{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:XCEBT2SSSMDFMUJD3EYWZLK2WO","short_pith_number":"pith:XCEBT2SS","canonical_record":{"source":{"id":"2506.01052","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-01T15:39:00Z","cross_cats_sorted":["math.OC","stat.ML"],"title_canon_sha256":"4250fba860983b2f8fc5e5eb47c7002baa8fe3f6eb3eaf0e0bf647e87cf901bb","abstract_canon_sha256":"40af841338c9e9b345b2865d2d2f48b7912a2c658f413d6c67f65cdb39dbc85b"},"schema_version":"1.0"},"canonical_sha256":"b88819ea529306565123d9316cad5ab3bab890adeb3d1f137747f57c6c83d681","source":{"kind":"arxiv","id":"2506.01052","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.01052","created_at":"2026-06-09T02:07:04Z"},{"alias_kind":"arxiv_version","alias_value":"2506.01052v3","created_at":"2026-06-09T02:07:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.01052","created_at":"2026-06-09T02:07:04Z"},{"alias_kind":"pith_short_12","alias_value":"XCEBT2SSSMDF","created_at":"2026-06-09T02:07:04Z"},{"alias_kind":"pith_short_16","alias_value":"XCEBT2SSSMDFMUJD","created_at":"2026-06-09T02:07:04Z"},{"alias_kind":"pith_short_8","alias_value":"XCEBT2SS","created_at":"2026-06-09T02:07:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:XCEBT2SSSMDFMUJD3EYWZLK2WO","target":"record","payload":{"canonical_record":{"source":{"id":"2506.01052","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-01T15:39:00Z","cross_cats_sorted":["math.OC","stat.ML"],"title_canon_sha256":"4250fba860983b2f8fc5e5eb47c7002baa8fe3f6eb3eaf0e0bf647e87cf901bb","abstract_canon_sha256":"40af841338c9e9b345b2865d2d2f48b7912a2c658f413d6c67f65cdb39dbc85b"},"schema_version":"1.0"},"canonical_sha256":"b88819ea529306565123d9316cad5ab3bab890adeb3d1f137747f57c6c83d681","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-09T02:07:04.206485Z","signature_b64":"DBiefv1IO2zgJNgEnselO+xgTW+6us3GUcdzp+ZS9PeGfz0vuPlWTH4af+4ELtcG5dFq+5LuPZJs+tCPT9nPCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b88819ea529306565123d9316cad5ab3bab890adeb3d1f137747f57c6c83d681","last_reissued_at":"2026-06-09T02:07:04.205396Z","signature_status":"signed_v1","first_computed_at":"2026-06-09T02:07:04.205396Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.01052","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-09T02:07:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"y3mZwLCWg1rp0yPd2ZHUIDya0nbsPRipH4xtMws88ZKrmyyiJ2KlTLyjsV9lnYC7fyTP9OTmzpIhJngt4Q6sAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T22:38:04.470248Z"},"content_sha256":"702bf97984ab9f0abf285d2dd2525ee5153ce7852ecae564e9f40d45012e7774","schema_version":"1.0","event_id":"sha256:702bf97984ab9f0abf285d2dd2525ee5153ce7852ecae564e9f40d45012e7774"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:XCEBT2SSSMDFMUJD3EYWZLK2WO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Robust $\\widetilde{\\mathcal{O}}(1/\\sqrt{T})$ Rate for Unprojected TD Learning with Linear Function Approximation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["math.OC","stat.ML"],"primary_cat":"cs.LG","authors_text":"Francesco Orabona, Wei-Cheng Lee","submitted_at":"2025-06-01T15:39:00Z","abstract_excerpt":"We investigate the finite-time convergence properties of Temporal Difference (TD) learning with linear function approximation, a cornerstone of reinforcement learning.\n  We are interested in the so-called ``robust'' setting, where the convergence guarantee does not depend on the potential function's minimal curvature.\n  While prior work has established convergence guarantees in this setting, these results typically rely on the artificial assumption that each iterate is projected onto a bounded set. Removing such a condition was left as an open problem by Bhandari et al. (COLT'18), hypothesizin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.01052","kind":"arxiv","version":3},"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/2506.01052/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":"2026-06-09T02:07:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Vejl7O27zf6czuJERmcRAlNktRV8XYjF9WoOxv0+IefJSVuXpCTmkKgrA79FPUwUkUlvdSlcjO9V4eWwMfddBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T22:38:04.471155Z"},"content_sha256":"fd059014030d4de64ad43862ff615c5c69154c0c41a13deb267c17573847c9a1","schema_version":"1.0","event_id":"sha256:fd059014030d4de64ad43862ff615c5c69154c0c41a13deb267c17573847c9a1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XCEBT2SSSMDFMUJD3EYWZLK2WO/bundle.json","state_url":"https://pith.science