{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:UEFNPICFZ4O7VBCSDECBRNPJYR","short_pith_number":"pith:UEFNPICF","schema_version":"1.0","canonical_sha256":"a10ad7a045cf1dfa8452190418b5e9c45efd7bf5156ae4cc4dfac2231b9d4b04","source":{"kind":"arxiv","id":"2606.31695","version":1},"attestation_state":"computed","paper":{"title":"Intrinsically Stable Spiking Neural Networks: Overcoming the Performance Barrier in the Absence of Batch Normalization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guanchao Qiao, Jian Bai, Liwei Meng, Ning Ning, Ruichen Ma, Shaogang Hu, Xiaoyang Zhang, Yang Liu","submitted_at":"2026-06-30T14:07:21Z","abstract_excerpt":"The performance of deep spiking neural networks (SNNs) often relies on batch normalization (BN). However, the advanced dynamic BN variants used in state-of-the-art models introduce runtime multiplications, which weaken the hardware-efficiency motivation of SNNs. To address this tension, we identify catastrophic firing-rate decay as a primary cause of severe performance degradation in normalization-free SNNs. Guided by this insight, this work proposes the Intrinsically Stable SNN (IS-SNN) architecture, which removes activation-normalization layers by enforcing signal homeostasis through topolog"},"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":"2606.31695","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-30T14:07:21Z","cross_cats_sorted":[],"title_canon_sha256":"02d8f02b7ee7252f09168dc5edcdde567e860cc5a8591238b344f963e3f8fae5","abstract_canon_sha256":"007fa74ca252e6f6f46a5c308dd08a13ed8d91e7ac3488e29d0c19aae1b3da9c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-01T01:18:12.045548Z","signature_b64":"UAzNkjPztgdcOCFb6ypRtKPmx1/xdHzeYcqtg2cIuj+KO7vksu8UmLUwij1qMYNWLurTwwRrKZtQSg3F4Tq2AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a10ad7a045cf1dfa8452190418b5e9c45efd7bf5156ae4cc4dfac2231b9d4b04","last_reissued_at":"2026-07-01T01:18:12.045051Z","signature_status":"signed_v1","first_computed_at":"2026-07-01T01:18:12.045051Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Intrinsically Stable Spiking Neural Networks: Overcoming the Performance Barrier in the Absence of Batch Normalization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guanchao Qiao, Jian Bai, Liwei Meng, Ning Ning, Ruichen Ma, Shaogang Hu, Xiaoyang Zhang, Yang Liu","submitted_at":"2026-06-30T14:07:21Z","abstract_excerpt":"The performance of deep spiking neural networks (SNNs) often relies on batch normalization (BN). However, the advanced dynamic BN variants used in state-of-the-art models introduce runtime multiplications, which weaken the hardware-efficiency motivation of SNNs. To address this tension, we identify catastrophic firing-rate decay as a primary cause of severe performance degradation in normalization-free SNNs. Guided by this insight, this work proposes the Intrinsically Stable SNN (IS-SNN) architecture, which removes activation-normalization layers by enforcing signal homeostasis through topolog"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.31695","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/2606.31695/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":"2606.31695","created_at":"2026-07-01T01:18:12.045125+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.31695v1","created_at":"2026-07-01T01:18:12.045125+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.31695","created_at":"2026-07-01T01:18:12.045125+00:00"},{"alias_kind":"pith_short_12","alias_value":"UEFNPICFZ4O7","created_at":"2026-07-01T01:18:12.045125+00:00"},{"alias_kind":"pith_short_16","alias_value":"UEFNPICFZ4O7VBCS","created_at":"2026-07-01T01:18:12.045125+00:00"},{"alias_kind":"pith_short_8","alias_value":"UEFNPICF","created_at":"2026-07-01T01:18:12.045125+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/UEFNPICFZ4O7VBCSDECBRNPJYR","json":"https://pith.science/pith/UEFNPICFZ4O7VBCSDECBRNPJYR.json","graph_json":"https://pith.science/api/pith-number/UEFNPICFZ4O7VBCSDECBRNPJYR/graph.json","events_json":"https://pith.science/api/pith-number/UEFNPICFZ4O7VBCSDECBRNPJYR/events.json","paper":"https://pith.science/paper/UEFNPICF"},"agent_actions":{"view_html":"https://pith.science/pith/UEFNPICFZ4O7VBCSDECBRNPJYR","download_json":"https://pith.science/pith/UEFNPICFZ4O7VBCSDECBRNPJYR.json","view_paper":"https://pith.science/paper/UEFNPICF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.31695&json=true","fetch_graph":"https://pith.science/api/pith-number/UEFNPICFZ4O7VBCSDECBRNPJYR/graph.json","fetch_events":"https://pith.science/api/pith-number/UEFNPICFZ4O7VBCSDECBRNPJYR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UEFNPICFZ4O7VBCSDECBRNPJYR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UEFNPICFZ4O7VBCSDECBRNPJYR/action/storage_attestation","attest_author":"https://pith.science/pith/UEFNPICFZ4O7VBCSDECBRNPJYR/action/author_attestation","sign_citation":"https://pith.science/pith/UEFNPICFZ4O7VBCSDECBRNPJYR/action/citation_signature","submit_replication":"https://pith.science/pith/UEFNPICFZ4O7VBCSDECBRNPJYR/action/replication_record"}},"created_at":"2026-07-01T01:18:12.045125+00:00","updated_at":"2026-07-01T01:18:12.045125+00:00"}