{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:L46G2RRMACH3RVGJ43SZ5EUUOU","short_pith_number":"pith:L46G2RRM","schema_version":"1.0","canonical_sha256":"5f3c6d462c008fb8d4c9e6e59e9294751737fad368cea13363e88665e1602099","source":{"kind":"arxiv","id":"2507.14456","version":4},"attestation_state":"computed","paper":{"title":"GEMINUS: Dual-aware Global and Scene-Adaptive Mixture-of-Experts for End-to-End Autonomous Driving","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Chi Wan, Jiatong Du, Nan Li, Peng Yi, Shuo Yang, Yanjun Huang, Yixin Cui, Yulong Bai","submitted_at":"2025-07-19T03:04:28Z","abstract_excerpt":"End-to-end autonomous driving requires adaptive and robust handling of complex and diverse traffic environments. However, prevalent single-mode planning methods attempt to learn an overall policy while struggling to acquire diversified driving skills to handle diverse scenarios. Therefore, this paper proposes GEMINUS, a Mixture-of-Experts end-to-end autonomous driving framework featuring a Global Expert and a Scene-Adaptive Experts Group, equipped with a Dual-aware Router. Specifically, the Global Expert is trained on the overall dataset, possessing robust performance. The Scene-Adaptive Exper"},"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":"2507.14456","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-07-19T03:04:28Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"43686b13c29a5c5a1d92f6059df04dc4fd432ce5275fbcf5aea5c820585db537","abstract_canon_sha256":"d2789436e162730e910871fbf5d23f29babc7f202a1b7fa3c7be30ef5335cb64"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:09:21.762682Z","signature_b64":"b+beyFufH+S4bMx0ZUsvbeLWlqY46DZo89yChYOwuN9p5r48JiWs63zANyLRJNG78NJxxa+/S4yyJKJGhSH3Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5f3c6d462c008fb8d4c9e6e59e9294751737fad368cea13363e88665e1602099","last_reissued_at":"2026-07-05T12:09:21.762151Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:09:21.762151Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GEMINUS: Dual-aware Global and Scene-Adaptive Mixture-of-Experts for End-to-End Autonomous Driving","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Chi Wan, Jiatong Du, Nan Li, Peng Yi, Shuo Yang, Yanjun Huang, Yixin Cui, Yulong Bai","submitted_at":"2025-07-19T03:04:28Z","abstract_excerpt":"End-to-end autonomous driving requires adaptive and robust handling of complex and diverse traffic environments. However, prevalent single-mode planning methods attempt to learn an overall policy while struggling to acquire diversified driving skills to handle diverse scenarios. Therefore, this paper proposes GEMINUS, a Mixture-of-Experts end-to-end autonomous driving framework featuring a Global Expert and a Scene-Adaptive Experts Group, equipped with a Dual-aware Router. Specifically, the Global Expert is trained on the overall dataset, possessing robust performance. The Scene-Adaptive Exper"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.14456","kind":"arxiv","version":4},"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/2507.14456/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2507.14456","created_at":"2026-07-05T12:09:21.762221+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.14456v4","created_at":"2026-07-05T12:09:21.762221+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.14456","created_at":"2026-07-05T12:09:21.762221+00:00"},{"alias_kind":"pith_short_12","alias_value":"L46G2RRMACH3","created_at":"2026-07-05T12:09:21.762221+00:00"},{"alias_kind":"pith_short_16","alias_value":"L46G2RRMACH3RVGJ","created_at":"2026-07-05T12:09:21.762221+00:00"},{"alias_kind":"pith_short_8","alias_value":"L46G2RRM","created_at":"2026-07-05T12:09:21.762221+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.22617","citing_title":"OmniSpace: Efficient Geometry Awareness for Autonomous Vehicles MLLMs","ref_index":57,"is_internal_anchor":false},{"citing_arxiv_id":"2606.04884","citing_title":"D$^3$-MoE:Dual Disentangled Diffusion Mixture-of-Experts for Style-Controllable End-to-End Autonomous Driving","ref_index":22,"is_internal_anchor":false},{"citing_arxiv_id":"2512.10719","citing_title":"SpaceDrive: Infusing Spatial Awareness into VLM-based Autonomous Driving","ref_index":60,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/L46G2RRMACH3RVGJ43SZ5EUUOU","json":"https://pith.science/pith/L46G2RRMACH3RVGJ43SZ5EUUOU.json","graph_json":"https://pith.science/api/pith-number/L46G2RRMACH3RVGJ43SZ5EUUOU/graph.json","events_json":"https://pith.science/api/pith-number/L46G2RRMACH3RVGJ43SZ5EUUOU/events.json","paper":"https://pith.science/paper/L46G2RRM"},"agent_actions":{"view_html":"https://pith.science/pith/L46G2RRMACH3RVGJ43SZ5EUUOU","download_json":"https://pith.science/pith/L46G2RRMACH3RVGJ43SZ5EUUOU.json","view_paper":"https://pith.science/paper/L46G2RRM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.14456&json=true","fetch_graph":"https://pith.science/api/pith-number/L46G2RRMACH3RVGJ43SZ5EUUOU/graph.json","fetch_events":"https://pith.science/api/pith-number/L46G2RRMACH3RVGJ43SZ5EUUOU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L46G2RRMACH3RVGJ43SZ5EUUOU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L46G2RRMACH3RVGJ43SZ5EUUOU/action/storage_attestation","attest_author":"https://pith.science/pith/L46G2RRMACH3RVGJ43SZ5EUUOU/action/author_attestation","sign_citation":"https://pith.science/pith/L46G2RRMACH3RVGJ43SZ5EUUOU/action/citation_signature","submit_replication":"https://pith.science/pith/L46G2RRMACH3RVGJ43SZ5EUUOU/action/replication_record"}},"created_at":"2026-07-05T12:09:21.762221+00:00","updated_at":"2026-07-05T12:09:21.762221+00:00"}