{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:EG35NHHM2H3NAIP5D3OEYBE2BI","short_pith_number":"pith:EG35NHHM","schema_version":"1.0","canonical_sha256":"21b7d69cecd1f6d021fd1edc4c049a0a38d41bfe5151b070d30f4f6cb42ffc33","source":{"kind":"arxiv","id":"2605.17284","version":1},"attestation_state":"computed","paper":{"title":"CLAP: Contrastive Latent-space Prompt Optimization for End-to-end Autonomous Driving","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.RO"],"primary_cat":"cs.CV","authors_text":"Ahmad Chalhoub, Boyuan Zheng, Qingzhao Zhang, Ruiyang Zhu, Yuehan He, Zesen Zhao, Z. Morley Mao","submitted_at":"2026-05-17T06:45:53Z","abstract_excerpt":"End-to-end autonomous driving systems powered by Vision-Language-Action (VLA) models achieve strong performance on common driving scenarios, yet remain brittle in rare but safety-critical long-tail situations such as active construction zones and complex yielding geometries. In this paper, we present a method that addresses the long-tail challenging scenes beyond data scaling and model training. We introduce CLAP (Contrastive Latent-space Prompt optimization), a location-aware adaptation framework that augments a frozen VLA driving model with per-roadblock soft prompts, optimized from crowdsou"},"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":"2605.17284","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-17T06:45:53Z","cross_cats_sorted":["cs.AI","cs.LG","cs.RO"],"title_canon_sha256":"7527dc00df1ad02071e2727099af9510b2e48ff0aa2c9086e258c822407b0cd1","abstract_canon_sha256":"9abcf44178f777f2a58be62e7ecdbe6c93a89f37e24d15b708a5b8c3aa8ba026"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:03:49.812796Z","signature_b64":"nxue9QzlMPjGGfbH2T+3KcO6ir58z+twb6pdcvYSTflITjA7k2cxr/ufZKbcVwmyMyNzM92UG1zBbzw/javOCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"21b7d69cecd1f6d021fd1edc4c049a0a38d41bfe5151b070d30f4f6cb42ffc33","last_reissued_at":"2026-05-20T00:03:49.812040Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:03:49.812040Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CLAP: Contrastive Latent-space Prompt Optimization for End-to-end Autonomous Driving","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.RO"],"primary_cat":"cs.CV","authors_text":"Ahmad Chalhoub, Boyuan Zheng, Qingzhao Zhang, Ruiyang Zhu, Yuehan He, Zesen Zhao, Z. Morley Mao","submitted_at":"2026-05-17T06:45:53Z","abstract_excerpt":"End-to-end autonomous driving systems powered by Vision-Language-Action (VLA) models achieve strong performance on common driving scenarios, yet remain brittle in rare but safety-critical long-tail situations such as active construction zones and complex yielding geometries. In this paper, we present a method that addresses the long-tail challenging scenes beyond data scaling and model training. We introduce CLAP (Contrastive Latent-space Prompt optimization), a location-aware adaptation framework that augments a frozen VLA driving model with per-roadblock soft prompts, optimized from crowdsou"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.17284","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/2605.17284/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"claim_evidence","ran_at":"2026-05-19T22:01:57.823152Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T21:33:23.770278Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"31ec4b4fb98e985c5ab4f6c7f5e9bc848a455e38b6a135b9c70a11e2c47ecf45"},"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":"2605.17284","created_at":"2026-05-20T00:03:49.812160+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.17284v1","created_at":"2026-05-20T00:03:49.812160+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.17284","created_at":"2026-05-20T00:03:49.812160+00:00"},{"alias_kind":"pith_short_12","alias_value":"EG35NHHM2H3N","created_at":"2026-05-20T00:03:49.812160+00:00"},{"alias_kind":"pith_short_16","alias_value":"EG35NHHM2H3NAIP5","created_at":"2026-05-20T00:03:49.812160+00:00"},{"alias_kind":"pith_short_8","alias_value":"EG35NHHM","created_at":"2026-05-20T00:03:49.812160+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/EG35NHHM2H3NAIP5D3OEYBE2BI","json":"https://pith.science/pith/EG35NHHM2H3NAIP5D3OEYBE2BI.json","graph_json":"https://pith.science/api/pith-number/EG35NHHM2H3NAIP5D3OEYBE2BI/graph.json","events_json":"https://pith.science/api/pith-number/EG35NHHM2H3NAIP5D3OEYBE2BI/events.json","paper":"https://pith.science/paper/EG35NHHM"},"agent_actions":{"view_html":"https://pith.science/pith/EG35NHHM2H3NAIP5D3OEYBE2BI","download_json":"https://pith.science/pith/EG35NHHM2H3NAIP5D3OEYBE2BI.json","view_paper":"https://pith.science/paper/EG35NHHM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.17284&json=true","fetch_graph":"https://pith.science/api/pith-number/EG35NHHM2H3NAIP5D3OEYBE2BI/graph.json","fetch_events":"https://pith.science/api/pith-number/EG35NHHM2H3NAIP5D3OEYBE2BI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EG35NHHM2H3NAIP5D3OEYBE2BI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EG35NHHM2H3NAIP5D3OEYBE2BI/action/storage_attestation","attest_author":"https://pith.science/pith/EG35NHHM2H3NAIP5D3OEYBE2BI/action/author_attestation","sign_citation":"https://pith.science/pith/EG35NHHM2H3NAIP5D3OEYBE2BI/action/citation_signature","submit_replication":"https://pith.science/pith/EG35NHHM2H3NAIP5D3OEYBE2BI/action/replication_record"}},"created_at":"2026-05-20T00:03:49.812160+00:00","updated_at":"2026-05-20T00:03:49.812160+00:00"}