{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:SKPLKAAGTU2O524V5X5GEY3WS2","short_pith_number":"pith:SKPLKAAG","canonical_record":{"source":{"id":"2605.07308","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-05-08T06:17:08Z","cross_cats_sorted":[],"title_canon_sha256":"4c0a7af4a6b801e899dc73f734099c2c5795ca0e0cc3833d736bd5c098af3882","abstract_canon_sha256":"e084975e0f6cd50fb850c2353143d5b9ff2efb8ebac8c52aa9d7f84e06de943b"},"schema_version":"1.0"},"canonical_sha256":"929eb500069d34eeeb95edfa626376969faeec916fa347c1c49f666036c943b5","source":{"kind":"arxiv","id":"2605.07308","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.07308","created_at":"2026-05-20T00:04:34Z"},{"alias_kind":"arxiv_version","alias_value":"2605.07308v2","created_at":"2026-05-20T00:04:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.07308","created_at":"2026-05-20T00:04:34Z"},{"alias_kind":"pith_short_12","alias_value":"SKPLKAAGTU2O","created_at":"2026-05-20T00:04:34Z"},{"alias_kind":"pith_short_16","alias_value":"SKPLKAAGTU2O524V","created_at":"2026-05-20T00:04:34Z"},{"alias_kind":"pith_short_8","alias_value":"SKPLKAAG","created_at":"2026-05-20T00:04:34Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:SKPLKAAGTU2O524V5X5GEY3WS2","target":"record","payload":{"canonical_record":{"source":{"id":"2605.07308","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-05-08T06:17:08Z","cross_cats_sorted":[],"title_canon_sha256":"4c0a7af4a6b801e899dc73f734099c2c5795ca0e0cc3833d736bd5c098af3882","abstract_canon_sha256":"e084975e0f6cd50fb850c2353143d5b9ff2efb8ebac8c52aa9d7f84e06de943b"},"schema_version":"1.0"},"canonical_sha256":"929eb500069d34eeeb95edfa626376969faeec916fa347c1c49f666036c943b5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:04:34.625945Z","signature_b64":"yATIOTowZT34hsijNvA7SU68op2RBjIW40mfUzdGw5jK29+n1uJdMNieoc6eM5P2Riq4iIAud7JWrpFzqBV5AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"929eb500069d34eeeb95edfa626376969faeec916fa347c1c49f666036c943b5","last_reissued_at":"2026-05-20T00:04:34.625108Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:04:34.625108Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.07308","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:04:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"op3aAT20A+KDbs/KVUsbZZ2S/tDVs2NBbNG2NuDoPDdtfFs4zPSKLiUcPEz1f+EdmVKULNFVGg/Fgy6KmQPUAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T23:40:26.987616Z"},"content_sha256":"ec4e33b9057bd379e2fa876bf5ecbaa916e35ea8e83730c242dbe35031437d42","schema_version":"1.0","event_id":"sha256:ec4e33b9057bd379e2fa876bf5ecbaa916e35ea8e83730c242dbe35031437d42"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:SKPLKAAGTU2O524V5X5GEY3WS2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"AT-VLA: Adaptive Tactile Injection for Enhanced Feedback Reaction in Vision-Language-Action Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"AT-VLA adds tactile signals to vision-language-action models only when they significantly aid action generation, paired with dual streams for fast responses.","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Guangrui Ren, Hao Dong, Hongwei Fan, Jiadong Xu, Juan Zhu, Muhe Cai, Xiaoqi Li, Yan Shen","submitted_at":"2026-05-08T06:17:08Z","abstract_excerpt":"Vision-Language-Action (VLA) models have significantly advanced the capabilities of robotic agents in executing diverse tasks; however, they still face challenges in contact-rich manipulation scenarios that require precise physical interactions. To address this limitation, recent studies have attempted to incorporate tactile signals during downstream tasks, enabling pretrained VLAs to interpret tactile feedback. Nevertheless, introducing new modalities during finetuning, which are rarely present in the pretrain stage, may disrupt the pretrained capabilities of VLAs. In addition, the inherently"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"AT-VLA introduces a novel Adaptive Tactile Injection mechanism... dynamically determines the appropriate timing and locations for tactile injection... Furthermore, to enable rapid and accurate tactile responses, we propose a Tactile Reaction Dual-Stream mechanism... achieving real-time close-loop responses within 0.04 s. Real-world experiments thoroughly validate the effectiveness of AT-VLA in contact-rich manipulation tasks.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That dynamically injecting tactile signals only when they 'significantly contribute' will avoid disrupting pretrained VLA capabilities and that the dual-stream split preserves necessary integration between vision-language reasoning and tactile control without loss of performance.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"AT-VLA introduces adaptive tactile injection and a dual-stream tactile reaction mechanism to integrate real-time tactile feedback into pretrained VLA models for contact-rich robotic manipulation.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"AT-VLA adds tactile signals to vision-language-action models only when they significantly aid action generation, paired with dual streams for fast responses.