{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:3YKV7I2H34WR43B4NPXWNM4OOY","short_pith_number":"pith:3YKV7I2H","canonical_record":{"source":{"id":"2604.07393","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2026-04-08T06:21:10Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"d9aa50c9a9c7444dc991a01e89555001dcfc9bbc3253776bb0cea9be387ad1e2","abstract_canon_sha256":"6c3c87eb83c105ccc080e77f1f952d2660022404e39510316e379225a09050f7"},"schema_version":"1.0"},"canonical_sha256":"de155fa347df2d1e6c3c6bef66b38e761ddcb789559333358ae5fcf39bdc5df8","source":{"kind":"arxiv","id":"2604.07393","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.07393","created_at":"2026-05-20T00:03:10Z"},{"alias_kind":"arxiv_version","alias_value":"2604.07393v2","created_at":"2026-05-20T00:03:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.07393","created_at":"2026-05-20T00:03:10Z"},{"alias_kind":"pith_short_12","alias_value":"3YKV7I2H34WR","created_at":"2026-05-20T00:03:10Z"},{"alias_kind":"pith_short_16","alias_value":"3YKV7I2H34WR43B4","created_at":"2026-05-20T00:03:10Z"},{"alias_kind":"pith_short_8","alias_value":"3YKV7I2H","created_at":"2026-05-20T00:03:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:3YKV7I2H34WR43B4NPXWNM4OOY","target":"record","payload":{"canonical_record":{"source":{"id":"2604.07393","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2026-04-08T06:21:10Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"d9aa50c9a9c7444dc991a01e89555001dcfc9bbc3253776bb0cea9be387ad1e2","abstract_canon_sha256":"6c3c87eb83c105ccc080e77f1f952d2660022404e39510316e379225a09050f7"},"schema_version":"1.0"},"canonical_sha256":"de155fa347df2d1e6c3c6bef66b38e761ddcb789559333358ae5fcf39bdc5df8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:03:10.709406Z","signature_b64":"y5z9vsEP8Osg8Nz64tcTQeNpqGurWx1fZJBs3pyDF2KFJq5l5Bd0yVe4Aa9ZNs+c5da2HwURjyMjN+j0sGqyAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"de155fa347df2d1e6c3c6bef66b38e761ddcb789559333358ae5fcf39bdc5df8","last_reissued_at":"2026-05-20T00:03:10.708513Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:03:10.708513Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2604.07393","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:03:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZZEDLnfZnVcTS3axPRiR7CB26a/w2Vkmt+x5F8GEdHcC28I/r1JbDLfnynS0ufafZgYTiLmda/fsM3l+X/u0Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T08:57:28.189278Z"},"content_sha256":"15221c7f333b97f220cfb657b4f2b464f6233a1a6b92996c06947395b03bb477","schema_version":"1.0","event_id":"sha256:15221c7f333b97f220cfb657b4f2b464f6233a1a6b92996c06947395b03bb477"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:3YKV7I2H34WR43B4NPXWNM4OOY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DSPR: Dual-Stream Physics-Residual Networks for Trustworthy Industrial Time Series Forecasting","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"DSPR decouples stable temporal patterns from regime-dependent residual dynamics to forecast industrial time series with high accuracy and physical consistency.","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Guoqing Wang, Pengwei Yang, Tianyu Li, Yeran Zhang","submitted_at":"2026-04-08T06:21:10Z","abstract_excerpt":"Accurate forecasting of industrial time series requires balancing predictive accuracy with physical plausibility under non-stationary operating conditions. Existing data-driven models often achieve strong statistical performance but struggle to respect regime-dependent interaction structures and transport delays inherent in real-world systems. To address this challenge, we propose DSPR (Dual-Stream Physics-Residual Networks), a forecasting framework that explicitly decouples stable temporal patterns from regime-dependent residual dynamics. The first stream models the statistical temporal evolu"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"Experiments on four industrial benchmarks spanning heterogeneous regimes demonstrate that DSPR consistently improves forecasting accuracy and robustness under regime shifts while maintaining strong physical plausibility. It achieves state-of-the-art predictive performance, with Mean Conservation Accuracy exceeding 99% and Total Variation Ratio reaching up to 97.2%.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the physical priors embedded in the Physics-Guided Dynamic Graph accurately reflect true regime-dependent interaction structures without introducing bias or suppressing valid correlations, and that the Adaptive Window module reliably estimates flow-dependent transport delays from data alone.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"DSPR decouples statistical temporal evolution from physics-informed residual dynamics via an adaptive window for transport delays and a physics-guided dynamic graph to achieve accurate, physically plausible forecasts on industrial benchmarks with over 99% mean conservation accuracy.