{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:KR6FZ5LIJMZYXEQV35D6VLFUXQ","short_pith_number":"pith:KR6FZ5LI","canonical_record":{"source":{"id":"2607.24148","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2026-07-27T08:29:05Z","cross_cats_sorted":[],"title_canon_sha256":"7322649e989b56a9efa4205e3e97f7fac8df3d2bf67dc9ec735bae83debf1267","abstract_canon_sha256":"f14997ebf0461d5f974ba1fc63b39b4e9a56cb6eb8959eb504eee64f1f051436"},"schema_version":"1.0"},"canonical_sha256":"547c5cf5684b338b9215df47eaacb4bc3d112bc2511dfacc82250e55839b129f","source":{"kind":"arxiv","id":"2607.24148","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.24148","created_at":"2026-07-28T01:23:47Z"},{"alias_kind":"arxiv_version","alias_value":"2607.24148v1","created_at":"2026-07-28T01:23:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.24148","created_at":"2026-07-28T01:23:47Z"},{"alias_kind":"pith_short_12","alias_value":"KR6FZ5LIJMZY","created_at":"2026-07-28T01:23:47Z"},{"alias_kind":"pith_short_16","alias_value":"KR6FZ5LIJMZYXEQV","created_at":"2026-07-28T01:23:47Z"},{"alias_kind":"pith_short_8","alias_value":"KR6FZ5LI","created_at":"2026-07-28T01:23:47Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:KR6FZ5LIJMZYXEQV35D6VLFUXQ","target":"record","payload":{"canonical_record":{"source":{"id":"2607.24148","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2026-07-27T08:29:05Z","cross_cats_sorted":[],"title_canon_sha256":"7322649e989b56a9efa4205e3e97f7fac8df3d2bf67dc9ec735bae83debf1267","abstract_canon_sha256":"f14997ebf0461d5f974ba1fc63b39b4e9a56cb6eb8959eb504eee64f1f051436"},"schema_version":"1.0"},"canonical_sha256":"547c5cf5684b338b9215df47eaacb4bc3d112bc2511dfacc82250e55839b129f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-28T01:23:47.232271Z","signature_b64":"AcEAM7qxIA//7Pvv28LloYMaSTTfR0pkvno9avDKqPmI10IkXoDZ5XmtsuydfsmVs71bfz4BWTpcKGZkNF3wCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"547c5cf5684b338b9215df47eaacb4bc3d112bc2511dfacc82250e55839b129f","last_reissued_at":"2026-07-28T01:23:47.231496Z","signature_status":"signed_v1","first_computed_at":"2026-07-28T01:23:47.231496Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2607.24148","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":"2026-07-28T01:23:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6iXDIH4jOydwDr9R3PN5KjMIUFrcm2YUESdUslx2hESyQA3E3r/KPF0YPWKPCbBg88Flc+F7C5bmfXp/9hrOAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T12:43:44.923449Z"},"content_sha256":"7452aff26ba914d81e0091260ee27f6d955a114f9ebef934cbbff5dac35fdebb","schema_version":"1.0","event_id":"sha256:7452aff26ba914d81e0091260ee27f6d955a114f9ebef934cbbff5dac35fdebb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:KR6FZ5LIJMZYXEQV35D6VLFUXQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Motion-Aware Vector Quantization Framework with Centroid Reuse for Efficient VLA Inference","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Chunyu Qi, Gang Li, Haibing Guan, Haozhe Jiang, Minnan Pei, Xiaoyao Liang, Zhuoran Song","submitted_at":"2026-07-27T08:29:05Z","abstract_excerpt":"Vision-Language-Action (VLA) models have demonstrated strong potential for embodied AI, yet their high inference latency on GPUs limits real-time deployment. Existing accelerators, such as Dadu-Corki, improve efficiency but treat VLA models as full-precision workloads, leaving substantial redundancy in both memory and computation underexploited.\n  In this paper, we propose VQVLA, an algorithm-hardware co-design framework that accelerates VLA inference by exploiting weight similarity and execution dynamics. We first introduce MotionVQ, a motion-aware vector quantization scheme that dynamically "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.24148","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/2607.24148/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":"2026-07-28T01:23:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GioQSNkAds1HSJlYhyGdnNQJWf/wDDHSnZIi26z99ldYXLRkkihF+Ru722h5FNkx/738ZTAhQvxD30SJqocHBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T12:43:44.923955Z"},"content_sha256":"df9220191ce3ebaeba725d1632c2534d1a0bcf8824f92745f91c8b538286af23","schema_version":"1.0","event_id":"sha256:df9220191ce3ebaeba725d1632c2534d1a0bcf8824f92745f91c8b538286af23"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KR6FZ5LIJMZYXEQV35D6VLFUXQ/bundle.json","state_url":"https://pith.