{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:B7OW6EHNEBZK3H6PAE4CG2KVPI","short_pith_number":"pith:B7OW6EHN","canonical_record":{"source":{"id":"2605.28051","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-27T06:52:08Z","cross_cats_sorted":[],"title_canon_sha256":"49376fd0e0650e5effbc4ec7686e3421eb50f2d31bb4dc2639874d1455771a7a","abstract_canon_sha256":"f0aafc21c77379bea8edbd0d9d0c53946f6b2eb8ebb4a77952f903bde862d39a"},"schema_version":"1.0"},"canonical_sha256":"0fdd6f10ed2072ad9fcf01382369557a0e2ccd1c43644112bcc218085f5000c0","source":{"kind":"arxiv","id":"2605.28051","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.28051","created_at":"2026-05-28T01:04:57Z"},{"alias_kind":"arxiv_version","alias_value":"2605.28051v1","created_at":"2026-05-28T01:04:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.28051","created_at":"2026-05-28T01:04:57Z"},{"alias_kind":"pith_short_12","alias_value":"B7OW6EHNEBZK","created_at":"2026-05-28T01:04:57Z"},{"alias_kind":"pith_short_16","alias_value":"B7OW6EHNEBZK3H6P","created_at":"2026-05-28T01:04:57Z"},{"alias_kind":"pith_short_8","alias_value":"B7OW6EHN","created_at":"2026-05-28T01:04:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:B7OW6EHNEBZK3H6PAE4CG2KVPI","target":"record","payload":{"canonical_record":{"source":{"id":"2605.28051","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-27T06:52:08Z","cross_cats_sorted":[],"title_canon_sha256":"49376fd0e0650e5effbc4ec7686e3421eb50f2d31bb4dc2639874d1455771a7a","abstract_canon_sha256":"f0aafc21c77379bea8edbd0d9d0c53946f6b2eb8ebb4a77952f903bde862d39a"},"schema_version":"1.0"},"canonical_sha256":"0fdd6f10ed2072ad9fcf01382369557a0e2ccd1c43644112bcc218085f5000c0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-28T01:04:57.130775Z","signature_b64":"s8vzOhQjJb+SiLIyYH9vwlzRBwEMQPkg42A26WiGZQKFmAA2gchG4ZCYhwmPyP5XI246Jv2sFStVJ/ZqqExgDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0fdd6f10ed2072ad9fcf01382369557a0e2ccd1c43644112bcc218085f5000c0","last_reissued_at":"2026-05-28T01:04:57.130344Z","signature_status":"signed_v1","first_computed_at":"2026-05-28T01:04:57.130344Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.28051","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":"2026-05-28T01:04:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CeNodEJTd8k4IUXCnIfqvVK+ke0Z/adNfUItsLMSdlPFsDkE/5KnT5xm+IC0HR2KTCW3TfHRTbOiX7fp+GkMAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T21:23:12.925794Z"},"content_sha256":"6f2ade314aca62d81ca91e489957a98ac20d11072a4544ce9d695d4a3e57f4ca","schema_version":"1.0","event_id":"sha256:6f2ade314aca62d81ca91e489957a98ac20d11072a4544ce9d695d4a3e57f4ca"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:B7OW6EHNEBZK3H6PAE4CG2KVPI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Beyond Surrogate Gradients: Fully Differentiable Token Pruning for Vision-Language Models","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Landi He, Lijian Xu, Mingde Yao, Shawn Young","submitted_at":"2026-05-27T06:52:08Z","abstract_excerpt":"Visual token pruning reduces the computational cost of Vision-Language Models (VLMs) by removing redundant visual tokens. Existing methods typically rely on Gumbel-Softmax to approximate discrete selection during training. However, the optimization is driven by surrogate gradients rather than the true selection process, leading to unreliable learning of token importance. In this paper, we propose DiffPrune, which reformulates pruning as continuous control of token information instead of discrete selection learning. Specifically, we introduce an Information Throttler that modulates each token u"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.28051","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/2605.28051/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":"2026-05-28T01:04:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7TnbPMgTIjt0hQKLZIBNbVYlX+KVx9jPDOGbM/KKgxEaiJhtIjt+fhoCQKxFAgZqRIiFIujgD2DPCxlND7anBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-10T21:23:12.926592Z"},"content_sha256":"246a4175b917685836ebe29545cbe13ff5ecc3d5e418216efb32381d6daef748","schema_version":"1.0","event_id":"sha256:246a4175b917685836ebe29545cbe13ff5ecc3d5e418216efb32381d6daef748"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/B7OW6EHNEBZK3H6PAE4CG2KVPI/bundle.json","state_url":"https