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"DSPR decouples stable temporal patterns from regime-dependent residual dynamics to forecast industrial time series with high accuracy and physical consistency.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"80c653d6f1a0bafd49fdcf261c90d6af1ada24a7a4579f0a77a39dcf39c7c3b0"},"source":{"id":"2604.07393","kind":"arxiv","version":2},"verdict":{"id":"7df57715-5f0f-4ae7-9b2d-d922eec94555","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-10T18:09:01.243903Z","strongest_claim":"Experiments on four industrial benchmarks spanning heterogeneous regimes demonstrate that DSPR consistently improves forecasting accuracy and robustness under regime shifts while maintaining strong physical plausibility. It achieves state-of-the-art predictive performance, with Mean Conservation Accuracy exceeding 99% and Total Variation Ratio reaching up to 97.2%.","one_line_summary":"DSPR decouples statistical temporal evolution from physics-informed residual dynamics via an adaptive window for transport delays and a physics-guided dynamic graph to achieve accurate, physically plausible forecasts on industrial benchmarks with over 99% mean conservation accuracy.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the physical priors embedded in the Physics-Guided Dynamic Graph accurately reflect true regime-dependent interaction structures without introducing bias or suppressing valid correlations, and that the Adaptive Window module reliably estimates flow-dependent transport delays from data alone.","pith_extraction_headline":"DSPR decouples stable temporal patterns from regime-dependent residual dynamics to forecast industrial time series with high accuracy and physical consistency."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2604.07393/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":"7df57715-5f0f-4ae7-9b2d-d922eec94555"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-20T00:03:10Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UEQRPYuESuVFNSsFxeLDwJGMbdS/H8aAzfyoaVSPNrF8VnD9FaUiF8KUN65gkmQeuvQl4HXB5KBJfUPZYOnvAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-04T08:57:28.190206Z"},"content_sha256":"d2af006ddd82c8b163eed4d3af62e7b966b298e20964695ec49f89cc2e2aaf34","schema_version":"1.0","event_id":"sha256:d2af006ddd82c8b163eed4d3af62e7b966b298e20964695ec49f89cc2e2aaf34"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3YKV7I2H34WR43B4NPXWNM4OOY/bundle.json","state_url":"https://pith.science/pith/3YKV7I2H34WR43B4NPXWNM4OOY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3YKV7I2H34WR43B4NPXWNM4OOY/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":"2026-06-04T08:57:28Z","links":{"resolver":"https://pith.science/pith/3YKV7I2H34WR43B4NPXWNM4OOY","bundle":"https://pith.science/pith/3YKV7I2H34WR43B4NPXWNM4OOY/bundle.json","state":"https://pith.science/pith/3YKV7I2H34WR43B4NPXWNM4OOY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3YKV7I2H34WR43B4NPXWNM4OOY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:3YKV7I2H34WR43B4NPXWNM4OOY","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":"6c3c87eb83c105ccc080e77f1f952d2660022404e39510316e379225a09050f7","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2026-04-08T06:21:10Z","title_canon_sha256":"d9aa50c9a9c7444dc991a01e89555001dcfc9bbc3253776bb0cea9be387ad1e2"},"schema_version":"1.0","source":{"id":"2604.07393","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2604.07393","created_at":"2026-05-20T00:03:10Z"},{"alias_kind":"arxiv_version","alias_value":"2604.07393v2","created_at":"2026-05-20T00:03:10Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2604.07393","created_at":"2026-05-20T00:03:10Z"},{"alias_kind":"pith_short_12","alias_value":"3YKV7I2H34WR","created_at":"2026-05-20T00:03:10Z"},{"alias_kind":"pith_short_16","alias_value":"3YKV7I2H34WR43B4","created_at":"2026-05-20T00:03:10Z"},{"alias_kind":"pith_short_8","alias_value":"3YKV7I2H","created_at":"2026-05-20T00:03:10Z"}],"graph_snapshots":[{"event_id":"sha256:d2af006ddd82c8b163eed4d3af62e7b966b298e20964695ec49f89cc2e2aaf34","target":"graph","created_at":"2026-05-20T00:03:10Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"Experiments on four industrial benchmarks spanning heterogeneous regimes demonstrate that DSPR consistently improves forecasting accuracy and robustness under regime shifts while maintaining strong physical plausibility. It achieves state-of-the-art predictive performance, with Mean Conservation Accuracy exceeding 99% and Total Variation Ratio reaching up to 97.2%."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That the physical priors embedded in the Physics-Guided Dynamic Graph accurately reflect true regime-dependent interaction structures without introducing bias or suppressing valid correlations, and that the Adaptive Window module reliably estimates flow-dependent transport delays from data alone."