{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:RXYW34HC74ZJGEYR6OR4GMT5BD","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":"7f9ed280c8fc713b74beac23c04021933a6cd5ea34404d80c8de3e97b6e0e2fc","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-05-22T15:44:30Z","title_canon_sha256":"63e8d837856143073414cd1d754d7694f2fcecd4fbc06daf04b26ff8add6e796"},"schema_version":"1.0","source":{"id":"2505.16805","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2505.16805","created_at":"2026-07-05T11:07:41Z"},{"alias_kind":"arxiv_version","alias_value":"2505.16805v1","created_at":"2026-07-05T11:07:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.16805","created_at":"2026-07-05T11:07:41Z"},{"alias_kind":"pith_short_12","alias_value":"RXYW34HC74ZJ","created_at":"2026-07-05T11:07:41Z"},{"alias_kind":"pith_short_16","alias_value":"RXYW34HC74ZJGEYR","created_at":"2026-07-05T11:07:41Z"},{"alias_kind":"pith_short_8","alias_value":"RXYW34HC","created_at":"2026-07-05T11:07:41Z"}],"graph_snapshots":[{"event_id":"sha256:442c06da25e6ef212fd1a11391be9662e866ddf371c188ec9552ed5e46f24b49","target":"graph","created_at":"2026-07-05T11:07:41Z","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/2505.16805/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The integration of Vision-Language Models (VLMs) into autonomous driving systems has shown promise in addressing key challenges such as learning complexity, interpretability, and common-sense reasoning. However, existing approaches often struggle with efficient integration and realtime decision-making due to computational demands. In this paper, we introduce SOLVE, an innovative framework that synergizes VLMs with end-to-end (E2E) models to enhance autonomous vehicle planning. Our approach emphasizes knowledge sharing at the feature level through a shared visual encoder, enabling comprehensive","authors_text":"Hongsheng Li, LinJiang Huang, Rongyao Fang, Shaoshuai Shi, Tao Ma, Xuesong Chen","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-05-22T15:44:30Z","title":"SOLVE: Synergy of Language-Vision and End-to-End Networks for Autonomous Driving"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.16805","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":"sha256:719dbf7d967dfc8e1d85d019aff192998ced75f924157d3e4af940e3800635ec","target":"record","created_at":"2026-07-05T11:07:41Z","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":"7f9ed280c8fc713b74beac23c04021933a6cd5ea34404d80c8de3e97b6e0e2fc","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-05-22T15:44:30Z","title_canon_sha256":"63e8d837856143073414cd1d754d7694f2fcecd4fbc06daf04b26ff8add6e796"},"schema_version":"1.0","source":{"id":"2505.16805","kind":"arxiv","version":1}},"canonical_sha256":"8df16df0e2ff32931311f3a3c3327d08e99e41eab42736c8f21ac44e9cd47c45","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8df16df0e2ff32931311f3a3c3327d08e99e41eab42736c8f21ac44e9cd47c45","first_computed_at":"2026-07-05T11:07:41.464297Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:07:41.464297Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QBjeiGDpsT8lBjEVJgg4rKuQ3bcSJoB5VYKVBDNcV8gje2unAdApa3Z6GlxEUeWe+gMEGx/KTaAu4nQPa3lGBA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:07:41.464824Z","signed_message":"canonical_sha256_bytes"},"source_id":"2505.16805","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:719dbf7d967dfc8e1d85d019aff192998ced75f924157d3e4af940e3800635ec","sha256:442c06da25e6ef212fd1a11391be9662e866ddf371c188ec9552ed5e46f24b49"],"state_sha256":"a952ce7518757d61501e0e934595c3d7661dbc1850eb9dc2a7d81087aa64d982"}