{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:J3JPPAUKETCW3EI2AMCSF3CIHN","short_pith_number":"pith:J3JPPAUK","canonical_record":{"source":{"id":"2606.11860","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-10T09:36:31Z","cross_cats_sorted":[],"title_canon_sha256":"b8e0adbbc2da98fdaa72faf75b0be87039eaad52eec3b0215e30f5ab3dec8588","abstract_canon_sha256":"9bb77b024d9048687ee7b17344688bd5de79e451c32fc0b9aa0d9e620c65bd79"},"schema_version":"1.0"},"canonical_sha256":"4ed2f7828a24c56d911a030522ec483b4ed5bb57bd0d7e3d3c835e9fc070ff70","source":{"kind":"arxiv","id":"2606.11860","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.11860","created_at":"2026-06-11T01:10:12Z"},{"alias_kind":"arxiv_version","alias_value":"2606.11860v1","created_at":"2026-06-11T01:10:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.11860","created_at":"2026-06-11T01:10:12Z"},{"alias_kind":"pith_short_12","alias_value":"J3JPPAUKETCW","created_at":"2026-06-11T01:10:12Z"},{"alias_kind":"pith_short_16","alias_value":"J3JPPAUKETCW3EI2","created_at":"2026-06-11T01:10:12Z"},{"alias_kind":"pith_short_8","alias_value":"J3JPPAUK","created_at":"2026-06-11T01:10:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:J3JPPAUKETCW3EI2AMCSF3CIHN","target":"record","payload":{"canonical_record":{"source":{"id":"2606.11860","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-10T09:36:31Z","cross_cats_sorted":[],"title_canon_sha256":"b8e0adbbc2da98fdaa72faf75b0be87039eaad52eec3b0215e30f5ab3dec8588","abstract_canon_sha256":"9bb77b024d9048687ee7b17344688bd5de79e451c32fc0b9aa0d9e620c65bd79"},"schema_version":"1.0"},"canonical_sha256":"4ed2f7828a24c56d911a030522ec483b4ed5bb57bd0d7e3d3c835e9fc070ff70","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-11T01:10:12.190619Z","signature_b64":"ye8fYjlcnbZvRrAbLPt395BjTWkVlfJAUdBYJnwjfKRUOlVdtAmfx4Lyi1FdYI7zTHLXUUeXWTJBg81q/aapAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4ed2f7828a24c56d911a030522ec483b4ed5bb57bd0d7e3d3c835e9fc070ff70","last_reissued_at":"2026-06-11T01:10:12.189694Z","signature_status":"signed_v1","first_computed_at":"2026-06-11T01:10:12.189694Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.11860","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":"2026-06-11T01:10:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Tuzn/MUd5RNpuj5U5r3wrEkCulMfiD3D0VzpMs5qzp0Xyb/Ug2lW/gF2rD/PQJ5OB5jSr1L3PAujup2S033WCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-24T08:08:27.406463Z"},"content_sha256":"b0e01443dc239695eb6c92be4d19e062c601160fb94a14df17e24ae1db9f55c4","schema_version":"1.0","event_id":"sha256:b0e01443dc239695eb6c92be4d19e062c601160fb94a14df17e24ae1db9f55c4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:J3JPPAUKETCW3EI2AMCSF3CIHN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"RePAIR: Predictive Self-Supervised Representation Learning in Chess","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Christoph Koller, Johannes F\\\"urnkranz, Timo Bertram","submitted_at":"2026-06-10T09:36:31Z","abstract_excerpt":"In this paper, we introduce Representation Prediction via Autoencoding using Iterative Refinement (RePAIR) - a novel self-supervised representation learning architecture that synthesizes Masked Autoencoders (MAE), Joint Embedding Predictive Architectures (JEPA), and Bidirectional Encoder Representations from Transformers (BERT). We demonstrate how it can be used to encode objects in sequential data like consecutive chess positions into compact yet meaningful representations. The basic principle of the architecture is to mask large portions of a sequence of latent states, similar to BERT and MA"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.11860","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/2606.11860/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":"2026-06-11T01:10:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WzF1XTLpMGI9/HJQJgStg54ehPdJwc7+uVRcNjPfoqoGzGKevqmbzUYn9VzKjHHipBsjtwXwzEIHcdvPxyDhBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-24T08:08:27.407143Z"},"content_sha256":"a7b1269fb5481161a99b75a1d80f723546cb0f617e68d15a45202c37670c351e","schema_version":"1.0","event_id":"sha256:a7b1269fb5481161a99b75a1d80f723546cb0f617e68d15a45202c37670c351e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/J3JPPAUKETCW3EI2AMCSF3CIHN/bundle.json","state_url":"https