{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:2MGWJWGXLTQDBWDIQ7E3QOXJ62","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":"82647ba9941d125a186d5ada4c76a240ccb5f00c0377d47c06584e5b6a439cc0","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-08-06T17:46:40Z","title_canon_sha256":"be62f3888c427878058469777aef07a609da9c08647f18c258c3e9f7fb2f94c1"},"schema_version":"1.0","source":{"id":"2508.04682","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.04682","created_at":"2026-07-05T11:50:39Z"},{"alias_kind":"arxiv_version","alias_value":"2508.04682v2","created_at":"2026-07-05T11:50:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.04682","created_at":"2026-07-05T11:50:39Z"},{"alias_kind":"pith_short_12","alias_value":"2MGWJWGXLTQD","created_at":"2026-07-05T11:50:39Z"},{"alias_kind":"pith_short_16","alias_value":"2MGWJWGXLTQDBWDI","created_at":"2026-07-05T11:50:39Z"},{"alias_kind":"pith_short_8","alias_value":"2MGWJWGX","created_at":"2026-07-05T11:50:39Z"}],"graph_snapshots":[{"event_id":"sha256:14bc1a2e6cf8f6b5d01a697e5ea04d384ea04e81d1e89e72132a6f0346d8a9a8","target":"graph","created_at":"2026-07-05T11:50:39Z","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/2508.04682/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"End-to-end training of multi-agent systems offers significant advantages in improving multi-task performance. However, training such models remains challenging and requires extensive manual design and monitoring. In this work, we introduce TurboTrain, a novel and efficient training framework for multi-agent perception and prediction. TurboTrain comprises two key components: a multi-agent spatiotemporal pretraining scheme based on masked reconstruction learning and a balanced multi-task learning strategy based on gradient conflict suppression. By streamlining the training process, our framework","authors_text":"Bolei Zhou, Jiaqi Ma, Seth Z. Zhao, Tianhui Cai, Zewei Zhou, Zhiyu Huang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-08-06T17:46:40Z","title":"TurboTrain: Towards Efficient and Balanced Multi-Task Learning for Multi-Agent Perception and Prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.04682","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:f72cbf66b1e224aa5ada9feca06ef01388f4f1cf253099f8982ea6d4c6322678","target":"record","created_at":"2026-07-05T11:50:39Z","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":"82647ba9941d125a186d5ada4c76a240ccb5f00c0377d47c06584e5b6a439cc0","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2025-08-06T17:46:40Z","title_canon_sha256":"be62f3888c427878058469777aef07a609da9c08647f18c258c3e9f7fb2f94c1"},"schema_version":"1.0","source":{"id":"2508.04682","kind":"arxiv","version":2}},"canonical_sha256":"d30d64d8d75ce030d86887c9b83ae9f6a6accc4c6e9c25107b72544ad4c968b1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d30d64d8d75ce030d86887c9b83ae9f6a6accc4c6e9c25107b72544ad4c968b1","first_computed_at":"2026-07-05T11:50:39.991937Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:50:39.991937Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uasXKOXNkvNMQN56vrCPmcFu4MjheqYXnP5fsSYOYjcxn6DLhvxz0i8eaMSjkhe1kkY3PyOuavRUPkOc3eqHBw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:50:39.992343Z","signed_message":"canonical_sha256_bytes"},"source_id":"2508.04682","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f72cbf66b1e224aa5ada9feca06ef01388f4f1cf253099f8982ea6d4c6322678","sha256:14bc1a2e6cf8f6b5d01a697e5ea04d384ea04e81d1e89e72132a6f0346d8a9a8"],"state_sha256":"c45c188577d06c2da16366aaab1ccf8475cfd8f89a1caa1e1d200be172acd156"}