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"DSPR decouples statistical temporal evolution from physics-informed residual dynamics via an adaptive window for transport delays and a physics-guided dynamic graph to achieve accurate, physically plausible forecasts on industrial benchmarks with over 99% mean conservation accuracy."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"DSPR decouples stable temporal patterns from regime-dependent residual dynamics to forecast industrial time series with high accuracy and physical consistency."}],"snapshot_sha256":"80c653d6f1a0bafd49fdcf261c90d6af1ada24a7a4579f0a77a39dcf39c7c3b0"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2604.07393/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Accurate forecasting of industrial time series requires balancing predictive accuracy with physical plausibility under non-stationary operating conditions. Existing data-driven models often achieve strong statistical performance but struggle to respect regime-dependent interaction structures and transport delays inherent in real-world systems. To address this challenge, we propose DSPR (Dual-Stream Physics-Residual Networks), a forecasting framework that explicitly decouples stable temporal patterns from regime-dependent residual dynamics. The first stream models the statistical temporal evolu","authors_text":"Guoqing Wang, Pengwei Yang, Tianyu Li, Yeran Zhang","cross_cats":["cs.AI"],"headline":"DSPR decouples stable temporal patterns from regime-dependent residual dynamics to forecast industrial time series with high accuracy and physical consistency.","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2026-04-08T06:21:10Z","title":"DSPR: Dual-Stream Physics-Residual Networks for Trustworthy Industrial Time Series Forecasting"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2604.07393","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-10T18:09:01.243903Z","id":"7df57715-5f0f-4ae7-9b2d-d922eec94555","model_set":{"reader":"grok-4.3"},"one_line_summary":"DSPR decouples statistical temporal evolution from physics-informed residual dynamics via an adaptive window for transport delays and a physics-guided dynamic graph to achieve accurate, physically plausible forecasts on industrial benchmarks with over 99% mean conservation accuracy.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"DSPR decouples stable temporal patterns from regime-dependent residual dynamics to forecast industrial time series with high accuracy and physical consistency.","strongest_claim":"Experiments on four industrial benchmarks spanning heterogeneous regimes demonstrate that DSPR consistently improves forecasting accuracy and robustness under regime shifts while maintaining strong physical plausibility. It achieves state-of-the-art predictive performance, with Mean Conservation Accuracy exceeding 99% and Total Variation Ratio reaching up to 97.2%.","weakest_assumption":"That the physical priors embedded in the Physics-Guided Dynamic Graph accurately reflect true regime-dependent interaction structures without introducing bias or suppressing valid correlations, and that the Adaptive Window module reliably estimates flow-dependent transport delays from data alone."}},"verdict_id":"7df57715-5f0f-4ae7-9b2d-d922eec94555"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:15221c7f333b97f220cfb657b4f2b464f6233a1a6b92996c06947395b03bb477","target":"record","created_at":"2026-05-20T00:03:10Z","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":"6c3c87eb83c105ccc080e77f1f952d2660022404e39510316e379225a09050f7","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2026-04-08T06:21:10Z","title_canon_sha256":"d9aa50c9a9c7444dc991a01e89555001dcfc9bbc3253776bb0cea9be387ad1e2"},"schema_version":"1.0","source":{"id":"2604.07393","kind":"arxiv","version":2}},"canonical_sha256":"de155fa347df2d1e6c3c6bef66b38e761ddcb789559333358ae5fcf39bdc5df8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"de155fa347df2d1e6c3c6bef66b38e761ddcb789559333358ae5fcf39bdc5df8","first_computed_at":"2026-05-20T00:03:10.708513Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:03:10.708513Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"y5z9vsEP8Osg8Nz64tcTQeNpqGurWx1fZJBs3pyDF2KFJq5l5Bd0yVe4Aa9ZNs+c5da2HwURjyMjN+j0sGqyAw==","signature_status":"signed_v1","signed_at":"2026-05-20T00:03:10.709406Z","signed_message":"canonical_sha256_bytes"},"source_id":"2604.07393","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:15221c7f333b97f220cfb657b4f2b464f6233a1a6b92996c06947395b03bb477","sha256:d2af006ddd82c8b163eed4d3af62e7b966b298e20964695ec49f89cc2e2aaf34"],"state_sha256":"f25be0690c0f321ce3abab9f438469388ab58b0bdf2d12b1397078d4bb97892d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6945pM4tdIKFNJqePheYJy4+sapk8pyB+ARkATwq+Y1rtxjIqA3LQbrRlqW3aFIBRyf9kKMYgac2EyrmH1cxAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-04T08:57:28.193842Z","bundle_sha256":"762e472311ca67b2fdaae3377590cd4405ab6ab7601b2ace9ee6016c52b0713b"}}