{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:C5WEWKNEC5LUFPWMHOWJIXE6AK","short_pith_number":"pith:C5WEWKNE","canonical_record":{"source":{"id":"2604.04834","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-04-06T16:35:57Z","cross_cats_sorted":["cs.MM","cs.RO","eess.IV"],"title_canon_sha256":"a172fe623d8b02d0cc8ebec9ee457523924914d082a250c7350a660b243af532","abstract_canon_sha256":"913b2537ba2df974ab24be41387af13d9e3b8e67b330c85ee562f53db9522bdc"},"schema_version":"1.0"},"canonical_sha256":"176c4b29a4175742becc3bac945c9e02b60fc220bdbece9c53ac9c8be0c1bf93","source":{"kind":"arxiv","id":"2604.04834","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.04834","created_at":"2026-07-01T01:17:50Z"},{"alias_kind":"arxiv_version","alias_value":"2604.04834v2","created_at":"2026-07-01T01:17:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.04834","created_at":"2026-07-01T01:17:50Z"},{"alias_kind":"pith_short_12","alias_value":"C5WEWKNEC5LU","created_at":"2026-07-01T01:17:50Z"},{"alias_kind":"pith_short_16","alias_value":"C5WEWKNEC5LUFPWM","created_at":"2026-07-01T01:17:50Z"},{"alias_kind":"pith_short_8","alias_value":"C5WEWKNE","created_at":"2026-07-01T01:17:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:C5WEWKNEC5LUFPWMHOWJIXE6AK","target":"record","payload":{"canonical_record":{"source":{"id":"2604.04834","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-04-06T16:35:57Z","cross_cats_sorted":["cs.MM","cs.RO","eess.IV"],"title_canon_sha256":"a172fe623d8b02d0cc8ebec9ee457523924914d082a250c7350a660b243af532","abstract_canon_sha256":"913b2537ba2df974ab24be41387af13d9e3b8e67b330c85ee562f53db9522bdc"},"schema_version":"1.0"},"canonical_sha256":"176c4b29a4175742becc3bac945c9e02b60fc220bdbece9c53ac9c8be0c1bf93","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-01T01:17:50.121891Z","signature_b64":"j9q4jDDICEXdKzw6cFJ3A8jb71TC1mNFCX+LCgLJrIeue6ayyA/uoLiatrYimY4RECTe/At5f43Sjlj3BdpWCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"176c4b29a4175742becc3bac945c9e02b60fc220bdbece9c53ac9c8be0c1bf93","last_reissued_at":"2026-07-01T01:17:50.121388Z","signature_status":"signed_v1","first_computed_at":"2026-07-01T01:17:50.121388Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2604.04834","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":"2026-07-01T01:17:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"z8w0Uc+gi2S6sXdQ2Abx7ZWqqyDuNiI6xT9BxujJf7H7lKXQuuQvuCh7XmN+toA3hp4yCRMtzU28y04PfpCNAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-02T02:34:22.799471Z"},"content_sha256":"22804eea7ef422af2b3ad2cca5e927cce8710c49a2f4dd9facbde01d91bb546d","schema_version":"1.0","event_id":"sha256:22804eea7ef422af2b3ad2cca5e927cce8710c49a2f4dd9facbde01d91bb546d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:C5WEWKNEC5LUFPWMHOWJIXE6AK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"E-VLA: Event-Augmented Vision-Language-Action Model for Dark and Blurred Scenes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Event-augmented VLA models restore robotic manipulation success in dark and blurred scenes via direct event fusion.","cross_cats":["cs.MM","cs.RO","eess.IV"],"primary_cat":"cs.CV","authors_text":"Hao Shi, Jiajun Zhai, Kailun Yang, Kaiwei Wang, Shangwei Guo","submitted_at":"2026-04-06T16:35:57Z","abstract_excerpt":"Robotic Vision-Language-Action (VLA) models generalize well for open-ended manipulation, but their perception is fragile under sensing-stage degradations such as extreme low light, motion blur, and black clipping. We present E-VLA, an event-augmented VLA framework that improves manipulation robustness when conventional frame-based vision becomes unreliable. Instead of reconstructing images from events, E-VLA directly leverages motion and structural cues in event streams to preserve semantic perception and perception-action consistency under adverse conditions. We build an open-source teleopera"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"even a simple parameter-free fusion, i.e., overlaying accumulated event maps onto RGB images, could substantially improve robustness in dark and blur-heavy scenes: on Pick-Place at 20 lux, success increases from 0% (image-only) to 60% with overlay fusion and to 90% with our event adapter; under severe motion blur (1000 ms exposure), Pick-Place improves from 0% to 20-25%, and Sorting from 5% to 32.5%.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the collected real-world RGB-event-action dataset and the chosen tasks/illumination settings are representative enough for the reported robustness gains to generalize to other robots, tasks, and VLA backbones without substantial retraining or hyperparameter retuning.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"E-VLA integrates event streams directly into VLA models via lightweight fusion, raising Pick-Place success from 0% to 60-90% at 20 lux and from 0% to 20-25% under severe motion blur.