{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:J2HFC5A7B2THFSX733XNEE5SB3","short_pith_number":"pith:J2HFC5A7","canonical_record":{"source":{"id":"2601.10100","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2026-01-15T06:11:44Z","cross_cats_sorted":["stat.ME","stat.TH"],"title_canon_sha256":"b94675c3e0c97a8893485e85873d26cd3f7e30051b09776007e1fe32aba3a9f3","abstract_canon_sha256":"c549c54441484ffba9aed3178a11631ee8afeb2b79abc14b4ca07ae069708964"},"schema_version":"1.0"},"canonical_sha256":"4e8e51741f0ea672caffdeeed213b20ed304314b0f9e18089a1ec00b8c31a041","source":{"kind":"arxiv","id":"2601.10100","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2601.10100","created_at":"2026-05-20T00:04:22Z"},{"alias_kind":"arxiv_version","alias_value":"2601.10100v3","created_at":"2026-05-20T00:04:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.10100","created_at":"2026-05-20T00:04:22Z"},{"alias_kind":"pith_short_12","alias_value":"J2HFC5A7B2TH","created_at":"2026-05-20T00:04:22Z"},{"alias_kind":"pith_short_16","alias_value":"J2HFC5A7B2THFSX7","created_at":"2026-05-20T00:04:22Z"},{"alias_kind":"pith_short_8","alias_value":"J2HFC5A7","created_at":"2026-05-20T00:04:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:J2HFC5A7B2THFSX733XNEE5SB3","target":"record","payload":{"canonical_record":{"source":{"id":"2601.10100","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2026-01-15T06:11:44Z","cross_cats_sorted":["stat.ME","stat.TH"],"title_canon_sha256":"b94675c3e0c97a8893485e85873d26cd3f7e30051b09776007e1fe32aba3a9f3","abstract_canon_sha256":"c549c54441484ffba9aed3178a11631ee8afeb2b79abc14b4ca07ae069708964"},"schema_version":"1.0"},"canonical_sha256":"4e8e51741f0ea672caffdeeed213b20ed304314b0f9e18089a1ec00b8c31a041","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:04:22.173168Z","signature_b64":"eTRDJnj7M+rZEZuz+mOKSIs1l5nNX47CCGOlwMAAL3wfaH72XbDNoaqTzslfsY86faOdywL7jxeokEgrJd3cAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4e8e51741f0ea672caffdeeed213b20ed304314b0f9e18089a1ec00b8c31a041","last_reissued_at":"2026-05-20T00:04:22.172167Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:04:22.172167Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2601.10100","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":"2026-05-20T00:04:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Fz94MhEzPj37go2qH55ENW4FyRZ9iABNmcLzRNcAwpqv96pYttuHEJON8YyBzEBLKSXgwlazfSxRY1BMjjPLDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T03:43:04.445486Z"},"content_sha256":"1566896df84ac0e26335d5ab377b223f261674a6242ae71795b72c11c13c24d4","schema_version":"1.0","event_id":"sha256:1566896df84ac0e26335d5ab377b223f261674a6242ae71795b72c11c13c24d4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:J2HFC5A7B2THFSX733XNEE5SB3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Prediction Suboptimality of the Lasso in Sparse Linear Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME","stat.TH"],"primary_cat":"math.ST","authors_text":"Guo Liu (Waseda University)","submitted_at":"2026-01-15T06:11:44Z","abstract_excerpt":"The choice of the tuning parameter in the Lasso is central to its statistical performance in high-dimensional linear regression. In this work, we study tuning regimes under which the Lasso exhibits suboptimal prediction performance, in the sense that a simple refinement improves upon it both on high-probability events and in mean squared prediction error. Our analysis shows that the relevant stochastic scale is governed by Gaussian maxima on the selected or localized support, which may be more informative than the universal rate in Lasso theory. We further illustrate how structural factors in "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.10100","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/2601.10100/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":"2026-05-20T00:04:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pnn3cmmcT4a00w0sBB1i9YvsH31uBB9nxswRCYqvK6lCAfqH27JPZXDi4EK0hAlRzR/CCsjUIxF7ED7OyXv3AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T03:43:04.446203Z"},"content_sha256":"e539e7ee3560899a3ecbfab09b3268bab66894a6ee18e6fe931b0b21c7cf7520","schema_version":"1.0","event_id":"sha256:e539e7ee3560899a3ecbfab09b3268bab66894a6ee18e6fe931b0b21c7cf7520"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/J2HFC5A7B2THFSX733XNEE5SB3/bundle.json","state_url":"https://pith.science/pith/J2HFC5