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"b7cc8d26f1e72966875c294f9cc2bed8f9ac62bc5987efa9db63a4787ed6f39d"},"source":{"id":"2605.07308","kind":"arxiv","version":2},"verdict":{"id":"ccead1ac-3807-4181-9ab6-b9c53d51c072","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-11T01:20:05.237836Z","strongest_claim":"AT-VLA introduces a novel Adaptive Tactile Injection mechanism... dynamically determines the appropriate timing and locations for tactile injection... Furthermore, to enable rapid and accurate tactile responses, we propose a Tactile Reaction Dual-Stream mechanism... achieving real-time close-loop responses within 0.04 s. Real-world experiments thoroughly validate the effectiveness of AT-VLA in contact-rich manipulation tasks.","one_line_summary":"AT-VLA introduces adaptive tactile injection and a dual-stream tactile reaction mechanism to integrate real-time tactile feedback into pretrained VLA models for contact-rich robotic manipulation.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That dynamically injecting tactile signals only when they 'significantly contribute' will avoid disrupting pretrained VLA capabilities and that the dual-stream split preserves necessary integration between vision-language reasoning and tactile control without loss of performance.","pith_extraction_headline":"AT-VLA adds tactile signals to vision-language-action models only when they significantly aid action generation, paired with dual streams for fast responses."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.07308/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-19T17:01:18.953126Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T11:53:52.922685Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"ad87ddd6877de00ded08cab35d5202dccc06c555e7e4c246e5cb063e0ab5186c"},"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":"ccead1ac-3807-4181-9ab6-b9c53d51c072"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:04:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"rpNOpV39YUdDDjqHvT3wJN7p53j8esO9tl6N75IMFRLNNstBmOGVaMkrxT8/a6qH14eFIBenxxMb1WbTgo5DDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T23:40:26.988488Z"},"content_sha256":"ed01e36094f59c2a9e7ca5d68d4fbb27fc46f3f6d7588bb882ebf784ecbb04d8","schema_version":"1.0","event_id":"sha256:ed01e36094f59c2a9e7ca5d68d4fbb27fc46f3f6d7588bb882ebf784ecbb04d8"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SKPLKAAGTU2O524V5X5GEY3WS2/bundle.json","state_url":"https://pith.science/pith/SKPLKAAGTU2O524V5X5GEY3WS2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SKPLKAAGTU2O524V5X5GEY3WS2/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":"2026-05-25T23:40:26Z","links":{"resolver":"https://pith.science/pith/SKPLKAAGTU2O524V5X5GEY3WS2","bundle":"https://pith.science/pith/SKPLKAAGTU2O524V5X5GEY3WS2/bundle.json","state":"https://pith.science/pith/SKPLKAAGTU2O524V5X5GEY3WS2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SKPLKAAGTU2O524V5X5GEY3WS2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:SKPLKAAGTU2O524V5X5GEY3WS2","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":"e084975e0f6cd50fb850c2353143d5b9ff2efb8ebac8c52aa9d7f84e06de943b","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-05-08T06:17:08Z","title_canon_sha256":"4c0a7af4a6b801e899dc73f734099c2c5795ca0e0cc3833d736bd5c098af3882"},"schema_version":"1.0","source":{"id":"2605.07308","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.07308","created_at":"2026-05-20T00:04:34Z"},{"alias_kind":"arxiv_version","alias_value":"2605.07308v2","created_at":"2026-05-20T00:04:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.07308","created_at":"2026-05-20T00:04:34Z"},{"alias_kind":"pith_short_12","alias_value":"SKPLKAAGTU2O","created_at":"2026-05-20T00:04:34Z"},{"alias_kind":"pith_short_16","alias_value":"SKPLKAAGTU2O524V","created_at":"2026-05-20T00:04:34Z"},{"alias_kind":"pith_short_8","alias_value":"SKPLKAAG","created_at":"2026-05-20T00:04:34Z"}],"graph_snapshots":[{"event_id":"sha256:ed01e36094f59c2a9e7ca5d68d4fbb27fc46f3f6d7588bb882ebf784ecbb04d8","target":"graph","created_at":"2026-05-20T00:04:34Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"AT-VLA introduces a novel Adaptive Tactile Injection mechanism... dynamically determines the appropriate timing and locations for tactile injection... Furthermore, to enable rapid and accurate tactile responses, we propose a Tactile Reaction Dual-Stream mechanism... achieving real-time close-loop responses within 0.04 s. Real-world experiments thoroughly validate the effectiveness of AT-VLA in contact-rich manipulation tasks."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That dynamically injecting tactile signals only when they 'significantly contribute' will avoid disrupting pretrained VLA capabilities and that the dual-stream split preserves necessary integration between vision-language reasoning and tactile control without loss of performance."