{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:A3MBYVKYWC3VJZXOGDVT7Q7R5G","short_pith_number":"pith:A3MBYVKY","schema_version":"1.0","canonical_sha256":"06d81c5558b0b754e6ee30eb3fc3f1e99ea650f6488ef48bc0e3b9a2e9b7558d","source":{"kind":"arxiv","id":"2607.20516","version":1},"attestation_state":"computed","paper":{"title":"Scaling Closed-Loop Feature Channel Configuration with LLMs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Dmitry Ignatov, Radu Timofte, Tolgay Atinc Uzun","submitted_at":"2026-07-07T22:31:53Z","abstract_excerpt":"Promising initial results in closed-loop large-language-model-based channel-configuration search demonstrated that neural-network widths can be optimized directly through executable code generation and accuracy feedback. However, those results were obtained from a relatively sparse set of valid evaluations, leaving open whether the observed optimization behavior transfers to a denser sampling regime and whether additional architectural regularities emerge when more generated networks are evaluated. To test this, the same search setting is scaled to 250 candidate networks per fine-tuning cycle."},"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":"2607.20516","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-07T22:31:53Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"43ba1944f5e9d620e573ec75e3816b0e92f42768dc2cf9c503bbb5ffed97ad75","abstract_canon_sha256":"c7b567daa48bbfecb1e37a621fbbe586b7b8136eea609e208d02365a3d136dfe"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-24T00:23:19.916074Z","signature_b64":"/B6rWPoqCHAh8j6GV/w1nAOfLP5Nx1ssb1VBdLQzxQe77sFOyu10gSBkA33gWBTWHSbJxynzNIisfWNPNiI2BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"06d81c5558b0b754e6ee30eb3fc3f1e99ea650f6488ef48bc0e3b9a2e9b7558d","last_reissued_at":"2026-07-24T00:23:19.915042Z","signature_status":"signed_v1","first_computed_at":"2026-07-24T00:23:19.915042Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Scaling Closed-Loop Feature Channel Configuration with LLMs","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Dmitry Ignatov, Radu Timofte, Tolgay Atinc Uzun","submitted_at":"2026-07-07T22:31:53Z","abstract_excerpt":"Promising initial results in closed-loop large-language-model-based channel-configuration search demonstrated that neural-network widths can be optimized directly through executable code generation and accuracy feedback. However, those results were obtained from a relatively sparse set of valid evaluations, leaving open whether the observed optimization behavior transfers to a denser sampling regime and whether additional architectural regularities emerge when more generated networks are evaluated. To test this, the same search setting is scaled to 250 candidate networks per fine-tuning cycle."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.20516","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/2607.20516/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":"2607.20516","created_at":"2026-07-24T00:23:19.915496+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.20516v1","created_at":"2026-07-24T00:23:19.915496+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.20516","created_at":"2026-07-24T00:23:19.915496+00:00"},{"alias_kind":"pith_short_12","alias_value":"A3MBYVKYWC3V","created_at":"2026-07-24T00:23:19.915496+00:00"},{"alias_kind":"pith_short_16","alias_value":"A3MBYVKYWC3VJZXO","created_at":"2026-07-24T00:23:19.915496+00:00"},{"alias_kind":"pith_short_8","alias_value":"A3MBYVKY","created_at":"2026-07-24T00:23:19.915496+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/A3MBYVKYWC3VJZXOGDVT7Q7R5G","json":"https://pith.science/pith/A3MBYVKYWC3VJZXOGDVT7Q7R5G.json","graph_json":"https://pith.science/api/pith-number/A3MBYVKYWC3VJZXOGDVT7Q7R5G/graph.json","events_json":"https://pith.science/api/pith-number/A3MBYVKYWC3VJZXOGDVT7Q7R5G/events.json","paper":"https://pith.science/paper/A3MBYVKY"},"agent_actions":{"view_html":"https://pith.science/pith/A3MBYVKYWC3VJZXOGDVT7Q7R5G","download_json":"https://pith.science/pith/A3MBYVKYWC3VJZXOGDVT7Q7R5G.json","view_paper":"https://pith.science/paper/A3MBYVKY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.20516&json=true","fetch_graph":"https://pith.science/api/pith-number/A3MBYVKYWC3VJZXOGDVT7Q7R5G/graph.json","fetch_events":"https://pith.science/api/pith-number/A3MBYVKYWC3VJZXOGDVT7Q7R5G/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/A3MBYVKYWC3VJZXOGDVT7Q7R5G/action/timestamp_anchor","attest_storage":"https://pith.science/pith/A3MBYVKYWC3VJZXOGDVT7Q7R5G/action/storage_attestation","attest_author":"https://pith.science/pith/A3MBYVKYWC3VJZXOGDVT7Q7R5G/action/author_attestation","sign_citation":"https://pith.science/pith/A3MBYVKYWC3VJZXOGDVT7Q7R5G/action/citation_signature","submit_replication":"https://pith.science/pith/A3MBYVKYWC3VJZXOGDVT7Q7R5G/action/replication_record"}},"created_at":"2026-07-24T00:23:19.915496+00:00","updated_at":"2026-07-24T00:23:19.915496+00:00"}