value":"2605.22205v1","created_at":"2026-05-22T01:04:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.22205","created_at":"2026-05-22T01:04:32Z"},{"alias_kind":"pith_short_12","alias_value":"T6VVSJDCEQZ6","created_at":"2026-05-22T01:04:32Z"},{"alias_kind":"pith_short_16","alias_value":"T6VVSJDCEQZ6DFFP","created_at":"2026-05-22T01:04:32Z"},{"alias_kind":"pith_short_8","alias_value":"T6VVSJDC","created_at":"2026-05-22T01:04:32Z"}],"graph_snapshots":[{"event_id":"sha256:0814003c5bad98ff68ca1d0c70a999d4a4c1dc9c185b7bfef45f522bf204842c","target":"graph","created_at":"2026-05-22T01:04:32Z","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/2605.22205/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large language models increasingly require specialization across diverse domains, yet existing approaches struggle to balance multi-domain capacities with strict memory and inference constraints. In this work, we introduce SkillWeave, a modular improvement framework that enables LLMs to specialize under fixed memory budgets. SkillWeave partitions full capabilities of a general-purpose model into skillpacks -- lightweight, domain-specific delta modules -- that reorganize and refine the model's internal knowledge. For efficient deployment, SkillWeave integrates SkillZip to compress skillpacks in","authors_text":"Guodong Du, Jiabo Zhang, Jing Li, Weijun Yao, Weiyang Guo, Yuan Zhou, Zesheng Shi, Zhuo Li","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-21T09:12:20Z","title":"Skill Weaving: Efficient LLM Improvement via Modular Skillpacks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.22205","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":"sha256:16367742295ef6f58ee1409bdf01463d8eaf5f9438d1dca0e16dbe17d635d344","target":"record","created_at":"2026-05-22T01:04:32Z","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":"f91d6ce351bd74386f86226da552e1c7d28dfc696304937d933abab9e40ea972","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-05-21T09:12:20Z","title_canon_sha256":"37d01373f8f675a8eac3a8efd84fd1f5bea20bb6c7319b94005a41c37d3f6061"},"schema_version":"1.0","source":{"id":"2605.22205","kind":"arxiv","version":1}},"canonical_sha256":"9fab5924622433e194afbca1c939499577171e3641a73f23e3edeb4c10f9bd3c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9fab5924622433e194afbca1c939499577171e3641a73f23e3edeb4c10f9bd3c","first_computed_at":"2026-05-22T01:04:32.142518Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-22T01:04:32.142518Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"t8QBPxagSTyOHzIOYFz2TPd0CdQyKSF2UvhjtbolvNX6ZWT/ds22PPAcg0rMGqJ62A7kuA4JlP5HD1o0Al5iDg==","signature_status":"signed_v1","signed_at":"2026-05-22T01:04:32.143293Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.22205","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:16367742295ef6f58ee1409bdf01463d8eaf5f9438d1dca0e16dbe17d635d344","sha256:0814003c5bad98ff68ca1d0c70a999d4a4c1dc9c185b7bfef45f522bf204842c"],"state_sha256":"cf8c530a414fc53bb2cb288c5842d548bc5d69a0d5dca5dee0178fdf9ef725bd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fQq19MvC7JG/ASlMYfJ0EazhxIB62Cdmc8qRkpdwOQ0YNktnVQR7mJCMimaVwV7/vjQxwRf/bHukhoJMLc8MDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T18:19:44.028241Z","bundle_sha256":"1f8579d246931d61f1efa11ada60aee98cb200e9bfa0608e4fc42abe007b2142"}}