{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:QHUNTCVKPQTXYNRPC6CIT6P4IR","short_pith_number":"pith:QHUNTCVK","canonical_record":{"source":{"id":"2606.08525","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-07T09:05:49Z","cross_cats_sorted":[],"title_canon_sha256":"29cad9cf381bc0050a57c00dd49ee672315b851f546331c267e36a5953f0df47","abstract_canon_sha256":"d66a6dcc1c473229838760ef6aa1ad5b4ecd0cbc7217138920c2a89bb5c40e70"},"schema_version":"1.0"},"canonical_sha256":"81e8d98aaa7c277c362f178489f9fc446cae87d337d97325faf367ee20c91ac6","source":{"kind":"arxiv","id":"2606.08525","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.08525","created_at":"2026-06-09T01:05:39Z"},{"alias_kind":"arxiv_version","alias_value":"2606.08525v1","created_at":"2026-06-09T01:05:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.08525","created_at":"2026-06-09T01:05:39Z"},{"alias_kind":"pith_short_12","alias_value":"QHUNTCVKPQTX","created_at":"2026-06-09T01:05:39Z"},{"alias_kind":"pith_short_16","alias_value":"QHUNTCVKPQTXYNRP","created_at":"2026-06-09T01:05:39Z"},{"alias_kind":"pith_short_8","alias_value":"QHUNTCVK","created_at":"2026-06-09T01:05:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:QHUNTCVKPQTXYNRPC6CIT6P4IR","target":"record","payload":{"canonical_record":{"source":{"id":"2606.08525","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-07T09:05:49Z","cross_cats_sorted":[],"title_canon_sha256":"29cad9cf381bc0050a57c00dd49ee672315b851f546331c267e36a5953f0df47","abstract_canon_sha256":"d66a6dcc1c473229838760ef6aa1ad5b4ecd0cbc7217138920c2a89bb5c40e70"},"schema_version":"1.0"},"canonical_sha256":"81e8d98aaa7c277c362f178489f9fc446cae87d337d97325faf367ee20c91ac6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-09T01:05:39.064621Z","signature_b64":"uEIJZABoOSPJwL5z1kXFE9hbHiGVk6BRmk+sDYB1GCKrcr3R/Gfa79vr9J9VKiMmGLP9r/EuyY7YIkO6TrkwBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"81e8d98aaa7c277c362f178489f9fc446cae87d337d97325faf367ee20c91ac6","last_reissued_at":"2026-06-09T01:05:39.064200Z","signature_status":"signed_v1","first_computed_at":"2026-06-09T01:05:39.064200Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.08525","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-09T01:05:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GFvgiwPXQNVUKcqdaTbmH1GC5gdSmXhc8vXEJ5BEVMWaQ19M/AcBY0m4S+IjZLdE5fTCOtgKbdrBChkEf6VAAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-03T12:36:13.980382Z"},"content_sha256":"e46a4dcefda454023e1416a53585ab78809ab6e266192da7b50e28968a3436de","schema_version":"1.0","event_id":"sha256:e46a4dcefda454023e1416a53585ab78809ab6e266192da7b50e28968a3436de"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:QHUNTCVKPQTXYNRPC6CIT6P4IR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DriveReward: A Comprehensive Dataset and Generative Vision-Language Reward Model for Autonomous Driving","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bing Wang, Fang Li, Fangzhen Li, Guang Chen, Haiyang Sun, Hangjun Ye, Hongwei Xie, Jiong Deng, Long Chen, Qimao Chen, Yang Ji, Yi Zhang, Yuechen Luo, Zehan Zhang","submitted_at":"2026-06-07T09:05:49Z","abstract_excerpt":"Reward models play a pivotal role in reinforcement learning (RL) and multi-modal trajectory selection for autonomous driving. However, acquiring such rewards typically relies on hand-crafted rule-based objectives or perception ground truth, which hinders generalization for data-scaling. While Vision-Language Models (VLMs) have demonstrated feasibility as reward models in other domains, their effectiveness in driving tasks remains underexplored. In this work, we bridge this gap by (1) introducing DriveReward, a reasoning trajectory evaluation dataset rigorously labeled via temporally-grounded v"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.08525","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/2606.08525/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-09T01:05:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mCy3TRw5AcIVQBslt6VBacsWXLOQTK6K9FuKbgzjRAI7GHMqYbCk5hg6Ft1tqwQO/9CucS5j8oqdyXJcDx64Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-03T12:36:13.980751Z"},"content_sha256":"e57bc509b2d9f673862a2dfae2e34d98782e6cc9d543d156f5f0ef12a238c567","schema_version":"1.0","event_id":"sha256:e57bc509b2d9f673862a2dfae2e34d98782e6cc9d543d156f5f0ef12a238c567"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QHUNTCVKPQTXYNRPC6CIT6P4IR/bundle.json","state_url":"https://pith.science/pith/QHUNTCVKPQTXYNRPC6CIT6P4IR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QHUNTCVKPQTXYNRPC6CIT6P4IR/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":"2026-07-03T12:36:13Z","links":{"resolver":"https://pith.science/pith/QHUNTCVKPQTXYNRPC6CIT6P4IR","bundle":"https://pith.science/pith/QHUNTCVKPQTXYNRPC6CIT6P4IR/bundle.json","state":"https://pith.science/pith/QHUNTCVKPQTXYNRPC6CIT6P4IR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QHUNTCVKPQTXYNRPC6CIT6P4IR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:QHUNTCVKPQTXYNRPC6CIT6P4IR","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":"d66a6dcc1c473229838760ef6aa1ad5b4ecd0cbc7217138920c2a89bb5c40e70","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-07T09:05:49Z","title_canon_sha256":"29cad9cf381bc0050a57c00dd49ee672315b851f546331c267e36a5953f0df47"},"schema_version":"1.0","source":{"id":"2606.08525","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.08525","created_at":"2026-06-09T01:05:39Z"},{"alias_kind":"arxiv_version","alias_value":"2606.08525v1","created_at":"2026-06-09T01:05:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.08525","created_at":"2026-06-09T01:05:39Z"},{"alias_kind":"pith_short_12","alias_value":"QHUNTCVKPQTX","created_at":"2026-06-09T01:05:39Z"},{"alias_kind":"pith_short_16","alias_value":"QHUNTCVKPQTXYNRP","created_at":"2026-06-09T01:05:39Z"},{"alias_kind":"pith_short_8","alias_value":"QHUNTCVK","created_at":"2026-06-09T01:05:39Z"}],"graph_snapshots":[{"event_id":"sha256:e57bc509b2d9f673862a2dfae2e34d98782e6cc9d543d156f5f0ef12a238c567","target":"graph","created_at":"2026-06-09T01:05:39Z","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/2606.08525/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Reward models play a pivotal role in reinforcement learning (RL) and multi-modal trajectory selection for autonomous driving. However, acquiring such rewards typically relies on hand-crafted rule-based objectives or perception ground truth, which hinders generalization for data-scaling. While Vision-Language Models (VLMs) have demonstrated feasibility as reward models in other domains, their effectiveness in driving tasks remains underexplored. In this work, we bridge this gap by (1) introducing DriveReward, a reasoning trajectory evaluation dataset rigorously labeled via temporally-grounded v","authors_text":"Bing Wang, Fang Li, Fangzhen Li, Guang Chen, Haiyang Sun, Hangjun Ye, Hongwei Xie, Jiong Deng, Long Chen, Qimao Chen, Yang Ji, Yi Zhang, Yuechen Luo, Zehan Zhang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-07T09:05:49Z","title":"DriveReward: A Comprehensive Dataset and Generative Vision-Language Reward Model for Autonomous Driving"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.08525","kind":"arxiv","version":1},"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:e46a4dcefda454023e1416a53585ab78809ab6e266192da7b50e28968a3436de","target":"record","created_at":"2026-06-09T01:05:39Z","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":"d66a6dcc1c473229838760ef6aa1ad5b4ecd0cbc7217138920c2a89bb5c40e70","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-07T09:05:49Z","title_canon_sha256":"29cad9cf381bc0050a57c00dd49ee672315b851f546331c267e36a5953f0df47"},"schema_version":"1.0","source":{"id":"2606.08525","kind":"arxiv","version":1}},"canonical_sha256":"81e8d98aaa7c277c362f178489f9fc446cae87d337d97325faf367ee20c91ac6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"81e8d98aaa7c277c362f178489f9fc446cae87d337d97325faf367ee20c91ac6","first_computed_at":"2026-06-09T01:05:39.064200Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-09T01:05:39.064200Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uEIJZABoOSPJwL5z1kXFE9hbHiGVk6BRmk+sDYB1GCKrcr3R/Gfa79vr9J9VKiMmGLP9r/EuyY7YIkO6TrkwBw==","signature_status":"signed_v1","signed_at":"2026-06-09T01:05:39.064621Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.08525","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e46a4dcefda454023e1416a53585ab78809ab6e266192da7b50e28968a3436de","sha256:e57bc509b2d9f673862a2dfae2e34d98782e6cc9d543d156f5f0ef12a238c567"],"state_sha256":"550bddf9c7552ad96f920e59f15af106c391945649a3d0164c5e92ffa82f50a1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fBv/j7LfzaWu1qV4YOJ+uEyy8ZXYlD5lZS53vE8fJUA/lZ6fOnXLbFWllgFU/ZmglxYLKmfadt+XocZ1tnrbAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-03T12:36:13.982756Z","bundle_sha256":"6b2563f596f3ce75c9f1ce860a4a14d649572f781226fb6b409ea8eb405935ca"}}