{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:SZO3DK57JMPFAFFQXYMNT3IPPT","short_pith_number":"pith:SZO3DK57","canonical_record":{"source":{"id":"2503.11549","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-14T16:12:23Z","cross_cats_sorted":[],"title_canon_sha256":"80baee3ebe67a8ba25817a78dd7079cbd5ad1807db8e9b4c4d00fafdc1e8e2af","abstract_canon_sha256":"7579c45e0907ad90aaa66ff9b81a6c11b7b344fc9acef2c94c9e7ce353b56d56"},"schema_version":"1.0"},"canonical_sha256":"965db1abbf4b1e5014b0be18d9ed0f7cc2a66ec38481af777e3263742271626c","source":{"kind":"arxiv","id":"2503.11549","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.11549","created_at":"2026-07-05T10:31:34Z"},{"alias_kind":"arxiv_version","alias_value":"2503.11549v1","created_at":"2026-07-05T10:31:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.11549","created_at":"2026-07-05T10:31:34Z"},{"alias_kind":"pith_short_12","alias_value":"SZO3DK57JMPF","created_at":"2026-07-05T10:31:34Z"},{"alias_kind":"pith_short_16","alias_value":"SZO3DK57JMPFAFFQ","created_at":"2026-07-05T10:31:34Z"},{"alias_kind":"pith_short_8","alias_value":"SZO3DK57","created_at":"2026-07-05T10:31:34Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:SZO3DK57JMPFAFFQXYMNT3IPPT","target":"record","payload":{"canonical_record":{"source":{"id":"2503.11549","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-14T16:12:23Z","cross_cats_sorted":[],"title_canon_sha256":"80baee3ebe67a8ba25817a78dd7079cbd5ad1807db8e9b4c4d00fafdc1e8e2af","abstract_canon_sha256":"7579c45e0907ad90aaa66ff9b81a6c11b7b344fc9acef2c94c9e7ce353b56d56"},"schema_version":"1.0"},"canonical_sha256":"965db1abbf4b1e5014b0be18d9ed0f7cc2a66ec38481af777e3263742271626c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:31:34.301094Z","signature_b64":"qI23C2OiblxRSjWMJxwkOIo1emtz++CpBLOSGchFmm6lhAqiHBh4hriqP69F9yAuyTXn8Y/RvkLxpeiRk4nXDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"965db1abbf4b1e5014b0be18d9ed0f7cc2a66ec38481af777e3263742271626c","last_reissued_at":"2026-07-05T10:31:34.300564Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:31:34.300564Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2503.11549","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:31:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"o7+05wMR6RM8WGvLN30iLDyJZnsls6pX+eDrhfZm9QmzxZq6a4MKI+P1axlH916IA5PxUbRUXOEjJDFb2S3GBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T04:27:20.670482Z"},"content_sha256":"820eb62c512526fcbe9147f3461af478576d9493bb8d17aaf266dbbc73b84530","schema_version":"1.0","event_id":"sha256:820eb62c512526fcbe9147f3461af478576d9493bb8d17aaf266dbbc73b84530"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:SZO3DK57JMPFAFFQXYMNT3IPPT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Similarity-Aware Token Pruning: Your VLM but Faster","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ahmadreza Jeddi, Babak Taati, Elham Dolatabadi, Negin Baghbanzadeh","submitted_at":"2025-03-14T16:12:23Z","abstract_excerpt":"The computational demands of Vision Transformers (ViTs) and Vision-Language Models (VLMs) remain a significant challenge due to the quadratic complexity of self-attention. While token pruning offers a promising solution, existing methods often introduce training overhead or fail to adapt dynamically across layers. We present SAINT, a training-free token pruning framework that leverages token similarity and a graph-based formulation to dynamically optimize pruning rates and redundancy thresholds. Through systematic analysis, we identify a universal three-stage token evolution process (aligner-e"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.11549","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/2503.11549/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:31:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+H7ZCxST2CuK7j7sNEzzjcWP/8UGJESWel7GQrItBz7l485cgvxowaX1jJlUVW0eGpdApl0jIwm+MxmqfoqHDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T04:27:20.670984Z"},"content_sha256":"f442a2584543cfd3fc74bb0b517c8ea034859d9b6851ee2a937950daa78003d1","schema_version":"1.0","event_id":"sha256:f442a2584543cfd3fc74bb0b517c8ea034859d9b6851ee2a937950daa78003d1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SZO3DK57JMPFAFFQXYMNT3IPPT/bundle.json","state_url":"https://pith.science/pith/SZO