science/pith/KR6FZ5LIJMZYXEQV35D6VLFUXQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KR6FZ5LIJMZYXEQV35D6VLFUXQ/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":"2026-08-04T12:43:44Z","links":{"resolver":"https://pith.science/pith/KR6FZ5LIJMZYXEQV35D6VLFUXQ","bundle":"https://pith.science/pith/KR6FZ5LIJMZYXEQV35D6VLFUXQ/bundle.json","state":"https://pith.science/pith/KR6FZ5LIJMZYXEQV35D6VLFUXQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KR6FZ5LIJMZYXEQV35D6VLFUXQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:KR6FZ5LIJMZYXEQV35D6VLFUXQ","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":"f14997ebf0461d5f974ba1fc63b39b4e9a56cb6eb8959eb504eee64f1f051436","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2026-07-27T08:29:05Z","title_canon_sha256":"7322649e989b56a9efa4205e3e97f7fac8df3d2bf67dc9ec735bae83debf1267"},"schema_version":"1.0","source":{"id":"2607.24148","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.24148","created_at":"2026-07-28T01:23:47Z"},{"alias_kind":"arxiv_version","alias_value":"2607.24148v1","created_at":"2026-07-28T01:23:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.24148","created_at":"2026-07-28T01:23:47Z"},{"alias_kind":"pith_short_12","alias_value":"KR6FZ5LIJMZY","created_at":"2026-07-28T01:23:47Z"},{"alias_kind":"pith_short_16","alias_value":"KR6FZ5LIJMZYXEQV","created_at":"2026-07-28T01:23:47Z"},{"alias_kind":"pith_short_8","alias_value":"KR6FZ5LI","created_at":"2026-07-28T01:23:47Z"}],"graph_snapshots":[{"event_id":"sha256:df9220191ce3ebaeba725d1632c2534d1a0bcf8824f92745f91c8b538286af23","target":"graph","created_at":"2026-07-28T01:23:47Z","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/2607.24148/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Vision-Language-Action (VLA) models have demonstrated strong potential for embodied AI, yet their high inference latency on GPUs limits real-time deployment. Existing accelerators, such as Dadu-Corki, improve efficiency but treat VLA models as full-precision workloads, leaving substantial redundancy in both memory and computation underexploited.\n  In this paper, we propose VQVLA, an algorithm-hardware co-design framework that accelerates VLA inference by exploiting weight similarity and execution dynamics. We first introduce MotionVQ, a motion-aware vector quantization scheme that dynamically ","authors_text":"Chunyu Qi, Gang Li, Haibing Guan, Haozhe Jiang, Minnan Pei, Xiaoyao Liang, Zhuoran Song","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2026-07-27T08:29:05Z","title":"A Motion-Aware Vector Quantization Framework with Centroid Reuse for Efficient VLA Inference"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.24148","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":"sha256:7452aff26ba914d81e0091260ee27f6d955a114f9ebef934cbbff5dac35fdebb","target":"record","created_at":"2026-07-28T01:23:47Z","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":"f14997ebf0461d5f974ba1fc63b39b4e9a56cb6eb8959eb504eee64f1f051436","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2026-07-27T08:29:05Z","title_canon_sha256":"7322649e989b56a9efa4205e3e97f7fac8df3d2bf67dc9ec735bae83debf1267"},"schema_version":"1.0","source":{"id":"2607.24148","kind":"arxiv","version":1}},"canonical_sha256":"547c5cf5684b338b9215df47eaacb4bc3d112bc2511dfacc82250e55839b129f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"547c5cf5684b338b9215df47eaacb4bc3d112bc2511dfacc82250e55839b129f","first_computed_at":"2026-07-28T01:23:47.231496Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-28T01:23:47.231496Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"AcEAM7qxIA//7Pvv28LloYMaSTTfR0pkvno9avDKqPmI10IkXoDZ5XmtsuydfsmVs71bfz4BWTpcKGZkNF3wCw==","signature_status":"signed_v1","signed_at":"2026-07-28T01:23:47.232271Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.24148","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7452aff26ba914d81e0091260ee27f6d955a114f9ebef934cbbff5dac35fdebb","sha256:df9220191ce3ebaeba725d1632c2534d1a0bcf8824f92745f91c8b538286af23"],"state_sha256":"0ba2c9c71493547d94e4794f741d59b5ec637da74bd9156a8db8e50b4c57ebf3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1sMDQnFn+BuPqPzfRyCljgRsymgn5+REg+lS2NAm9Vjlrp2BFyKkGtDHZRCGJCoaSJU5YpaKXnZc2IUmFOXaDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T12:43:44.928089Z","bundle_sha256":"f352155dceab0ce17e207380e6b26b3922706b68c8a923f6beebc01c907d06d4"}}