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Event-augmented VLA models restore robotic manipulation success in dark and blurred scenes via direct event fusion.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"3d65153ec6afbf62319dbeb006c5f4f070803558fd013aadbc044e31e796e2dc"},"source":{"id":"2604.04834","kind":"arxiv","version":2},"verdict":{"id":"f892352b-f3f6-4ec2-bd84-593ebb0a2bec","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T20:18:16.271874Z","strongest_claim":"even a simple parameter-free fusion, i.e., overlaying accumulated event maps onto RGB images, could substantially improve robustness in dark and blur-heavy scenes: on Pick-Place at 20 lux, success increases from 0% (image-only) to 60% with overlay fusion and to 90% with our event adapter; under severe motion blur (1000 ms exposure), Pick-Place improves from 0% to 20-25%, and Sorting from 5% to 32.5%.","one_line_summary":"E-VLA integrates event streams directly into VLA models via lightweight fusion, raising Pick-Place success from 0% to 60-90% at 20 lux and from 0% to 20-25% under severe motion blur.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the collected real-world RGB-event-action dataset and the chosen tasks/illumination settings are representative enough for the reported robustness gains to generalize to other robots, tasks, and VLA backbones without substantial retraining or hyperparameter retuning.","pith_extraction_headline":"Event-augmented VLA models restore robotic manipulation success in dark and blurred scenes via direct event fusion."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.04834/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":2,"snapshot_sha256":"5eaf2cd823fab9ac8be449304adc1b70a8e444b12286df165c1119bdd010c273"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"f892352b-f3f6-4ec2-bd84-593ebb0a2bec"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-01T01:17:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OwrDgB6MGt9kJxasGmnruuVDU9dzaAubq/442dfIy3fi+2zxzMcwg9BtxrzR/BxC8FBuNboI6uKVzjkDJqblCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-02T02:34:22.800225Z"},"content_sha256":"4beb5efb92b7b0cde43d2270cf8d034fda9498eadcb9a1f2a082ba6236891fe0","schema_version":"1.0","event_id":"sha256:4beb5efb92b7b0cde43d2270cf8d034fda9498eadcb9a1f2a082ba6236891fe0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/C5WEWKNEC5LUFPWMHOWJIXE6AK/bundle.json","state_url":"https://pith.science/pith/C5WEWKNEC5LUFPWMHOWJIXE6AK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/C5WEWKNEC5LUFPWMHOWJIXE6AK/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":"2026-07-02T02:34:22Z","links":{"resolver":"https://pith.science/pith/C5WEWKNEC5LUFPWMHOWJIXE6AK","bundle":"https://pith.science/pith/C5WEWKNEC5LUFPWMHOWJIXE6AK/bundle.json","state":"https://pith.science/pith/C5WEWKNEC5LUFPWMHOWJIXE6AK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/C5WEWKNEC5LUFPWMHOWJIXE6AK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:C5WEWKNEC5LUFPWMHOWJIXE6AK","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":"913b2537ba2df974ab24be41387af13d9e3b8e67b330c85ee562f53db9522bdc","cross_cats_sorted":["cs.MM","cs.RO","eess.IV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-04-06T16:35:57Z","title_canon_sha256":"a172fe623d8b02d0cc8ebec9ee457523924914d082a250c7350a660b243af532"},"schema_version":"1.0","source":{"id":"2604.04834","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.04834","created_at":"2026-07-01T01:17:50Z"},{"alias_kind":"arxiv_version","alias_value":"2604.04834v2","created_at":"2026-07-01T01:17:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.04834","created_at":"2026-07-01T01:17:50Z"},{"alias_kind":"pith_short_12","alias_value":"C5WEWKNEC5LU","created_at":"2026-07-01T01:17:50Z"},{"alias_kind":"pith_short_16","alias_value":"C5WEWKNEC5LUFPWM","created_at":"2026-07-01T01:17:50Z"},{"alias_kind":"pith_short_8","alias_value":"C5WEWKNE","created_at":"2026-07-01T01:17:50Z"}],"graph_snapshots":[{"event_id":"sha256:4beb5efb92b7b0cde43d2270cf8d034fda9498eadcb9a1f2a082ba6236891fe0","target":"graph","created_at":"2026-07-01T01:17:50Z","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":"even a simple parameter-free fusion, i.e., overlaying accumulated event maps onto RGB images, could substantially improve robustness in dark and blur-heavy scenes: on Pick-Place at 20 lux, success increases from 0% (image-only) to 60% with overlay fusion and to 90% with our event adapter; under severe motion blur (1000 ms exposure), Pick-Place improves from 0% to 20-25%, and Sorting from 5% to 32.5%."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That the collected real-world RGB-event-action dataset and the chosen tasks/illumination settings are representative enough for the reported robustness gains to generalize to other robots, tasks, and VLA backbones without substantial retraining or hyperparameter retuning."