{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:XUHG4AGYVFC23ERMNG67PVPMVF","short_pith_number":"pith:XUHG4AGY","canonical_record":{"source":{"id":"2607.11530","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-07-13T13:15:18Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"f0e043387e1dbe2da657da4093dc89826caf1f2a8886aedf064c0f25a66c89e0","abstract_canon_sha256":"4e0a716ef85b61cfe2e180b5c49ddee3a341ee34a5a0638778da5bd97abafebf"},"schema_version":"1.0"},"canonical_sha256":"bd0e6e00d8a945ad922c69bdf7d5eca94c58a9d05937ce2a58687464e4469cd0","source":{"kind":"arxiv","id":"2607.11530","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.11530","created_at":"2026-07-14T02:22:09Z"},{"alias_kind":"arxiv_version","alias_value":"2607.11530v1","created_at":"2026-07-14T02:22:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.11530","created_at":"2026-07-14T02:22:09Z"},{"alias_kind":"pith_short_12","alias_value":"XUHG4AGYVFC2","created_at":"2026-07-14T02:22:09Z"},{"alias_kind":"pith_short_16","alias_value":"XUHG4AGYVFC23ERM","created_at":"2026-07-14T02:22:09Z"},{"alias_kind":"pith_short_8","alias_value":"XUHG4AGY","created_at":"2026-07-14T02:22:09Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:XUHG4AGYVFC23ERMNG67PVPMVF","target":"record","payload":{"canonical_record":{"source":{"id":"2607.11530","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-07-13T13:15:18Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"f0e043387e1dbe2da657da4093dc89826caf1f2a8886aedf064c0f25a66c89e0","abstract_canon_sha256":"4e0a716ef85b61cfe2e180b5c49ddee3a341ee34a5a0638778da5bd97abafebf"},"schema_version":"1.0"},"canonical_sha256":"bd0e6e00d8a945ad922c69bdf7d5eca94c58a9d05937ce2a58687464e4469cd0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-14T02:22:09.223096Z","signature_b64":"Zq60wJw1TFqOlku5IGKsaXRg9mbC4I09Ko0jr9CpIJUfrLY4tu3iffIuchIBIXdhUaHj3pqijwOtGuYMwF7HBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bd0e6e00d8a945ad922c69bdf7d5eca94c58a9d05937ce2a58687464e4469cd0","last_reissued_at":"2026-07-14T02:22:09.222295Z","signature_status":"signed_v1","first_computed_at":"2026-07-14T02:22:09.222295Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2607.11530","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":"2026-07-14T02:22:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"62KXx/BL+OvIe9o9F7L0ftuBbB5NpEBAZKcR7oGUbQCwgwqakCr69s7nKU/nutF5xs34NNC9v8bFwZTLY4AYDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T13:13:40.857494Z"},"content_sha256":"94b74feb8ae138fde1a704e3e2d32528cd5dc53b0f67c488e7cd5147f51364ec","schema_version":"1.0","event_id":"sha256:94b74feb8ae138fde1a704e3e2d32528cd5dc53b0f67c488e7cd5147f51364ec"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:XUHG4AGYVFC23ERMNG67PVPMVF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Residual Kinematic Corrections for Continuous Neural Decoding via Reinforcement Learning","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.AI","authors_text":"Attila Korik, Benjamin Metcalfe, Damien Coyle, Jiamian Li, Karl McCreadie, Leen Jabban, Naomi Du Bois, Niall McShane, \\\"Ozg\\\"ur \\c{S}im\\c{s}ek","submitted_at":"2026-07-13T13:15:18Z","abstract_excerpt":"Decoding continuous three-dimensional (3D) motor imagery (MI) using non-invasive electroencephalography (EEG)-based brain--computer interfaces (BCIs) remains challenging due to signal variability and residual decoding errors. Deep learning architectures such as convolutional neural network--long short-term memory (CNN--LSTM) models can capture spatial and temporal dynamics for continuous kinematic decoding; however, systematic residual errors persist in predicted trajectories. We propose a two-stage decoding framework that applies reinforcement learning (RL) to perform residual kinematic corre"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.11530","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/2607.11530/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":"2026-07-14T02:22:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WO2tvoLJxiii7VgcWSrnECOZo72XDotqIETSdvx3gB8AmPCh2C2iqg5fX+M58CBJEUf8x9KOtxamUze0yG9fBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T13:13:40.858042Z"},"content_sha256":"d45277cb3fe2682fab6212518676ec673f239d4a08c999e236897c5ac5031955","schema_version":"1.0","event_id":"sha256:d45277cb3fe2682fab6212518676ec673f239d4a08c999e236897c5ac5031955"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XUHG4AGYVFC23ERMNG67PVPMVF/bundle.json","state_