{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:FRB6DSGZWRR4UHALCORVR6HT2U","short_pith_number":"pith:FRB6DSGZ","schema_version":"1.0","canonical_sha256":"2c43e1c8d9b463ca1c0b13a358f8f3d5396f183c5962fa8f6b61539855fd6868","source":{"kind":"arxiv","id":"2607.00780","version":1},"attestation_state":"computed","paper":{"title":"SpiralFovea: Input-Adaptive Foveated Tokenization as a Third Lever of Resource-Adaptive Inference","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Kyan Mahajan, Mohammad Saqlain","submitted_at":"2026-07-01T11:09:07Z","abstract_excerpt":"Most adaptive-inference techniques for foundation models change what the model does - early exit, MoE routing, KV-cache compression, dynamic attention sparsity. The input that hits the backbone, however, remains a fixed-grid tokenisation indifferent to image content. We argue that this is a missed lever. We present SpiralFovea, a parameter-free, input-adaptive tokeniser in which token identity, location, scale, and count are all functions of local visual entropy and selection completes before any backbone parameter is queried. Around content-driven hotspot anchors, multi-scale spiral rings pro"},"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":"2607.00780","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-01T11:09:07Z","cross_cats_sorted":[],"title_canon_sha256":"6447352ae4e58c6b17ece21c58fcece6afca1a86cd617938adaa2f5d0d77f667","abstract_canon_sha256":"83361cae6b91bba5fdf07e580b8f2683ebe0210c30f491d4060b82c782bb1be5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-02T01:17:55.210427Z","signature_b64":"sySwGvYMAP6pBahyDcD9g38K9sI1vwwO8ho7M0QtmQVlvxlYy/KtPFjUkBrBLEiiXaI0UxdiuAzq4ORekdb+Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2c43e1c8d9b463ca1c0b13a358f8f3d5396f183c5962fa8f6b61539855fd6868","last_reissued_at":"2026-07-02T01:17:55.210001Z","signature_status":"signed_v1","first_computed_at":"2026-07-02T01:17:55.210001Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SpiralFovea: Input-Adaptive Foveated Tokenization as a Third Lever of Resource-Adaptive Inference","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Kyan Mahajan, Mohammad Saqlain","submitted_at":"2026-07-01T11:09:07Z","abstract_excerpt":"Most adaptive-inference techniques for foundation models change what the model does - early exit, MoE routing, KV-cache compression, dynamic attention sparsity. The input that hits the backbone, however, remains a fixed-grid tokenisation indifferent to image content. We argue that this is a missed lever. We present SpiralFovea, a parameter-free, input-adaptive tokeniser in which token identity, location, scale, and count are all functions of local visual entropy and selection completes before any backbone parameter is queried. Around content-driven hotspot anchors, multi-scale spiral rings pro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.00780","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/2607.00780/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":"2607.00780","created_at":"2026-07-02T01:17:55.210070+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.00780v1","created_at":"2026-07-02T01:17:55.210070+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.00780","created_at":"2026-07-02T01:17:55.210070+00:00"},{"alias_kind":"pith_short_12","alias_value":"FRB6DSGZWRR4","created_at":"2026-07-02T01:17:55.210070+00:00"},{"alias_kind":"pith_short_16","alias_value":"FRB6DSGZWRR4UHAL","created_at":"2026-07-02T01:17:55.210070+00:00"},{"alias_kind":"pith_short_8","alias_value":"FRB6DSGZ","created_at":"2026-07-02T01:17:55.210070+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/FRB6DSGZWRR4UHALCORVR6HT2U","json":"https://pith.science/pith/FRB6DSGZWRR4UHALCORVR6HT2U.json","graph_json":"https://pith.science/api/pith-number/FRB6DSGZWRR4UHALCORVR6HT2U/graph.json","events_json":"https://pith.science/api/pith-number/FRB6DSGZWRR4UHALCORVR6HT2U/events.json","paper":"https://pith.science/paper/FRB6DSGZ"},"agent_actions":{"view_html":"https://pith.science/pith/FRB6DSGZWRR4UHALCORVR6HT2U","download_json":"https://pith.science/pith/FRB6DSGZWRR4UHALCORVR6HT2U.json","view_paper":"https://pith.science/paper/FRB6DSGZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.00780&json=true","fetch_graph":"https://pith.science/api/pith-number/FRB6DSGZWRR4UHALCORVR6HT2U/graph.json","fetch_events":"https://pith.science/api/pith-number/FRB6DSGZWRR4UHALCORVR6HT2U/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FRB6DSGZWRR4UHALCORVR6HT2U/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FRB6DSGZWRR4UHALCORVR6HT2U/action/storage_attestation","attest_author":"https://pith.science/pith/FRB6DSGZWRR4UHALCORVR6HT2U/action/author_attestation","sign_citation":"https://pith.science/pith/FRB6DSGZWRR4UHALCORVR6HT2U/action/citation_signature","submit_replication":"https://pith.science/pith/FRB6DSGZWRR4UHALCORVR6HT2U/action/replication_record"}},"created_at":"2026-07-02T01:17:55.210070+00:00","updated_at":"2026-07-02T01:17:55.210070+00:00"}