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"E-VLA integrates event streams directly into VLA models via lightweight fusion, raising Pick-Place success from 0% to 60-90% at 20 lux and from 0% to 20-25% under severe motion blur."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Event-augmented VLA models restore robotic manipulation success in dark and blurred scenes via direct event fusion."}],"snapshot_sha256":"3d65153ec6afbf62319dbeb006c5f4f070803558fd013aadbc044e31e796e2dc"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"5eaf2cd823fab9ac8be449304adc1b70a8e444b12286df165c1119bdd010c273"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2604.04834/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Robotic Vision-Language-Action (VLA) models generalize well for open-ended manipulation, but their perception is fragile under sensing-stage degradations such as extreme low light, motion blur, and black clipping. We present E-VLA, an event-augmented VLA framework that improves manipulation robustness when conventional frame-based vision becomes unreliable. Instead of reconstructing images from events, E-VLA directly leverages motion and structural cues in event streams to preserve semantic perception and perception-action consistency under adverse conditions. We build an open-source teleopera","authors_text":"Hao Shi, Jiajun Zhai, Kailun Yang, Kaiwei Wang, Shangwei Guo","cross_cats":["cs.MM","cs.RO","eess.IV"],"headline":"Event-augmented VLA models restore robotic manipulation success in dark and blurred scenes via direct event fusion.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-04-06T16:35:57Z","title":"E-VLA: Event-Augmented Vision-Language-Action Model for Dark and Blurred Scenes"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2604.04834","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-10T20:18:16.271874Z","id":"f892352b-f3f6-4ec2-bd84-593ebb0a2bec","model_set":{"reader":"grok-4.3"},"one_line_summary":"E-VLA integrates event streams directly into VLA models via lightweight fusion, raising Pick-Place success from 0% to 60-90% at 20 lux and from 0% to 20-25% under severe motion blur.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Event-augmented VLA models restore robotic manipulation success in dark and blurred scenes via direct event fusion.","strongest_claim":"even a simple parameter-free fusion, i.e., overlaying accumulated event maps onto RGB images, could substantially improve robustness in dark and blur-heavy scenes: on Pick-Place at 20 lux, success increases from 0% (image-only) to 60% with overlay fusion and to 90% with our event adapter; under severe motion blur (1000 ms exposure), Pick-Place improves from 0% to 20-25%, and Sorting from 5% to 32.5%.","weakest_assumption":"That the collected real-world RGB-event-action dataset and the chosen tasks/illumination settings are representative enough for the reported robustness gains to generalize to other robots, tasks, and VLA backbones without substantial retraining or hyperparameter retuning."}},"verdict_id":"f892352b-f3f6-4ec2-bd84-593ebb0a2bec"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:22804eea7ef422af2b3ad2cca5e927cce8710c49a2f4dd9facbde01d91bb546d","target":"record","created_at":"2026-07-01T01:17:50Z","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":"913b2537ba2df974ab24be41387af13d9e3b8e67b330c85ee562f53db9522bdc","cross_cats_sorted":["cs.MM","cs.RO","eess.IV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-04-06T16:35:57Z","title_canon_sha256":"a172fe623d8b02d0cc8ebec9ee457523924914d082a250c7350a660b243af532"},"schema_version":"1.0","source":{"id":"2604.04834","kind":"arxiv","version":2}},"canonical_sha256":"176c4b29a4175742becc3bac945c9e02b60fc220bdbece9c53ac9c8be0c1bf93","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"176c4b29a4175742becc3bac945c9e02b60fc220bdbece9c53ac9c8be0c1bf93","first_computed_at":"2026-07-01T01:17:50.121388Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-01T01:17:50.121388Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"j9q4jDDICEXdKzw6cFJ3A8jb71TC1mNFCX+LCgLJrIeue6ayyA/uoLiatrYimY4RECTe/At5f43Sjlj3BdpWCw==","signature_status":"signed_v1","signed_at":"2026-07-01T01:17:50.121891Z","signed_message":"canonical_sha256_bytes"},"source_id":"2604.04834","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:22804eea7ef422af2b3ad2cca5e927cce8710c49a2f4dd9facbde01d91bb546d","sha256:4beb5efb92b7b0cde43d2270cf8d034fda9498eadcb9a1f2a082ba6236891fe0"],"state_sha256":"d3596d9684f6294dbb9ba049e6a8c81a0e56526b492b7cf8d614f99a91af048d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FCk5ujRwHj6/+wBFuWaEI3O4jm85VSci3WDP3kLh4c65n4k0wfNdbTfr1zJAxQO92aZjTWB5ryLy0KF/RjNNDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-02T02:34:22.804272Z","bundle_sha256":"217db5ab7d1ee2e1e0da04f65732cfa7a3112f0f88b648dddd436fd0a480174b"}}