{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:MUY5UOQ7SSDXOP5F5YRHVDIMXY","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":"bacfe30461d56f4bef883a00955e753d5164c961505be519aac265b0287f4e37","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-28T23:11:13Z","title_canon_sha256":"48e256719bee5a18b58b9d5f5c7e98a209c483c0d1fc7a85e2c0a012eec4469c"},"schema_version":"1.0","source":{"id":"2605.30651","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.30651","created_at":"2026-06-01T01:03:06Z"},{"alias_kind":"arxiv_version","alias_value":"2605.30651v1","created_at":"2026-06-01T01:03:06Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.30651","created_at":"2026-06-01T01:03:06Z"},{"alias_kind":"pith_short_12","alias_value":"MUY5UOQ7SSDX","created_at":"2026-06-01T01:03:06Z"},{"alias_kind":"pith_short_16","alias_value":"MUY5UOQ7SSDXOP5F","created_at":"2026-06-01T01:03:06Z"},{"alias_kind":"pith_short_8","alias_value":"MUY5UOQ7","created_at":"2026-06-01T01:03:06Z"}],"graph_snapshots":[{"event_id":"sha256:47bfd6ea0e9f41d38f5910751d9c2f897a33d042a7c071d98531a20005166594","target":"graph","created_at":"2026-06-01T01:03:06Z","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/2605.30651/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We study trajectory selection for reasoning distillation, where teacher-generated reasoning trajectories are selectively used as supervision for a student model. Existing methods rely on heuristics such as trajectory quality or model confidence, but they often overlook whether a trajectory is learnable by the student. In this paper, we present LARK, a learnability-grounded method for reasoning trajectory selection. LARK selects trajectories that the student can learn efficiently while preserving the generalization of the full training distribution. At the core of LARK is a learnability factor ","authors_text":"Amanda Hughes, Chih-Chun Chen, Fenglong Ma, Kaixiang Zhao, Porter Jenkins, Taylor W. Killian, Tianrun Yu, Weitong Zhang","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-28T23:11:13Z","title":"LARK: Learnability-Grounded Trajectory Selection for Efficient Reasoning Distillation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.30651","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":"sha256:3729b9b61b16796544c70f75eaac4f37935446555e49498e95d64fa384702e8f","target":"record","created_at":"2026-06-01T01:03:06Z","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":"bacfe30461d56f4bef883a00955e753d5164c961505be519aac265b0287f4e37","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-28T23:11:13Z","title_canon_sha256":"48e256719bee5a18b58b9d5f5c7e98a209c483c0d1fc7a85e2c0a012eec4469c"},"schema_version":"1.0","source":{"id":"2605.30651","kind":"arxiv","version":1}},"canonical_sha256":"6531da3a1f9487773fa5ee227a8d0cbe0a917b345e3a4ddfd4710a798daf44bb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6531da3a1f9487773fa5ee227a8d0cbe0a917b345e3a4ddfd4710a798daf44bb","first_computed_at":"2026-06-01T01:03:06.391538Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-01T01:03:06.391538Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"DhHC9+v49Kjl7R18YrWTdA/upQdDCt1A5kmoP4Ir59l4ZaF7ZiXpGEes+ktCj+0P4IpGfpzj61Cgy/TgOe9WCw==","signature_status":"signed_v1","signed_at":"2026-06-01T01:03:06.393269Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.30651","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3729b9b61b16796544c70f75eaac4f37935446555e49498e95d64fa384702e8f","sha256:47bfd6ea0e9f41d38f5910751d9c2f897a33d042a7c071d98531a20005166594"],"state_sha256":"4064b0127efdd1cea96e8454afe6256808ad1d2be8e77cd2406c6c09bde2899b"}