{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:5SCDEDYC3YVBAD3ZTMMEL3DSJK","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":"bb58975917e3b38853b622e7790cf12700559f53702315dd3afd18ecf3f3a30d","cross_cats_sorted":["cs.AI","cs.LG","cs.RO"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-01-12T01:31:07Z","title_canon_sha256":"65309403ff74c669d9a5959c07a4f96b868bcf4363ae96f3069ac67c633e9f25"},"schema_version":"1.0","source":{"id":"2501.06680","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2501.06680","created_at":"2026-07-05T11:45:30Z"},{"alias_kind":"arxiv_version","alias_value":"2501.06680v2","created_at":"2026-07-05T11:45:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.06680","created_at":"2026-07-05T11:45:30Z"},{"alias_kind":"pith_short_12","alias_value":"5SCDEDYC3YVB","created_at":"2026-07-05T11:45:30Z"},{"alias_kind":"pith_short_16","alias_value":"5SCDEDYC3YVBAD3Z","created_at":"2026-07-05T11:45:30Z"},{"alias_kind":"pith_short_8","alias_value":"5SCDEDYC","created_at":"2026-07-05T11:45:30Z"}],"graph_snapshots":[{"event_id":"sha256:59a4b8ba3264cac5f25d13771c51c3032002424d83c92c3af02a6c4246429ff0","target":"graph","created_at":"2026-07-05T11:45:30Z","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/2501.06680/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Vision-language models (VLMs) have become a promising approach to enhancing perception and decision-making in autonomous driving. The gap remains in applying VLMs to understand complex scenarios interacting with pedestrians and efficient vehicle deployment. In this paper, we propose a knowledge distillation method that transfers knowledge from large-scale vision-language foundation models to efficient vision networks, and we apply it to pedestrian behavior prediction and scene understanding tasks, achieving promising results in generating more diverse and comprehensive semantic attributes. We ","authors_text":"Haoxiang Gao, Jinghan Cao, Li Zhang, Yu Zhao, Zhou Yang","cross_cats":["cs.AI","cs.LG","cs.RO"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-01-12T01:31:07Z","title":"Application of Vision-Language Model to Pedestrians Behavior and Scene Understanding in Autonomous Driving"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.06680","kind":"arxiv","version":2},"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":"sha256:7fc25617ff6d441694d30ecd28664a15493f436d57ac90830e98d44471b00dd8","target":"record","created_at":"2026-07-05T11:45:30Z","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":"bb58975917e3b38853b622e7790cf12700559f53702315dd3afd18ecf3f3a30d","cross_cats_sorted":["cs.AI","cs.LG","cs.RO"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-01-12T01:31:07Z","title_canon_sha256":"65309403ff74c669d9a5959c07a4f96b868bcf4363ae96f3069ac67c633e9f25"},"schema_version":"1.0","source":{"id":"2501.06680","kind":"arxiv","version":2}},"canonical_sha256":"ec84320f02de2a100f799b1845ec724a843b86056e40c1942aac7e47334f20ae","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ec84320f02de2a100f799b1845ec724a843b86056e40c1942aac7e47334f20ae","first_computed_at":"2026-07-05T11:45:30.640907Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:45:30.640907Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EYP0/9nKEXGAJ1S+MNnX+YUPLfFNbQrLDf1Si22bEHZKtR9w7CKg/cPkjKDrj8ue3zso2xM7NzBLdVNY/OTnAw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:45:30.641436Z","signed_message":"canonical_sha256_bytes"},"source_id":"2501.06680","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7fc25617ff6d441694d30ecd28664a15493f436d57ac90830e98d44471b00dd8","sha256:59a4b8ba3264cac5f25d13771c51c3032002424d83c92c3af02a6c4246429ff0"],"state_sha256":"b31104ed212287b95e97f70b4f1db0e6fa617fdb50c9bc2de62d97d97df07787"}