A7B2THFSX733XNEE5SB3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/J2HFC5A7B2THFSX733XNEE5SB3/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":"2026-06-11T03:43:04Z","links":{"resolver":"https://pith.science/pith/J2HFC5A7B2THFSX733XNEE5SB3","bundle":"https://pith.science/pith/J2HFC5A7B2THFSX733XNEE5SB3/bundle.json","state":"https://pith.science/pith/J2HFC5A7B2THFSX733XNEE5SB3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/J2HFC5A7B2THFSX733XNEE5SB3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:J2HFC5A7B2THFSX733XNEE5SB3","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":"c549c54441484ffba9aed3178a11631ee8afeb2b79abc14b4ca07ae069708964","cross_cats_sorted":["stat.ME","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2026-01-15T06:11:44Z","title_canon_sha256":"b94675c3e0c97a8893485e85873d26cd3f7e30051b09776007e1fe32aba3a9f3"},"schema_version":"1.0","source":{"id":"2601.10100","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2601.10100","created_at":"2026-05-20T00:04:22Z"},{"alias_kind":"arxiv_version","alias_value":"2601.10100v3","created_at":"2026-05-20T00:04:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.10100","created_at":"2026-05-20T00:04:22Z"},{"alias_kind":"pith_short_12","alias_value":"J2HFC5A7B2TH","created_at":"2026-05-20T00:04:22Z"},{"alias_kind":"pith_short_16","alias_value":"J2HFC5A7B2THFSX7","created_at":"2026-05-20T00:04:22Z"},{"alias_kind":"pith_short_8","alias_value":"J2HFC5A7","created_at":"2026-05-20T00:04:22Z"}],"graph_snapshots":[{"event_id":"sha256:e539e7ee3560899a3ecbfab09b3268bab66894a6ee18e6fe931b0b21c7cf7520","target":"graph","created_at":"2026-05-20T00:04:22Z","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/2601.10100/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The choice of the tuning parameter in the Lasso is central to its statistical performance in high-dimensional linear regression. In this work, we study tuning regimes under which the Lasso exhibits suboptimal prediction performance, in the sense that a simple refinement improves upon it both on high-probability events and in mean squared prediction error. Our analysis shows that the relevant stochastic scale is governed by Gaussian maxima on the selected or localized support, which may be more informative than the universal rate in Lasso theory. We further illustrate how structural factors in ","authors_text":"Guo Liu (Waseda University)","cross_cats":["stat.ME","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2026-01-15T06:11:44Z","title":"Prediction Suboptimality of the Lasso in Sparse Linear Regression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.10100","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":"sha256:1566896df84ac0e26335d5ab377b223f261674a6242ae71795b72c11c13c24d4","target":"record","created_at":"2026-05-20T00:04:22Z","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":"c549c54441484ffba9aed3178a11631ee8afeb2b79abc14b4ca07ae069708964","cross_cats_sorted":["stat.ME","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2026-01-15T06:11:44Z","title_canon_sha256":"b94675c3e0c97a8893485e85873d26cd3f7e30051b09776007e1fe32aba3a9f3"},"schema_version":"1.0","source":{"id":"2601.10100","kind":"arxiv","version":3}},"canonical_sha256":"4e8e51741f0ea672caffdeeed213b20ed304314b0f9e18089a1ec00b8c31a041","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4e8e51741f0ea672caffdeeed213b20ed304314b0f9e18089a1ec00b8c31a041","first_computed_at":"2026-05-20T00:04:22.172167Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:04:22.172167Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"eTRDJnj7M+rZEZuz+mOKSIs1l5nNX47CCGOlwMAAL3wfaH72XbDNoaqTzslfsY86faOdywL7jxeokEgrJd3cAA==","signature_status":"signed_v1","signed_at":"2026-05-20T00:04:22.173168Z","signed_message":"canonical_sha256_bytes"},"source_id":"2601.10100","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1566896df84ac0e26335d5ab377b223f261674a6242ae71795b72c11c13c24d4","sha256:e539e7ee3560899a3ecbfab09b3268bab66894a6ee18e6fe931b0b21c7cf7520"],"state_sha256":"114184eef10c16a964af4846dc8ecf882c218d3bc66c175cab972ed56e0b11da"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"166AoyOZ79MqhDnL+hqtgwf0nbwl23EYGsV/7eK58lpXOTnDvu0wGcJtnpuSRJhI2fio+w+r/lXNsjjslLK6DA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-11T03:43:04.450001Z","bundle_sha256":"c3ed68af2e4921002513f8b69aeea005906b61b9bab9f0138f1c3d60bd09f8c7"}}