3DK57JMPFAFFQXYMNT3IPPT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SZO3DK57JMPFAFFQXYMNT3IPPT/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-08T04:27:20Z","links":{"resolver":"https://pith.science/pith/SZO3DK57JMPFAFFQXYMNT3IPPT","bundle":"https://pith.science/pith/SZO3DK57JMPFAFFQXYMNT3IPPT/bundle.json","state":"https://pith.science/pith/SZO3DK57JMPFAFFQXYMNT3IPPT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SZO3DK57JMPFAFFQXYMNT3IPPT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:SZO3DK57JMPFAFFQXYMNT3IPPT","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":"7579c45e0907ad90aaa66ff9b81a6c11b7b344fc9acef2c94c9e7ce353b56d56","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-14T16:12:23Z","title_canon_sha256":"80baee3ebe67a8ba25817a78dd7079cbd5ad1807db8e9b4c4d00fafdc1e8e2af"},"schema_version":"1.0","source":{"id":"2503.11549","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.11549","created_at":"2026-07-05T10:31:34Z"},{"alias_kind":"arxiv_version","alias_value":"2503.11549v1","created_at":"2026-07-05T10:31:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.11549","created_at":"2026-07-05T10:31:34Z"},{"alias_kind":"pith_short_12","alias_value":"SZO3DK57JMPF","created_at":"2026-07-05T10:31:34Z"},{"alias_kind":"pith_short_16","alias_value":"SZO3DK57JMPFAFFQ","created_at":"2026-07-05T10:31:34Z"},{"alias_kind":"pith_short_8","alias_value":"SZO3DK57","created_at":"2026-07-05T10:31:34Z"}],"graph_snapshots":[{"event_id":"sha256:f442a2584543cfd3fc74bb0b517c8ea034859d9b6851ee2a937950daa78003d1","target":"graph","created_at":"2026-07-05T10:31:34Z","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/2503.11549/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The computational demands of Vision Transformers (ViTs) and Vision-Language Models (VLMs) remain a significant challenge due to the quadratic complexity of self-attention. While token pruning offers a promising solution, existing methods often introduce training overhead or fail to adapt dynamically across layers. We present SAINT, a training-free token pruning framework that leverages token similarity and a graph-based formulation to dynamically optimize pruning rates and redundancy thresholds. Through systematic analysis, we identify a universal three-stage token evolution process (aligner-e","authors_text":"Ahmadreza Jeddi, Babak Taati, Elham Dolatabadi, Negin Baghbanzadeh","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-14T16:12:23Z","title":"Similarity-Aware Token Pruning: Your VLM but Faster"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.11549","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":"sha256:820eb62c512526fcbe9147f3461af478576d9493bb8d17aaf266dbbc73b84530","target":"record","created_at":"2026-07-05T10:31:34Z","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":"7579c45e0907ad90aaa66ff9b81a6c11b7b344fc9acef2c94c9e7ce353b56d56","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-03-14T16:12:23Z","title_canon_sha256":"80baee3ebe67a8ba25817a78dd7079cbd5ad1807db8e9b4c4d00fafdc1e8e2af"},"schema_version":"1.0","source":{"id":"2503.11549","kind":"arxiv","version":1}},"canonical_sha256":"965db1abbf4b1e5014b0be18d9ed0f7cc2a66ec38481af777e3263742271626c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"965db1abbf4b1e5014b0be18d9ed0f7cc2a66ec38481af777e3263742271626c","first_computed_at":"2026-07-05T10:31:34.300564Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:31:34.300564Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"qI23C2OiblxRSjWMJxwkOIo1emtz++CpBLOSGchFmm6lhAqiHBh4hriqP69F9yAuyTXn8Y/RvkLxpeiRk4nXDg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:31:34.301094Z","signed_message":"canonical_sha256_bytes"},"source_id":"2503.11549","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:820eb62c512526fcbe9147f3461af478576d9493bb8d17aaf266dbbc73b84530","sha256:f442a2584543cfd3fc74bb0b517c8ea034859d9b6851ee2a937950daa78003d1"],"state_sha256":"54ded25208907bac0e2bfaec0c59fd94f49a24342e4d8bf5ffb7bedfd82d4e1b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6S4TxtvSdcQk/BciLfcJ+YmRksfKsocWXUlBz7JLoT8oKch0B3zlsdlpgIdxsY3Dycv4WvIFCSbn1rzUI7eXAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T04:27:20.677274Z","bundle_sha256":"7b5044ea946f1b871616cf2852fc66437441eacbf91d929b67e65ffc5997f565"}}