{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:WASO67LIOIPE574OVZNRNG5KCL","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":"4d88ae9dbaaaeed0c82ba6396e138374632aa3f98d619e78f4ef93654bfd55a3","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-02-20T12:31:31Z","title_canon_sha256":"3826f69fa28288d54cdff4d0ff1f09518affc30d3aaebd707f24a950423e18f6"},"schema_version":"1.0","source":{"id":"2502.14504","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.14504","created_at":"2026-07-05T10:17:27Z"},{"alias_kind":"arxiv_version","alias_value":"2502.14504v1","created_at":"2026-07-05T10:17:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.14504","created_at":"2026-07-05T10:17:27Z"},{"alias_kind":"pith_short_12","alias_value":"WASO67LIOIPE","created_at":"2026-07-05T10:17:27Z"},{"alias_kind":"pith_short_16","alias_value":"WASO67LIOIPE574O","created_at":"2026-07-05T10:17:27Z"},{"alias_kind":"pith_short_8","alias_value":"WASO67LI","created_at":"2026-07-05T10:17:27Z"}],"graph_snapshots":[{"event_id":"sha256:2e91c6fd510c0c3bfc16dfd95605153c6dd44f20377dc0c98a1fe8c0f05c9a1c","target":"graph","created_at":"2026-07-05T10:17:27Z","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/2502.14504/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities across a range of multimodal tasks. However, their inference efficiency is constrained by the large number of visual tokens processed during decoding. To address this challenge, we propose Per-Layer Per-Head Vision Token Pruning (PLPHP), a two-level fine-grained pruning method including Layer-Level Retention Rate Allocation and Head-Level Vision Token Pruning. Motivated by the Vision Token Re-attention phenomenon across decoder layers, we dynamically adjust token retention rates layer by layer. Layers that exhibit s","authors_text":"Chen Gao, Chenran Huang, Kaiyuan Li, Xiaoping Zhang, Xinlei Chen, Yong Li, Yu Meng","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-02-20T12:31:31Z","title":"PLPHP: Per-Layer Per-Head Vision Token Pruning for Efficient Large Vision-Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.14504","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":"sha256:8ff6acfeaccf4a28dfb139d1e6a6e6592d8256971af38d56df1d80e13f297c1e","target":"record","created_at":"2026-07-05T10:17:27Z","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":"4d88ae9dbaaaeed0c82ba6396e138374632aa3f98d619e78f4ef93654bfd55a3","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-02-20T12:31:31Z","title_canon_sha256":"3826f69fa28288d54cdff4d0ff1f09518affc30d3aaebd707f24a950423e18f6"},"schema_version":"1.0","source":{"id":"2502.14504","kind":"arxiv","version":1}},"canonical_sha256":"b024ef7d68721e4eff8eae5b169baa12f8864ba151da05b511ce68ba91ec7810","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b024ef7d68721e4eff8eae5b169baa12f8864ba151da05b511ce68ba91ec7810","first_computed_at":"2026-07-05T10:17:27.697265Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:17:27.697265Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"aEEPU9Z8bnt7er2AcgZjq2+KJ7MSRCYdGNM6Zzx3MumGzFBCR9ebZKk47VG8O6ba6N2+PekFkY9rGVvl6qnXBg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:17:27.697707Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.14504","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8ff6acfeaccf4a28dfb139d1e6a6e6592d8256971af38d56df1d80e13f297c1e","sha256:2e91c6fd510c0c3bfc16dfd95605153c6dd44f20377dc0c98a1fe8c0f05c9a1c"],"state_sha256":"27213b2470eeaf3d095f510e6fc2e3c29f53806fc75589c0d51ac4f34d73605c"}