://pith.science/pith/J3JPPAUKETCW3EI2AMCSF3CIHN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/J3JPPAUKETCW3EI2AMCSF3CIHN/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":"2026-06-24T08:08:27Z","links":{"resolver":"https://pith.science/pith/J3JPPAUKETCW3EI2AMCSF3CIHN","bundle":"https://pith.science/pith/J3JPPAUKETCW3EI2AMCSF3CIHN/bundle.json","state":"https://pith.science/pith/J3JPPAUKETCW3EI2AMCSF3CIHN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/J3JPPAUKETCW3EI2AMCSF3CIHN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:J3JPPAUKETCW3EI2AMCSF3CIHN","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":"9bb77b024d9048687ee7b17344688bd5de79e451c32fc0b9aa0d9e620c65bd79","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-10T09:36:31Z","title_canon_sha256":"b8e0adbbc2da98fdaa72faf75b0be87039eaad52eec3b0215e30f5ab3dec8588"},"schema_version":"1.0","source":{"id":"2606.11860","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.11860","created_at":"2026-06-11T01:10:12Z"},{"alias_kind":"arxiv_version","alias_value":"2606.11860v1","created_at":"2026-06-11T01:10:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.11860","created_at":"2026-06-11T01:10:12Z"},{"alias_kind":"pith_short_12","alias_value":"J3JPPAUKETCW","created_at":"2026-06-11T01:10:12Z"},{"alias_kind":"pith_short_16","alias_value":"J3JPPAUKETCW3EI2","created_at":"2026-06-11T01:10:12Z"},{"alias_kind":"pith_short_8","alias_value":"J3JPPAUK","created_at":"2026-06-11T01:10:12Z"}],"graph_snapshots":[{"event_id":"sha256:a7b1269fb5481161a99b75a1d80f723546cb0f617e68d15a45202c37670c351e","target":"graph","created_at":"2026-06-11T01:10:12Z","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/2606.11860/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this paper, we introduce Representation Prediction via Autoencoding using Iterative Refinement (RePAIR) - a novel self-supervised representation learning architecture that synthesizes Masked Autoencoders (MAE), Joint Embedding Predictive Architectures (JEPA), and Bidirectional Encoder Representations from Transformers (BERT). We demonstrate how it can be used to encode objects in sequential data like consecutive chess positions into compact yet meaningful representations. The basic principle of the architecture is to mask large portions of a sequence of latent states, similar to BERT and MA","authors_text":"Christoph Koller, Johannes F\\\"urnkranz, Timo Bertram","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-10T09:36:31Z","title":"RePAIR: Predictive Self-Supervised Representation Learning in Chess"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.11860","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:b0e01443dc239695eb6c92be4d19e062c601160fb94a14df17e24ae1db9f55c4","target":"record","created_at":"2026-06-11T01:10:12Z","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":"9bb77b024d9048687ee7b17344688bd5de79e451c32fc0b9aa0d9e620c65bd79","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-10T09:36:31Z","title_canon_sha256":"b8e0adbbc2da98fdaa72faf75b0be87039eaad52eec3b0215e30f5ab3dec8588"},"schema_version":"1.0","source":{"id":"2606.11860","kind":"arxiv","version":1}},"canonical_sha256":"4ed2f7828a24c56d911a030522ec483b4ed5bb57bd0d7e3d3c835e9fc070ff70","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4ed2f7828a24c56d911a030522ec483b4ed5bb57bd0d7e3d3c835e9fc070ff70","first_computed_at":"2026-06-11T01:10:12.189694Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-11T01:10:12.189694Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ye8fYjlcnbZvRrAbLPt395BjTWkVlfJAUdBYJnwjfKRUOlVdtAmfx4Lyi1FdYI7zTHLXUUeXWTJBg81q/aapAg==","signature_status":"signed_v1","signed_at":"2026-06-11T01:10:12.190619Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.11860","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b0e01443dc239695eb6c92be4d19e062c601160fb94a14df17e24ae1db9f55c4","sha256:a7b1269fb5481161a99b75a1d80f723546cb0f617e68d15a45202c37670c351e"],"state_sha256":"cc238225cd65128d128386e51339dcb62ac7492ac8aa6d8a57abc0cbd91c5931"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oGjXVMobPUF5TsTgLYIcn/eXzhv8x/Tb3/joNDSzCxjlwluSf060d0aF4qnB26qB2//SxXyvy6sSIIEHjjF6Dw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-24T08:08:27.410814Z","bundle_sha256":"63f61aeb2aaed951475e0f8410c51f13070e3a5142357fd39d0e2a3bcd5d277a"}}