{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:HETK53KG74WOH5TA7LT36CW3IL","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":"67159ccb1fb010c57813f6dc773c6c70e05a345283529acdb09ee697f333864f","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2026-06-28T19:37:32Z","title_canon_sha256":"fc705d0fa74db241a3c0310cbc4ae7be9e26a57a784a6a7738977f805b12c27a"},"schema_version":"1.0","source":{"id":"2606.29575","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.29575","created_at":"2026-06-30T01:18:12Z"},{"alias_kind":"arxiv_version","alias_value":"2606.29575v1","created_at":"2026-06-30T01:18:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.29575","created_at":"2026-06-30T01:18:12Z"},{"alias_kind":"pith_short_12","alias_value":"HETK53KG74WO","created_at":"2026-06-30T01:18:12Z"},{"alias_kind":"pith_short_16","alias_value":"HETK53KG74WOH5TA","created_at":"2026-06-30T01:18:12Z"},{"alias_kind":"pith_short_8","alias_value":"HETK53KG","created_at":"2026-06-30T01:18:12Z"}],"graph_snapshots":[{"event_id":"sha256:2c9cb71b864792082e4f656b5c67f6be572f5249808c3459131ff186f82641f6","target":"graph","created_at":"2026-06-30T01:18:12Z","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/2606.29575/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent advances in speech separation (SS) have led to compact front-end models with small parameter sizes, yet their high computational cost remains a major barrier for deployment on edge devices. To address this, we propose TF-MoE, a sparse Mixture-of-Experts (MoE) framework that enhances model capacity with almost no increase in inference cost. Our method introduces dynamic expert specialization in time and frequency dimensions through alternating time-wise and frequency-wise MoE modules, each dynamically selecting experts per frame or mel band. Built upon a mel-band-splitting Conformer back","authors_text":"Chenda Li, Qinzhe Hu, Shujie Liu, Wangyou Zhang, Yan Lu, Yanmin Qian","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2026-06-28T19:37:32Z","title":"TF-MoE: Time-Frequency Mixture-of-Experts for Efficient Speech Separation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.29575","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":"sha256:0fd2296f1c632ec57cc1507752ad6f70bcb18fa28a71824951799cbf0714c0d8","target":"record","created_at":"2026-06-30T01:18:12Z","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":"67159ccb1fb010c57813f6dc773c6c70e05a345283529acdb09ee697f333864f","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.SD","submitted_at":"2026-06-28T19:37:32Z","title_canon_sha256":"fc705d0fa74db241a3c0310cbc4ae7be9e26a57a784a6a7738977f805b12c27a"},"schema_version":"1.0","source":{"id":"2606.29575","kind":"arxiv","version":1}},"canonical_sha256":"3926aeed46ff2ce3f660fae7bf0adb42eade1295e9381b6f3b96ab3815fd975d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"3926aeed46ff2ce3f660fae7bf0adb42eade1295e9381b6f3b96ab3815fd975d","first_computed_at":"2026-06-30T01:18:12.712171Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-30T01:18:12.712171Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"pMhfN1SPEtG8BAZnJU/8FEgVdzJnYzCDOAWWzQV115qSd+XR9byIQ3krHxVc3EZEUjyjizLNrscKi5bQkmQnDg==","signature_status":"signed_v1","signed_at":"2026-06-30T01:18:12.712655Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.29575","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0fd2296f1c632ec57cc1507752ad6f70bcb18fa28a71824951799cbf0714c0d8","sha256:2c9cb71b864792082e4f656b5c67f6be572f5249808c3459131ff186f82641f6"],"state_sha256":"b8195d3e4f6b6a3a2badf2651ed0bc7bffb8415a92e7082ed10e5fd5ece0ed2b"}