{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:AYMIUY3QG45L4KUYVMRCQ6B2T5","short_pith_number":"pith:AYMIUY3Q","schema_version":"1.0","canonical_sha256":"06188a6370373abe2a98ab2228783a9f6f9a7c9430e80772dfc4335fe2057e94","source":{"kind":"arxiv","id":"2608.06434","version":1},"attestation_state":"computed","paper":{"title":"Fast and Accurate: An Adaptive VLA Inference Framework through Environment-aware Model Selection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Guanqing Deng, Guiqin Wang, Jingwen Li, Lang Qin, Qinghai Guo, Shengzeng Huo, Tao Fang, Wenxin Ren, Xiang Wang, Xiaochen Zhang, Xiaowen Dong, Yuewei Sun, Yuxin Ma, Zechuan Tian","submitted_at":"2026-08-06T06:32:27Z","abstract_excerpt":"Embodied intelligence demands both long-horizon reasoning and real-time closed-loop responsiveness. Recent dual-system Vision-Language-Action (VLA) architectures combine fast reactive control with slow deliberative reasoning to balance inference speed and task success rate. However, existing dual-process VLAs tightly couple the fast module to intermediate representations of the slow module, necessitating end-to-end joint training and limiting modularity, extensibility and flexible system switching. In this paper, we propose Environment-aware Model Selection (EMS), an adaptive VLA inference fra"},"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":"2608.06434","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-08-06T06:32:27Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"0ba4e774ad7e574cfbe999182018c7aab0e8da0c617b7120e47c73d3c290e62c","abstract_canon_sha256":"e0bab3fe8d3da1b4bb92b6e6070893e9dbf26713411cced941962dd082461380"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-10T01:10:40.268066Z","signature_b64":"6M7/hFqdAdS9mlvgHycr3xuI38RHYgt9a9gIg3eC1LS5HRIBWGqnjWqgbqdgx+ZrTxiZrmIHx7ZwMQYuubxNAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"06188a6370373abe2a98ab2228783a9f6f9a7c9430e80772dfc4335fe2057e94","last_reissued_at":"2026-08-10T01:10:40.265745Z","signature_status":"signed_v1","first_computed_at":"2026-08-10T01:10:40.265745Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fast and Accurate: An Adaptive VLA Inference Framework through Environment-aware Model Selection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.RO","authors_text":"Guanqing Deng, Guiqin Wang, Jingwen Li, Lang Qin, Qinghai Guo, Shengzeng Huo, Tao Fang, Wenxin Ren, Xiang Wang, Xiaochen Zhang, Xiaowen Dong, Yuewei Sun, Yuxin Ma, Zechuan Tian","submitted_at":"2026-08-06T06:32:27Z","abstract_excerpt":"Embodied intelligence demands both long-horizon reasoning and real-time closed-loop responsiveness. Recent dual-system Vision-Language-Action (VLA) architectures combine fast reactive control with slow deliberative reasoning to balance inference speed and task success rate. However, existing dual-process VLAs tightly couple the fast module to intermediate representations of the slow module, necessitating end-to-end joint training and limiting modularity, extensibility and flexible system switching. In this paper, we propose Environment-aware Model Selection (EMS), an adaptive VLA inference fra"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.06434","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/2608.06434/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2608.06434","created_at":"2026-08-10T01:10:40.266685+00:00"},{"alias_kind":"arxiv_version","alias_value":"2608.06434v1","created_at":"2026-08-10T01:10:40.266685+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.06434","created_at":"2026-08-10T01:10:40.266685+00:00"},{"alias_kind":"pith_short_12","alias_value":"AYMIUY3QG45L","created_at":"2026-08-10T01:10:40.266685+00:00"},{"alias_kind":"pith_short_16","alias_value":"AYMIUY3QG45L4KUY","created_at":"2026-08-10T01:10:40.266685+00:00"},{"alias_kind":"pith_short_8","alias_value":"AYMIUY3Q","created_at":"2026-08-10T01:10:40.266685+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AYMIUY3QG45L4KUYVMRCQ6B2T5","json":"https://pith.science/pith/AYMIUY3QG45L4KUYVMRCQ6B2T5.json","graph_json":"https://pith.science/api/pith-number/AYMIUY3QG45L4KUYVMRCQ6B2T5/graph.json","events_json":"https://pith.science/api/pith-number/AYMIUY3QG45L4KUYVMRCQ6B2T5/events.json","paper":"https://pith.science/paper/AYMIUY3Q"},"agent_actions":{"view_html":"https://pith.science/pith/AYMIUY3QG45L4KUYVMRCQ6B2T5","download_json":"https://pith.science/pith/AYMIUY3QG45L4KUYVMRCQ6B2T5.json","view_paper":"https://pith.science/paper/AYMIUY3Q","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2608.06434&json=true","fetch_graph":"https://pith.science/api/pith-number/AYMIUY3QG45L4KUYVMRCQ6B2T5/graph.json","fetch_events":"https://pith.science/api/pith-number/AYMIUY3QG45L4KUYVMRCQ6B2T5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AYMIUY3QG45L4KUYVMRCQ6B2T5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AYMIUY3QG45L4KUYVMRCQ6B2T5/action/storage_attestation","attest_author":"https://pith.science/pith/AYMIUY3QG45L4KUYVMRCQ6B2T5/action/author_attestation","sign_citation":"https://pith.science/pith/AYMIUY3QG45L4KUYVMRCQ6B2T5/action/citation_signature","submit_replication":"https://pith.science/pith/AYMIUY3QG45L4KUYVMRCQ6B2T5/action/replication_record"}},"created_at":"2026-08-10T01:10:40.266685+00:00","updated_at":"2026-08-10T01:10:40.266685+00:00"}