{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:QYPJWJITPHXHSPAKJKWKDO3SR5","short_pith_number":"pith:QYPJWJIT","schema_version":"1.0","canonical_sha256":"861e9b251379ee793c0a4aaca1bb728f51b278780dbd5e46240a8023ea1cdf49","source":{"kind":"arxiv","id":"2607.03065","version":1},"attestation_state":"computed","paper":{"title":"Spectral Rewiring for Exploration, Purification, and Model Merging","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Hanlin Wu, Hao Zhou, Hongli Yu, Huan-ang Gao, Wei-Ying Ma, Ya-Qin Zhang, Yuxuan Song, Zhilong Zhang","submitted_at":"2026-07-03T07:57:31Z","abstract_excerpt":"Reinforcement learning has become a standard post-training recipe for large language models, but dense full-parameter updates create two deployment-relevant bottlenecks: suppressed reasoning performance, often reflected by premature saturation of test-time scaling, and interference when consolidating multiple capabilities through multi-domain training or model merging. We show that the reasoning-effective component of these updates is largely concentrated in the base model's spectral space, motivating Subspace-Aligned Rewiring (SAR), a post-hoc editing method that retains this spectral core wh"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2607.03065","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-07-03T07:57:31Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"d203684d43ecabb1bb29a3d5edf882c4641b80459279f54a96e0b79dffc7d74c","abstract_canon_sha256":"785e39a45e69ac014974b01c82e37db4013de040ab05e102557495fbe421245d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-07T01:16:41.714540Z","signature_b64":"Z+x7ASOZeaAFDujzmEv/qIkzsTdodKy3PTBnph+bz0NzfDs4FJYAbBHqeao9hLxHpYr8yHxQa8IjSKBQTo7eDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"861e9b251379ee793c0a4aaca1bb728f51b278780dbd5e46240a8023ea1cdf49","last_reissued_at":"2026-07-07T01:16:41.713964Z","signature_status":"signed_v1","first_computed_at":"2026-07-07T01:16:41.713964Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Spectral Rewiring for Exploration, Purification, and Model Merging","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Hanlin Wu, Hao Zhou, Hongli Yu, Huan-ang Gao, Wei-Ying Ma, Ya-Qin Zhang, Yuxuan Song, Zhilong Zhang","submitted_at":"2026-07-03T07:57:31Z","abstract_excerpt":"Reinforcement learning has become a standard post-training recipe for large language models, but dense full-parameter updates create two deployment-relevant bottlenecks: suppressed reasoning performance, often reflected by premature saturation of test-time scaling, and interference when consolidating multiple capabilities through multi-domain training or model merging. We show that the reasoning-effective component of these updates is largely concentrated in the base model's spectral space, motivating Subspace-Aligned Rewiring (SAR), a post-hoc editing method that retains this spectral core wh"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.03065","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/2607.03065/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2607.03065","created_at":"2026-07-07T01:16:41.714038+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.03065v1","created_at":"2026-07-07T01:16:41.714038+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.03065","created_at":"2026-07-07T01:16:41.714038+00:00"},{"alias_kind":"pith_short_12","alias_value":"QYPJWJITPHXH","created_at":"2026-07-07T01:16:41.714038+00:00"},{"alias_kind":"pith_short_16","alias_value":"QYPJWJITPHXHSPAK","created_at":"2026-07-07T01:16:41.714038+00:00"},{"alias_kind":"pith_short_8","alias_value":"QYPJWJIT","created_at":"2026-07-07T01:16:41.714038+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QYPJWJITPHXHSPAKJKWKDO3SR5","json":"https://pith.science/pith/QYPJWJITPHXHSPAKJKWKDO3SR5.json","graph_json":"https://pith.science/api/pith-number/QYPJWJITPHXHSPAKJKWKDO3SR5/graph.json","events_json":"https://pith.science/api/pith-number/QYPJWJITPHXHSPAKJKWKDO3SR5/events.json","paper":"https://pith.science/paper/QYPJWJIT"},"agent_actions":{"view_html":"https://pith.science/pith/QYPJWJITPHXHSPAKJKWKDO3SR5","download_json":"https://pith.science/pith/QYPJWJITPHXHSPAKJKWKDO3SR5.json","view_paper":"https://pith.science/paper/QYPJWJIT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.03065&json=true","fetch_graph":"https://pith.science/api/pith-number/QYPJWJITPHXHSPAKJKWKDO3SR5/graph.json","fetch_events":"https://pith.science/api/pith-number/QYPJWJITPHXHSPAKJKWKDO3SR5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QYPJWJITPHXHSPAKJKWKDO3SR5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QYPJWJITPHXHSPAKJKWKDO3SR5/action/storage_attestation","attest_author":"https://pith.science/pith/QYPJWJITPHXHSPAKJKWKDO3SR5/action/author_attestation","sign_citation":"https://pith.science/pith/QYPJWJITPHXHSPAKJKWKDO3SR5/action/citation_signature","submit_replication":"https://pith.science/pith/QYPJWJITPHXHSPAKJKWKDO3SR5/action/replication_record"}},"created_at":"2026-07-07T01:16:41.714038+00:00","updated_at":"2026-07-07T01:16:41.714038+00:00"}