{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:672NDRZV2BQZX7COAPMZTGB4UP","short_pith_number":"pith:672NDRZV","canonical_record":{"source":{"id":"2605.28583","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2026-05-27T15:06:34Z","cross_cats_sorted":["cs.AI","cs.LG","cs.SY","eess.SY"],"title_canon_sha256":"54684c20f9e2bffae83605a075e7fa8c27d9f1ecd4ae905f56fc8d214b907a17","abstract_canon_sha256":"7205278f76b413a3d3ab103213aaf1e4aed7939389014a645fb25c94931fee64"},"schema_version":"1.0"},"canonical_sha256":"f7f4d1c735d0619bfc4e03d999983ca3f464673931ca1716a9dad9160dd2f3dd","source":{"kind":"arxiv","id":"2605.28583","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.28583","created_at":"2026-05-28T02:04:57Z"},{"alias_kind":"arxiv_version","alias_value":"2605.28583v1","created_at":"2026-05-28T02:04:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.28583","created_at":"2026-05-28T02:04:57Z"},{"alias_kind":"pith_short_12","alias_value":"672NDRZV2BQZ","created_at":"2026-05-28T02:04:57Z"},{"alias_kind":"pith_short_16","alias_value":"672NDRZV2BQZX7CO","created_at":"2026-05-28T02:04:57Z"},{"alias_kind":"pith_short_8","alias_value":"672NDRZV","created_at":"2026-05-28T02:04:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:672NDRZV2BQZX7COAPMZTGB4UP","target":"record","payload":{"canonical_record":{"source":{"id":"2605.28583","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2026-05-27T15:06:34Z","cross_cats_sorted":["cs.AI","cs.LG","cs.SY","eess.SY"],"title_canon_sha256":"54684c20f9e2bffae83605a075e7fa8c27d9f1ecd4ae905f56fc8d214b907a17","abstract_canon_sha256":"7205278f76b413a3d3ab103213aaf1e4aed7939389014a645fb25c94931fee64"},"schema_version":"1.0"},"canonical_sha256":"f7f4d1c735d0619bfc4e03d999983ca3f464673931ca1716a9dad9160dd2f3dd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-28T02:04:57.035260Z","signature_b64":"rDtHo8wACEIl9H076p9iRNwKqtxvOlxvkut9fzI9Fd7XQ4f7ndBp9fkXFfxIrW20BjtnRUNslWBUVo51scDcBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f7f4d1c735d0619bfc4e03d999983ca3f464673931ca1716a9dad9160dd2f3dd","last_reissued_at":"2026-05-28T02:04:57.034687Z","signature_status":"signed_v1","first_computed_at":"2026-05-28T02:04:57.034687Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.28583","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":"2026-05-28T02:04:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0m55K+AIGKS03Qdb0eBpP1oQgTAjfpRj9eYqcj3YfMG2gPCNwYWfrkNf5UBBH2/yvR0xWKY/BJTGZXHBTWS3DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-04T08:17:39.427164Z"},"content_sha256":"086d1db766392a5e6d4c79fe0f61a02d9a466562d71ed75b5565c60a33c93343","schema_version":"1.0","event_id":"sha256:086d1db766392a5e6d4c79fe0f61a02d9a466562d71ed75b5565c60a33c93343"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:672NDRZV2BQZX7COAPMZTGB4UP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"SARAD: LLM-Based Safety-Aware Hybrid Reinforcement Learning with Collision Prediction for Autonomous Driving","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","cs.SY","eess.SY"],"primary_cat":"cs.RO","authors_text":"Guoxi Chen, Kangyu Wu, Peng Cui, Ya Zhang","submitted_at":"2026-05-27T15:06:34Z","abstract_excerpt":"Ensuring both safety and efficiency in decision-making for autonomous driving systems remains a fundamental challenge. Traditional Deep Reinforcement Learning (DRL) suffers from unsafe random exploration and slow convergence, while Large Language Models (LLMs) demonstrate inherent latency in real-time inference operations. To address these limitations, this paper proposes SARAD, a novel safety-aware hybrid framework that synergizes LLMs and DRL for autonomous driving. SARAD substitutes the random exploration of DRL with Retrieval-Augmented Generation (RAG)-enhanced, LLM-guided decisions source"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.28583","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/2605.28583/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":"2026-05-28T02:04:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"O0FKkGv/FnDopljOIyRI4+4Qc+QAX3nRNtpy07H8aOlC2pd1UIGAfJwuDeCVaaz5WrjvSqvOkSQhhz8qRiU4CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-04T08:17:39.427623Z"},"content_sha256":"34b2aed881ddec9354220452143570b516855c9f611c6603d55f0c24eca07dce","schema_version":"1.0","event_id":"sha256:34b2aed881ddec9354220452143570b516855c9f611c6603d55f0c24eca07dce"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/672NDRZV2BQZX7COAPMZTGB4UP/bundle.json","state_url":"https://pith.science/pith/672NDRZV2BQZX7COAPMZTGB4UP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/672NDRZV2BQZX7COAPMZTGB4UP/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":"2026-07-04T08:17:39Z","links":{"resolver":"https://pith.science/pith/672NDRZV2BQZX7COAPMZTGB4UP","bundle":"https://pith.science/pith/672NDRZV2BQZX7COAPMZTGB4UP/bundle.json","state":"https://pith.science/pith/672NDRZV2BQZX7COAPMZTGB4UP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/672NDRZV2BQZX7COAPMZTGB4UP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:672NDRZV2BQZX7COAPMZTGB4UP","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":"7205278f76b413a3d3ab103213aaf1e4aed7939389014a645fb25c94931fee64","cross_cats_sorted":["cs.AI","cs.LG","cs.SY","eess.SY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2026-05-27T15:06:34Z","title_canon_sha256":"54684c20f9e2bffae83605a075e7fa8c27d9f1ecd4ae905f56fc8d214b907a17"},"schema_version":"1.0","source":{"id":"2605.28583","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.28583","created_at":"2026-05-28T02:04:57Z"},{"alias_kind":"arxiv_version","alias_value":"2605.28583v1","created_at":"2026-05-28T02:04:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.28583","created_at":"2026-05-28T02:04:57Z"},{"alias_kind":"pith_short_12","alias_value":"672NDRZV2BQZ","created_at":"2026-05-28T02:04:57Z"},{"alias_kind":"pith_short_16","alias_value":"672NDRZV2BQZX7CO","created_at":"2026-05-28T02:04:57Z"},{"alias_kind":"pith_short_8","alias_value":"672NDRZV","created_at":"2026-05-28T02:04:57Z"}],"graph_snapshots":[{"event_id":"sha256:34b2aed881ddec9354220452143570b516855c9f611c6603d55f0c24eca07dce","target":"graph","created_at":"2026-05-28T02:04:57Z","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/2605.28583/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Ensuring both safety and efficiency in decision-making for autonomous driving systems remains a fundamental challenge. Traditional Deep Reinforcement Learning (DRL) suffers from unsafe random exploration and slow convergence, while Large Language Models (LLMs) demonstrate inherent latency in real-time inference operations. To address these limitations, this paper proposes SARAD, a novel safety-aware hybrid framework that synergizes LLMs and DRL for autonomous driving. SARAD substitutes the random exploration of DRL with Retrieval-Augmented Generation (RAG)-enhanced, LLM-guided decisions source","authors_text":"Guoxi Chen, Kangyu Wu, Peng Cui, Ya Zhang","cross_cats":["cs.AI","cs.LG","cs.SY","eess.SY"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2026-05-27T15:06:34Z","title":"SARAD: LLM-Based Safety-Aware Hybrid Reinforcement Learning with Collision Prediction for Autonomous Driving"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.28583","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":"sha256:086d1db766392a5e6d4c79fe0f61a02d9a466562d71ed75b5565c60a33c93343","target":"record","created_at":"2026-05-28T02:04:57Z","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":"7205278f76b413a3d3ab103213aaf1e4aed7939389014a645fb25c94931fee64","cross_cats_sorted":["cs.AI","cs.LG","cs.SY","eess.SY"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.RO","submitted_at":"2026-05-27T15:06:34Z","title_canon_sha256":"54684c20f9e2bffae83605a075e7fa8c27d9f1ecd4ae905f56fc8d214b907a17"},"schema_version":"1.0","source":{"id":"2605.28583","kind":"arxiv","version":1}},"canonical_sha256":"f7f4d1c735d0619bfc4e03d999983ca3f464673931ca1716a9dad9160dd2f3dd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f7f4d1c735d0619bfc4e03d999983ca3f464673931ca1716a9dad9160dd2f3dd","first_computed_at":"2026-05-28T02:04:57.034687Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-28T02:04:57.034687Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rDtHo8wACEIl9H076p9iRNwKqtxvOlxvkut9fzI9Fd7XQ4f7ndBp9fkXFfxIrW20BjtnRUNslWBUVo51scDcBA==","signature_status":"signed_v1","signed_at":"2026-05-28T02:04:57.035260Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.28583","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:086d1db766392a5e6d4c79fe0f61a02d9a466562d71ed75b5565c60a33c93343","sha256:34b2aed881ddec9354220452143570b516855c9f611c6603d55f0c24eca07dce"],"state_sha256":"40aa415107cb56afc22f7633aaf4d276dec9c7dc14cf5d00d83482666c5f0ec4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6a4oRjhr+Z3ruBgBHpOsK47kQkxiiAzCwkiDHQaqoIQAMbqNPXxdIwbCnxWHb+1kDT8z/yCJxqS7q0Chk8nzBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-04T08:17:39.429490Z","bundle_sha256":"77219c24c7a04433eabfff25358295ed54a5f2799dada698c47f5b30ab0dae30"}}