{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:DE66YH6RS73TJN3ZIMSS2XXULK","short_pith_number":"pith:DE66YH6R","schema_version":"1.0","canonical_sha256":"193dec1fd197f734b77943252d5ef45a88589f61e9b967df9bae1ba4fcc79350","source":{"kind":"arxiv","id":"2506.13456","version":2},"attestation_state":"computed","paper":{"title":"Block-wise Adaptive Caching for Accelerating Diffusion Policy","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.AI","authors_text":"Hanyun Cui, Jianbo Zhou, Kangye Ji, Lei Chen, Shengjia Hua, Ye Li, Yuan Meng, Zhi Wang","submitted_at":"2025-06-16T13:14:58Z","abstract_excerpt":"Diffusion Policy has demonstrated strong visuomotor modeling capabilities, but its high computational cost renders it impractical for real-time robotic control. Despite huge redundancy across repetitive denoising steps, existing diffusion acceleration techniques fail to generalize to Diffusion Policy due to fundamental architectural and data divergences. In this paper, we propose $\\textbf{B}$lock-wise $\\textbf{A}$daptive $\\textbf{C}$aching ($\\textbf{BAC}$), a method to accelerate Diffusion Policy by caching intermediate action features. BAC achieves lossless action generation acceleration by a"},"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":"2506.13456","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2025-06-16T13:14:58Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"e0706d574fa056add1d613d3d63042c68d1b60384402236501c927486d31e82b","abstract_canon_sha256":"106b88a05144d70144dfc2880aa1b400fbc055e3ae953c885bbcfb27b04e78ad"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:45:13.687815Z","signature_b64":"K+DEMG0WaJWXDbK5xEwbCPfUSrW/c63z6PNSizVfaM8+708s3NH+mq+F+Z8ml4NR8x4xs/kOiBsX/K9M1JsyBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"193dec1fd197f734b77943252d5ef45a88589f61e9b967df9bae1ba4fcc79350","last_reissued_at":"2026-05-18T02:45:13.687143Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:45:13.687143Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Block-wise Adaptive Caching for Accelerating Diffusion Policy","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.AI","authors_text":"Hanyun Cui, Jianbo Zhou, Kangye Ji, Lei Chen, Shengjia Hua, Ye Li, Yuan Meng, Zhi Wang","submitted_at":"2025-06-16T13:14:58Z","abstract_excerpt":"Diffusion Policy has demonstrated strong visuomotor modeling capabilities, but its high computational cost renders it impractical for real-time robotic control. Despite huge redundancy across repetitive denoising steps, existing diffusion acceleration techniques fail to generalize to Diffusion Policy due to fundamental architectural and data divergences. In this paper, we propose $\\textbf{B}$lock-wise $\\textbf{A}$daptive $\\textbf{C}$aching ($\\textbf{BAC}$), a method to accelerate Diffusion Policy by caching intermediate action features. BAC achieves lossless action generation acceleration by a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.13456","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"2506.13456","created_at":"2026-05-18T02:45:13.687246+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.13456v2","created_at":"2026-05-18T02:45:13.687246+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.13456","created_at":"2026-05-18T02:45:13.687246+00:00"},{"alias_kind":"pith_short_12","alias_value":"DE66YH6RS73T","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_16","alias_value":"DE66YH6RS73TJN3Z","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_8","alias_value":"DE66YH6R","created_at":"2026-05-18T12:33:37.589309+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":4,"sample":[{"citing_arxiv_id":"2601.12894","citing_title":"Sparse ActionGen: Accelerating Diffusion Policy with Real-time Pruning","ref_index":7,"is_internal_anchor":true},{"citing_arxiv_id":"2508.13073","citing_title":"Large VLM-based Vision-Language-Action Models for Robotic Manipulation: A Survey","ref_index":193,"is_internal_anchor":true},{"citing_arxiv_id":"2605.13316","citing_title":"Test-time Sparsity for Extreme Fast Action Diffusion","ref_index":9,"is_internal_anchor":true},{"citing_arxiv_id":"2604.24447","citing_title":"Characterizing Vision-Language-Action Models across XPUs: Constraints and Acceleration for On-Robot Deployment","ref_index":8,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DE66YH6RS73TJN3ZIMSS2XXULK","json":"https://pith.science/pith/DE66YH6RS73TJN3ZIMSS2XXULK.json","graph_json":"https://pith.science/api/pith-number/DE66YH6RS73TJN3ZIMSS2XXULK/graph.json","events_json":"https://pith.science/api/pith-number/DE66YH6RS73TJN3ZIMSS2XXULK/events.json","paper":"https://pith.science/paper/DE66YH6R"},"agent_actions":{"view_html":"https://pith.science/pith/DE66YH6RS73TJN3ZIMSS2XXULK","download_json":"https://pith.science/pith/DE66YH6RS73TJN3ZIMSS2XXULK.json","view_paper":"https://pith.science/paper/DE66YH6R","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.13456&json=true","fetch_graph":"https://pith.science/api/pith-number/DE66YH6RS73TJN3ZIMSS2XXULK/graph.json","fetch_events":"https://pith.science/api/pith-number/DE66YH6RS73TJN3ZIMSS2XXULK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DE66YH6RS73TJN3ZIMSS2XXULK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DE66YH6RS73TJN3ZIMSS2XXULK/action/storage_attestation","attest_author":"https://pith.science/pith/DE66YH6RS73TJN3ZIMSS2XXULK/action/author_attestation","sign_citation":"https://pith.science/pith/DE66YH6RS73TJN3ZIMSS2XXULK/action/citation_signature","submit_replication":"https://pith.science/pith/DE66YH6RS73TJN3ZIMSS2XXULK/action/replication_record"}},"created_at":"2026-05-18T02:45:13.687246+00:00","updated_at":"2026-05-18T02:45:13.687246+00:00"}