{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:SNHBDF3EN5GTWPSZ3OBYSJ3UQO","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":"3eab46ec7ec607efc9428b2b809fa323bde607af93081a39f32795ee9804425c","cross_cats_sorted":["cs.LG","cs.SY"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SY","submitted_at":"2025-04-02T14:18:52Z","title_canon_sha256":"7689fb6fe663b973e05354fb49a56c483eb81b6313ae5e82b3bc72035117d3d7"},"schema_version":"1.0","source":{"id":"2504.01766","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.01766","created_at":"2026-07-05T10:43:29Z"},{"alias_kind":"arxiv_version","alias_value":"2504.01766v1","created_at":"2026-07-05T10:43:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.01766","created_at":"2026-07-05T10:43:29Z"},{"alias_kind":"pith_short_12","alias_value":"SNHBDF3EN5GT","created_at":"2026-07-05T10:43:29Z"},{"alias_kind":"pith_short_16","alias_value":"SNHBDF3EN5GTWPSZ","created_at":"2026-07-05T10:43:29Z"},{"alias_kind":"pith_short_8","alias_value":"SNHBDF3E","created_at":"2026-07-05T10:43:29Z"}],"graph_snapshots":[{"event_id":"sha256:ac2fb8d6947fd363426ef73809a66db0f0e4670db654bfc00a1400dd470cf573","target":"graph","created_at":"2026-07-05T10:43:29Z","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/2504.01766/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Compounding error, where small prediction mistakes accumulate over time, presents a major challenge in learning-based control. For example, this issue often limits the performance of model-based reinforcement learning and imitation learning. One common approach to mitigate compounding error is to train multi-step predictors directly, rather than relying on autoregressive rollout of a single-step model. However, it is not well understood when the benefits of multi-step prediction outweigh the added complexity of learning a more complicated model. In this work, we provide a rigorous analysis of ","authors_text":"Anne Somalwar, Bruce D. Lee, George J. Pappas, Nikolai Matni","cross_cats":["cs.LG","cs.SY"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SY","submitted_at":"2025-04-02T14:18:52Z","title":"Learning with Imperfect Models: When Multi-step Prediction Mitigates Compounding Error"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.01766","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:e205a2f74976284a6019d498f3017faa1361b2762bf43fdd87b5d1bc3c5fde61","target":"record","created_at":"2026-07-05T10:43:29Z","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":"3eab46ec7ec607efc9428b2b809fa323bde607af93081a39f32795ee9804425c","cross_cats_sorted":["cs.LG","cs.SY"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SY","submitted_at":"2025-04-02T14:18:52Z","title_canon_sha256":"7689fb6fe663b973e05354fb49a56c483eb81b6313ae5e82b3bc72035117d3d7"},"schema_version":"1.0","source":{"id":"2504.01766","kind":"arxiv","version":1}},"canonical_sha256":"934e1197646f4d3b3e59db8389277483abed5f7a0bb7e1003af9ef3bbe06c261","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"934e1197646f4d3b3e59db8389277483abed5f7a0bb7e1003af9ef3bbe06c261","first_computed_at":"2026-07-05T10:43:29.565364Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:43:29.565364Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uXq72SiQ2l1Bki8qcTesJ3Q31it+9UT7hydxfCb6wnMo4wvhZfvyuUk6PCGD0E3RfXW/wJGJH112kGIhJYavDw==","signature_status":"signed_v1","signed_at":"2026-07-05T10:43:29.565819Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.01766","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e205a2f74976284a6019d498f3017faa1361b2762bf43fdd87b5d1bc3c5fde61","sha256:ac2fb8d6947fd363426ef73809a66db0f0e4670db654bfc00a1400dd470cf573"],"state_sha256":"af80ccbe5f3af5d329ee5a42d8eef50c09c6ae5204e2f3d8a84ea4b74c82e405"}