://pith.science/pith/B7OW6EHNEBZK3H6PAE4CG2KVPI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/B7OW6EHNEBZK3H6PAE4CG2KVPI/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":"2026-06-10T21:23:12Z","links":{"resolver":"https://pith.science/pith/B7OW6EHNEBZK3H6PAE4CG2KVPI","bundle":"https://pith.science/pith/B7OW6EHNEBZK3H6PAE4CG2KVPI/bundle.json","state":"https://pith.science/pith/B7OW6EHNEBZK3H6PAE4CG2KVPI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/B7OW6EHNEBZK3H6PAE4CG2KVPI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:B7OW6EHNEBZK3H6PAE4CG2KVPI","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":"f0aafc21c77379bea8edbd0d9d0c53946f6b2eb8ebb4a77952f903bde862d39a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-27T06:52:08Z","title_canon_sha256":"49376fd0e0650e5effbc4ec7686e3421eb50f2d31bb4dc2639874d1455771a7a"},"schema_version":"1.0","source":{"id":"2605.28051","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.28051","created_at":"2026-05-28T01:04:57Z"},{"alias_kind":"arxiv_version","alias_value":"2605.28051v1","created_at":"2026-05-28T01:04:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.28051","created_at":"2026-05-28T01:04:57Z"},{"alias_kind":"pith_short_12","alias_value":"B7OW6EHNEBZK","created_at":"2026-05-28T01:04:57Z"},{"alias_kind":"pith_short_16","alias_value":"B7OW6EHNEBZK3H6P","created_at":"2026-05-28T01:04:57Z"},{"alias_kind":"pith_short_8","alias_value":"B7OW6EHN","created_at":"2026-05-28T01:04:57Z"}],"graph_snapshots":[{"event_id":"sha256:246a4175b917685836ebe29545cbe13ff5ecc3d5e418216efb32381d6daef748","target":"graph","created_at":"2026-05-28T01:04:57Z","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/2605.28051/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Visual token pruning reduces the computational cost of Vision-Language Models (VLMs) by removing redundant visual tokens. Existing methods typically rely on Gumbel-Softmax to approximate discrete selection during training. However, the optimization is driven by surrogate gradients rather than the true selection process, leading to unreliable learning of token importance. In this paper, we propose DiffPrune, which reformulates pruning as continuous control of token information instead of discrete selection learning. Specifically, we introduce an Information Throttler that modulates each token u","authors_text":"Landi He, Lijian Xu, Mingde Yao, Shawn Young","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-27T06:52:08Z","title":"Beyond Surrogate Gradients: Fully Differentiable Token Pruning for Vision-Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.28051","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":"sha256:6f2ade314aca62d81ca91e489957a98ac20d11072a4544ce9d695d4a3e57f4ca","target":"record","created_at":"2026-05-28T01:04:57Z","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":"f0aafc21c77379bea8edbd0d9d0c53946f6b2eb8ebb4a77952f903bde862d39a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-27T06:52:08Z","title_canon_sha256":"49376fd0e0650e5effbc4ec7686e3421eb50f2d31bb4dc2639874d1455771a7a"},"schema_version":"1.0","source":{"id":"2605.28051","kind":"arxiv","version":1}},"canonical_sha256":"0fdd6f10ed2072ad9fcf01382369557a0e2ccd1c43644112bcc218085f5000c0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0fdd6f10ed2072ad9fcf01382369557a0e2ccd1c43644112bcc218085f5000c0","first_computed_at":"2026-05-28T01:04:57.130344Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-28T01:04:57.130344Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"s8vzOhQjJb+SiLIyYH9vwlzRBwEMQPkg42A26WiGZQKFmAA2gchG4ZCYhwmPyP5XI246Jv2sFStVJ/ZqqExgDg==","signature_status":"signed_v1","signed_at":"2026-05-28T01:04:57.130775Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.28051","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6f2ade314aca62d81ca91e489957a98ac20d11072a4544ce9d695d4a3e57f4ca","sha256:246a4175b917685836ebe29545cbe13ff5ecc3d5e418216efb32381d6daef748"],"state_sha256":"98f83f4c05d76249ce128ae2b0511495e4b5610e6d26bad0f88cbc83cbe173ac"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cMqaJJAzUNYFn4+XaHxkXoxftGJUhgFtQrsU3Im2/pDsoBVeC8YF2LQDSAW7iF8N6pqa2vxrcWihR14GMAl2Aw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-10T21:23:12.930924Z","bundle_sha256":"5183a8e6b5a6407489560b6628b5b5fbf60dd87848ac0d2ee6f0db767f071d67"}}