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"AT-VLA introduces adaptive tactile injection and a dual-stream tactile reaction mechanism to integrate real-time tactile feedback into pretrained VLA models for contact-rich robotic manipulation."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"AT-VLA adds tactile signals to vision-language-action models only when they significantly aid action generation, paired with dual streams for fast responses."}],"snapshot_sha256":"b7cc8d26f1e72966875c294f9cc2bed8f9ac62bc5987efa9db63a4787ed6f39d"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"doi_title_agreement","ran_at":"2026-05-19T17:01:18.953126Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-19T11:53:52.922685Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2605.07308/integrity.json","findings":[],"snapshot_sha256":"ad87ddd6877de00ded08cab35d5202dccc06c555e7e4c246e5cb063e0ab5186c","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Vision-Language-Action (VLA) models have significantly advanced the capabilities of robotic agents in executing diverse tasks; however, they still face challenges in contact-rich manipulation scenarios that require precise physical interactions. To address this limitation, recent studies have attempted to incorporate tactile signals during downstream tasks, enabling pretrained VLAs to interpret tactile feedback. Nevertheless, introducing new modalities during finetuning, which are rarely present in the pretrain stage, may disrupt the pretrained capabilities of VLAs. In addition, the inherently","authors_text":"Guangrui Ren, Hao Dong, Hongwei Fan, Jiadong Xu, Juan Zhu, Muhe Cai, Xiaoqi Li, Yan Shen","cross_cats":[],"headline":"AT-VLA adds tactile signals to vision-language-action models only when they significantly aid action generation, paired with dual streams for fast responses.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-05-08T06:17:08Z","title":"AT-VLA: Adaptive Tactile Injection for Enhanced Feedback Reaction in Vision-Language-Action Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.07308","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-11T01:20:05.237836Z","id":"ccead1ac-3807-4181-9ab6-b9c53d51c072","model_set":{"reader":"grok-4.3"},"one_line_summary":"AT-VLA introduces adaptive tactile injection and a dual-stream tactile reaction mechanism to integrate real-time tactile feedback into pretrained VLA models for contact-rich robotic manipulation.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"AT-VLA adds tactile signals to vision-language-action models only when they significantly aid action generation, paired with dual streams for fast responses.","strongest_claim":"AT-VLA introduces a novel Adaptive Tactile Injection mechanism... dynamically determines the appropriate timing and locations for tactile injection... Furthermore, to enable rapid and accurate tactile responses, we propose a Tactile Reaction Dual-Stream mechanism... achieving real-time close-loop responses within 0.04 s. Real-world experiments thoroughly validate the effectiveness of AT-VLA in contact-rich manipulation tasks.","weakest_assumption":"That dynamically injecting tactile signals only when they 'significantly contribute' will avoid disrupting pretrained VLA capabilities and that the dual-stream split preserves necessary integration between vision-language reasoning and tactile control without loss of performance."}},"verdict_id":"ccead1ac-3807-4181-9ab6-b9c53d51c072"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:ec4e33b9057bd379e2fa876bf5ecbaa916e35ea8e83730c242dbe35031437d42","target":"record","created_at":"2026-05-20T00:04:34Z","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":"e084975e0f6cd50fb850c2353143d5b9ff2efb8ebac8c52aa9d7f84e06de943b","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.RO","submitted_at":"2026-05-08T06:17:08Z","title_canon_sha256":"4c0a7af4a6b801e899dc73f734099c2c5795ca0e0cc3833d736bd5c098af3882"},"schema_version":"1.0","source":{"id":"2605.07308","kind":"arxiv","version":2}},"canonical_sha256":"929eb500069d34eeeb95edfa626376969faeec916fa347c1c49f666036c943b5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"929eb500069d34eeeb95edfa626376969faeec916fa347c1c49f666036c943b5","first_computed_at":"2026-05-20T00:04:34.625108Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:04:34.625108Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"yATIOTowZT34hsijNvA7SU68op2RBjIW40mfUzdGw5jK29+n1uJdMNieoc6eM5P2Riq4iIAud7JWrpFzqBV5AQ==","signature_status":"signed_v1","signed_at":"2026-05-20T00:04:34.625945Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.07308","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ec4e33b9057bd379e2fa876bf5ecbaa916e35ea8e83730c242dbe35031437d42","sha256:ed01e36094f59c2a9e7ca5d68d4fbb27fc46f3f6d7588bb882ebf784ecbb04d8"],"state_sha256":"f5b2ba3d96c2af7c2d9815d6ab99d1da6298ce5474ff47488378139adb7b2567"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EhxJ7kQUgUa/KudIfqCkz23IHy+fGyUTcJqlAkP42bvnnk34uism7MQOxS+anIFfoPqFe4vNxWC2pz3eGu0PDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T23:40:26.991457Z","bundle_sha256":"b474aa11e616fe55738ce98c961dc40eb77c48f652f82830ca5de9d59f6349f9"}}