{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:B7QG5KVAKAQBFYDWXCX3YP7QMI","short_pith_number":"pith:B7QG5KVA","canonical_record":{"source":{"id":"2606.29999","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-06-29T09:09:16Z","cross_cats_sorted":[],"title_canon_sha256":"a86b1761efee0effa5067d309d9745220755745cae777abee81453f26e51aaab","abstract_canon_sha256":"6ef5db9c3d363dc8f9bf8840a7f98dc9186afbe9e74ac6ea7c39b0b0bdca49ae"},"schema_version":"1.0"},"canonical_sha256":"0fe06eaaa0502012e076b8afbc3ff062220bbdb43987644774447c016b878c8e","source":{"kind":"arxiv","id":"2606.29999","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.29999","created_at":"2026-06-30T02:17:44Z"},{"alias_kind":"arxiv_version","alias_value":"2606.29999v1","created_at":"2026-06-30T02:17:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.29999","created_at":"2026-06-30T02:17:44Z"},{"alias_kind":"pith_short_12","alias_value":"B7QG5KVAKAQB","created_at":"2026-06-30T02:17:44Z"},{"alias_kind":"pith_short_16","alias_value":"B7QG5KVAKAQBFYDW","created_at":"2026-06-30T02:17:44Z"},{"alias_kind":"pith_short_8","alias_value":"B7QG5KVA","created_at":"2026-06-30T02:17:44Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:B7QG5KVAKAQBFYDWXCX3YP7QMI","target":"record","payload":{"canonical_record":{"source":{"id":"2606.29999","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-06-29T09:09:16Z","cross_cats_sorted":[],"title_canon_sha256":"a86b1761efee0effa5067d309d9745220755745cae777abee81453f26e51aaab","abstract_canon_sha256":"6ef5db9c3d363dc8f9bf8840a7f98dc9186afbe9e74ac6ea7c39b0b0bdca49ae"},"schema_version":"1.0"},"canonical_sha256":"0fe06eaaa0502012e076b8afbc3ff062220bbdb43987644774447c016b878c8e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-30T02:17:44.988996Z","signature_b64":"MTKV8gummsQah3vomREcnQgMau0Vqlc5L+kOnomZuRmPlSJp9SsKdQKicb/Ie9duLFT+bY5xAIO6Kq53bJ9LCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0fe06eaaa0502012e076b8afbc3ff062220bbdb43987644774447c016b878c8e","last_reissued_at":"2026-06-30T02:17:44.988395Z","signature_status":"signed_v1","first_computed_at":"2026-06-30T02:17:44.988395Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.29999","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":"2026-06-30T02:17:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8uGtAsPxa324YppPzwfkKpZM2AU5vRpnvrzi/BIAnK1yDEM+qLyud4rKoMvsF+Pm33QjtHCvHNbxZ7hOluDzBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-02T04:10:00.975414Z"},"content_sha256":"758972e0d484f5cfc0e0135330b269993c34d586b1901ae2a23e61418b11a8cc","schema_version":"1.0","event_id":"sha256:758972e0d484f5cfc0e0135330b269993c34d586b1901ae2a23e61418b11a8cc"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:B7QG5KVAKAQBFYDWXCX3YP7QMI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"AlgoSkill: Learning to Design Algorithms by Scheduling Human-Like Skills","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Liang Zhao, Xinyuan Song, Zekun Cai","submitted_at":"2026-06-29T09:09:16Z","abstract_excerpt":"Designing an algorithm from a natural-language problem statement requires identifying the problem structure, reading constraints, choosing a suitable paradigm, checking correctness, and refining complexity. Existing large language model (LLM) methods often rely on direct generation or generic self-refinement, leaving these steps implicit. We propose AlgoSkill, which models algorithm design as sequential decision-making over a typed library of algorithmic skills, including abstraction, constraint analysis, state design, data-structure selection, proof checking, counterexample construction, and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.29999","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/2606.29999/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":"2026-06-30T02:17:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qtW1SEkF0nddnu/FdnZLPZ88URvsXBN0yJMA9Q8I++d4LDziKrdtw2ZvgPt61sXi+QyW1iP1XlfflmqGVhpMCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-02T04:10:00.975810Z"},"content_sha256":"763e808adb41990c8288d8da3f94580126215a319cb15407e47934f9b53280eb","schema_version":"1.0","event_id":"sha256:763e808adb41990c8288d8da3f94580126215a319cb15407e47934f9b53280eb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/B7QG5KVAKAQBFYDWXCX3YP7QMI/bundle.json","state_url":"https://pith.