url":"https://pith.science/pith/XUHG4AGYVFC23ERMNG67PVPMVF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XUHG4AGYVFC23ERMNG67PVPMVF/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":"2026-08-04T13:13:40Z","links":{"resolver":"https://pith.science/pith/XUHG4AGYVFC23ERMNG67PVPMVF","bundle":"https://pith.science/pith/XUHG4AGYVFC23ERMNG67PVPMVF/bundle.json","state":"https://pith.science/pith/XUHG4AGYVFC23ERMNG67PVPMVF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XUHG4AGYVFC23ERMNG67PVPMVF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:XUHG4AGYVFC23ERMNG67PVPMVF","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":"4e0a716ef85b61cfe2e180b5c49ddee3a341ee34a5a0638778da5bd97abafebf","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-07-13T13:15:18Z","title_canon_sha256":"f0e043387e1dbe2da657da4093dc89826caf1f2a8886aedf064c0f25a66c89e0"},"schema_version":"1.0","source":{"id":"2607.11530","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.11530","created_at":"2026-07-14T02:22:09Z"},{"alias_kind":"arxiv_version","alias_value":"2607.11530v1","created_at":"2026-07-14T02:22:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.11530","created_at":"2026-07-14T02:22:09Z"},{"alias_kind":"pith_short_12","alias_value":"XUHG4AGYVFC2","created_at":"2026-07-14T02:22:09Z"},{"alias_kind":"pith_short_16","alias_value":"XUHG4AGYVFC23ERM","created_at":"2026-07-14T02:22:09Z"},{"alias_kind":"pith_short_8","alias_value":"XUHG4AGY","created_at":"2026-07-14T02:22:09Z"}],"graph_snapshots":[{"event_id":"sha256:d45277cb3fe2682fab6212518676ec673f239d4a08c999e236897c5ac5031955","target":"graph","created_at":"2026-07-14T02:22:09Z","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/2607.11530/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Decoding continuous three-dimensional (3D) motor imagery (MI) using non-invasive electroencephalography (EEG)-based brain--computer interfaces (BCIs) remains challenging due to signal variability and residual decoding errors. Deep learning architectures such as convolutional neural network--long short-term memory (CNN--LSTM) models can capture spatial and temporal dynamics for continuous kinematic decoding; however, systematic residual errors persist in predicted trajectories. We propose a two-stage decoding framework that applies reinforcement learning (RL) to perform residual kinematic corre","authors_text":"Attila Korik, Benjamin Metcalfe, Damien Coyle, Jiamian Li, Karl McCreadie, Leen Jabban, Naomi Du Bois, Niall McShane, \\\"Ozg\\\"ur \\c{S}im\\c{s}ek","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-07-13T13:15:18Z","title":"Learning Residual Kinematic Corrections for Continuous Neural Decoding via Reinforcement Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.11530","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":"sha256:94b74feb8ae138fde1a704e3e2d32528cd5dc53b0f67c488e7cd5147f51364ec","target":"record","created_at":"2026-07-14T02:22:09Z","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":"4e0a716ef85b61cfe2e180b5c49ddee3a341ee34a5a0638778da5bd97abafebf","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2026-07-13T13:15:18Z","title_canon_sha256":"f0e043387e1dbe2da657da4093dc89826caf1f2a8886aedf064c0f25a66c89e0"},"schema_version":"1.0","source":{"id":"2607.11530","kind":"arxiv","version":1}},"canonical_sha256":"bd0e6e00d8a945ad922c69bdf7d5eca94c58a9d05937ce2a58687464e4469cd0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bd0e6e00d8a945ad922c69bdf7d5eca94c58a9d05937ce2a58687464e4469cd0","first_computed_at":"2026-07-14T02:22:09.222295Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-14T02:22:09.222295Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Zq60wJw1TFqOlku5IGKsaXRg9mbC4I09Ko0jr9CpIJUfrLY4tu3iffIuchIBIXdhUaHj3pqijwOtGuYMwF7HBg==","signature_status":"signed_v1","signed_at":"2026-07-14T02:22:09.223096Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.11530","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:94b74feb8ae138fde1a704e3e2d32528cd5dc53b0f67c488e7cd5147f51364ec","sha256:d45277cb3fe2682fab6212518676ec673f239d4a08c999e236897c5ac5031955"],"state_sha256":"df7a3ba2e3c14b45bfa4c5912d39acda28a0aa4b0997581ed5c63ff783bcb2cc"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pVBpS7UYwvck95SO392sPAUNSm8gi+vR/buJNhOGSx6nDKo8fs5Tao2p50xVHMj2uyvMCU1hyFPvo6TgxvZXBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T13:13:40.864633Z","bundle_sha256":"26109977497f5b6c2a55faa3f9930f33bc57d5ba29ad980f83eef32f3fb57a97"}}