/pith/XCEBT2SSSMDFMUJD3EYWZLK2WO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XCEBT2SSSMDFMUJD3EYWZLK2WO/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":"2026-06-11T22:38:04Z","links":{"resolver":"https://pith.science/pith/XCEBT2SSSMDFMUJD3EYWZLK2WO","bundle":"https://pith.science/pith/XCEBT2SSSMDFMUJD3EYWZLK2WO/bundle.json","state":"https://pith.science/pith/XCEBT2SSSMDFMUJD3EYWZLK2WO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XCEBT2SSSMDFMUJD3EYWZLK2WO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:XCEBT2SSSMDFMUJD3EYWZLK2WO","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":"40af841338c9e9b345b2865d2d2f48b7912a2c658f413d6c67f65cdb39dbc85b","cross_cats_sorted":["math.OC","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-01T15:39:00Z","title_canon_sha256":"4250fba860983b2f8fc5e5eb47c7002baa8fe3f6eb3eaf0e0bf647e87cf901bb"},"schema_version":"1.0","source":{"id":"2506.01052","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.01052","created_at":"2026-06-09T02:07:04Z"},{"alias_kind":"arxiv_version","alias_value":"2506.01052v3","created_at":"2026-06-09T02:07:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.01052","created_at":"2026-06-09T02:07:04Z"},{"alias_kind":"pith_short_12","alias_value":"XCEBT2SSSMDF","created_at":"2026-06-09T02:07:04Z"},{"alias_kind":"pith_short_16","alias_value":"XCEBT2SSSMDFMUJD","created_at":"2026-06-09T02:07:04Z"},{"alias_kind":"pith_short_8","alias_value":"XCEBT2SS","created_at":"2026-06-09T02:07:04Z"}],"graph_snapshots":[{"event_id":"sha256:fd059014030d4de64ad43862ff615c5c69154c0c41a13deb267c17573847c9a1","target":"graph","created_at":"2026-06-09T02:07:04Z","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/2506.01052/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We investigate the finite-time convergence properties of Temporal Difference (TD) learning with linear function approximation, a cornerstone of reinforcement learning.\n  We are interested in the so-called ``robust'' setting, where the convergence guarantee does not depend on the potential function's minimal curvature.\n  While prior work has established convergence guarantees in this setting, these results typically rely on the artificial assumption that each iterate is projected onto a bounded set. Removing such a condition was left as an open problem by Bhandari et al. (COLT'18), hypothesizin","authors_text":"Francesco Orabona, Wei-Cheng Lee","cross_cats":["math.OC","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-01T15:39:00Z","title":"A Robust $\\widetilde{\\mathcal{O}}(1/\\sqrt{T})$ Rate for Unprojected TD Learning with Linear Function Approximation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.01052","kind":"arxiv","version":3},"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":"sha256:702bf97984ab9f0abf285d2dd2525ee5153ce7852ecae564e9f40d45012e7774","target":"record","created_at":"2026-06-09T02:07:04Z","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":"40af841338c9e9b345b2865d2d2f48b7912a2c658f413d6c67f65cdb39dbc85b","cross_cats_sorted":["math.OC","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-06-01T15:39:00Z","title_canon_sha256":"4250fba860983b2f8fc5e5eb47c7002baa8fe3f6eb3eaf0e0bf647e87cf901bb"},"schema_version":"1.0","source":{"id":"2506.01052","kind":"arxiv","version":3}},"canonical_sha256":"b88819ea529306565123d9316cad5ab3bab890adeb3d1f137747f57c6c83d681","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b88819ea529306565123d9316cad5ab3bab890adeb3d1f137747f57c6c83d681","first_computed_at":"2026-06-09T02:07:04.205396Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-09T02:07:04.205396Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"DBiefv1IO2zgJNgEnselO+xgTW+6us3GUcdzp+ZS9PeGfz0vuPlWTH4af+4ELtcG5dFq+5LuPZJs+tCPT9nPCw==","signature_status":"signed_v1","signed_at":"2026-06-09T02:07:04.206485Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.01052","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:702bf97984ab9f0abf285d2dd2525ee5153ce7852ecae564e9f40d45012e7774","sha256:fd059014030d4de64ad43862ff615c5c69154c0c41a13deb267c17573847c9a1"],"state_sha256":"418d5b6ca9c115a969052da11b04b740f0c783ef385773fc183b4c49fb804310"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"134DMpLzbRB2rY9o9BekYgvCQYgdKw7ZdgUpDL+yQQ5VS35o65xqjW5wb7m0avQbdKo4cTcB4gH4+ISqijoBDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-11T22:38:04.475674Z","bundle_sha256":"2cb281b0b05bc9dbf9f1ebd00e41c7645f98ba190fb926ed662eca9f7687a5e5"}}