science/pith/B7QG5KVAKAQBFYDWXCX3YP7QMI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/B7QG5KVAKAQBFYDWXCX3YP7QMI/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":"2026-07-02T04:10:00Z","links":{"resolver":"https://pith.science/pith/B7QG5KVAKAQBFYDWXCX3YP7QMI","bundle":"https://pith.science/pith/B7QG5KVAKAQBFYDWXCX3YP7QMI/bundle.json","state":"https://pith.science/pith/B7QG5KVAKAQBFYDWXCX3YP7QMI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/B7QG5KVAKAQBFYDWXCX3YP7QMI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:B7QG5KVAKAQBFYDWXCX3YP7QMI","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":"6ef5db9c3d363dc8f9bf8840a7f98dc9186afbe9e74ac6ea7c39b0b0bdca49ae","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-06-29T09:09:16Z","title_canon_sha256":"a86b1761efee0effa5067d309d9745220755745cae777abee81453f26e51aaab"},"schema_version":"1.0","source":{"id":"2606.29999","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.29999","created_at":"2026-06-30T02:17:44Z"},{"alias_kind":"arxiv_version","alias_value":"2606.29999v1","created_at":"2026-06-30T02:17:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.29999","created_at":"2026-06-30T02:17:44Z"},{"alias_kind":"pith_short_12","alias_value":"B7QG5KVAKAQB","created_at":"2026-06-30T02:17:44Z"},{"alias_kind":"pith_short_16","alias_value":"B7QG5KVAKAQBFYDW","created_at":"2026-06-30T02:17:44Z"},{"alias_kind":"pith_short_8","alias_value":"B7QG5KVA","created_at":"2026-06-30T02:17:44Z"}],"graph_snapshots":[{"event_id":"sha256:763e808adb41990c8288d8da3f94580126215a319cb15407e47934f9b53280eb","target":"graph","created_at":"2026-06-30T02:17:44Z","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/2606.29999/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Designing an algorithm from a natural-language problem statement requires identifying the problem structure, reading constraints, choosing a suitable paradigm, checking correctness, and refining complexity. Existing large language model (LLM) methods often rely on direct generation or generic self-refinement, leaving these steps implicit. We propose AlgoSkill, which models algorithm design as sequential decision-making over a typed library of algorithmic skills, including abstraction, constraint analysis, state design, data-structure selection, proof checking, counterexample construction, and ","authors_text":"Liang Zhao, Xinyuan Song, Zekun Cai","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-06-29T09:09:16Z","title":"AlgoSkill: Learning to Design Algorithms by Scheduling Human-Like Skills"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.29999","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":"sha256:758972e0d484f5cfc0e0135330b269993c34d586b1901ae2a23e61418b11a8cc","target":"record","created_at":"2026-06-30T02:17:44Z","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":"6ef5db9c3d363dc8f9bf8840a7f98dc9186afbe9e74ac6ea7c39b0b0bdca49ae","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-06-29T09:09:16Z","title_canon_sha256":"a86b1761efee0effa5067d309d9745220755745cae777abee81453f26e51aaab"},"schema_version":"1.0","source":{"id":"2606.29999","kind":"arxiv","version":1}},"canonical_sha256":"0fe06eaaa0502012e076b8afbc3ff062220bbdb43987644774447c016b878c8e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0fe06eaaa0502012e076b8afbc3ff062220bbdb43987644774447c016b878c8e","first_computed_at":"2026-06-30T02:17:44.988395Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-30T02:17:44.988395Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"MTKV8gummsQah3vomREcnQgMau0Vqlc5L+kOnomZuRmPlSJp9SsKdQKicb/Ie9duLFT+bY5xAIO6Kq53bJ9LCQ==","signature_status":"signed_v1","signed_at":"2026-06-30T02:17:44.988996Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.29999","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:758972e0d484f5cfc0e0135330b269993c34d586b1901ae2a23e61418b11a8cc","sha256:763e808adb41990c8288d8da3f94580126215a319cb15407e47934f9b53280eb"],"state_sha256":"dff3fad995b34271435d546afa511f6a7ed4c6eacceb41ab5ca5694eabafd1bb"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8yyKqhP3RfSvpwAx7TqSv87zofqEC4LxLaXVI6SHK4aI012xPex0U2mTmHT/3+RIBry2gnJUj6xKH40LGe63Cg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-02T04:10:00.978042Z","bundle_sha256":"d6fce8b379bd743cae5211302bcab3f1d29400030d1c9137044b47e7d3ad9dbf"}}