{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:CTZRFHNNFL5PBUPJUMU6QLYY2W","short_pith_number":"pith:CTZRFHNN","canonical_record":{"source":{"id":"2605.16520","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-15T18:14:38Z","cross_cats_sorted":[],"title_canon_sha256":"0ee3a777f258d5163bddc72483a5e34197e61f122871ef3c28497e89734191b1","abstract_canon_sha256":"6efad944643158de319aca57d979d5d7e2b8b22516833b8a69c046bf82baa65f"},"schema_version":"1.0"},"canonical_sha256":"14f3129dad2afaf0d1e9a329e82f18d5be28b4cc355c74cf70de29417d2ea0b8","source":{"kind":"arxiv","id":"2605.16520","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.16520","created_at":"2026-05-20T00:02:27Z"},{"alias_kind":"arxiv_version","alias_value":"2605.16520v1","created_at":"2026-05-20T00:02:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.16520","created_at":"2026-05-20T00:02:27Z"},{"alias_kind":"pith_short_12","alias_value":"CTZRFHNNFL5P","created_at":"2026-05-20T00:02:27Z"},{"alias_kind":"pith_short_16","alias_value":"CTZRFHNNFL5PBUPJ","created_at":"2026-05-20T00:02:27Z"},{"alias_kind":"pith_short_8","alias_value":"CTZRFHNN","created_at":"2026-05-20T00:02:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:CTZRFHNNFL5PBUPJUMU6QLYY2W","target":"record","payload":{"canonical_record":{"source":{"id":"2605.16520","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-15T18:14:38Z","cross_cats_sorted":[],"title_canon_sha256":"0ee3a777f258d5163bddc72483a5e34197e61f122871ef3c28497e89734191b1","abstract_canon_sha256":"6efad944643158de319aca57d979d5d7e2b8b22516833b8a69c046bf82baa65f"},"schema_version":"1.0"},"canonical_sha256":"14f3129dad2afaf0d1e9a329e82f18d5be28b4cc355c74cf70de29417d2ea0b8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:02:27.091905Z","signature_b64":"Xf+EepXpQ6PaHd89/J7AkyXQBwK78ZLLsF+PW3EDhFO2+6ZMAEoWr61TOlik7DR5LKeA3QEhbom0cH92m2lrCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"14f3129dad2afaf0d1e9a329e82f18d5be28b4cc355c74cf70de29417d2ea0b8","last_reissued_at":"2026-05-20T00:02:27.091145Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:02:27.091145Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.16520","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":"2026-05-20T00:02:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NxQk9FLN+9vxAOtBwPnk0HEx+81gQcnq7zEQWWpQQD2zv5Vw7OI+4abnh3S5vyENlrnuQIfbrYcs6vR/dV3cCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T06:49:17.400655Z"},"content_sha256":"c3edbb10ad8ecc66302a1e54698f5a922c03e24c76037e12a158d1b60ae3db2b","schema_version":"1.0","event_id":"sha256:c3edbb10ad8ecc66302a1e54698f5a922c03e24c76037e12a158d1b60ae3db2b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:CTZRFHNNFL5PBUPJUMU6QLYY2W","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Global Convergence of Sampling-Based Nonconvex Optimization through Diffusion-Style Smoothing","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Chaoyi Pan, Guannan Qu, Guanya Shi, Zeji Yi","submitted_at":"2026-05-15T18:14:38Z","abstract_excerpt":"Sampling-based optimization (SBO), like cross-entropy method and evolutionary algorithms, has achieved many successes in solving non-convex problems without gradients, yet its convergence is poorly understood. In this paper, we establish a non-asymptotic convergence analysis for SBO through the lens of smoothing. Specifically, we recast SBO as gradient descent on a smoothed objective, mirroring noise-conditioned score ascent in diffusion models. Our first contribution is a landscape analysis of the smoothed objective, demonstrating how smoothing helps escape local minima and uncovering a funda"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.16520","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/2605.16520/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"ai_meta_artifact","ran_at":"2026-05-19T19:33:23.081764Z","status":"skipped","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T19:21:56.949830Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"90719cd96da22f686e5f97d5dfdeec0797e796e80052af70078e61576b59a0f6"},"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":"2026-05-20T00:02:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"n7UOx3kNMMFI+jpM1FqywA6RFqIMTLLkA8PxK4Q7kX8L3rqegKfpd81KkptfFY+bGQS6C0ez59pKCHEuKzIFDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T06:49:17.401475Z"},"content_sha256":"a49c8d8849882e00dcfafe58d725ad8ee585092208182967d1e7cd3e2c4603a1","schema_version":"1.0","event_id":"sha256:a49c8d8849882e00dcfafe58d725ad8ee585092208182967d1e7cd3e2c4603a1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CTZRFHNNFL5PBUPJUMU6QLYY2W/bundle.json","state_url":"https://pith.science/pith/CTZRFHNNFL5PBUPJUMU6QLYY2W/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CTZRFHNNFL5PBUPJUMU6QLYY2W/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":"2026-05-23T06:49:17Z","links":{"resolver":"https://pith.science/pith/CTZRFHNNFL5PBUPJUMU6QLYY2W","bundle":"https://pith.science/pith/CTZRFHNNFL5PBUPJUMU6QLYY2W/bundle.json","state":"https://pith.science/pith/CTZRFHNNFL5PBUPJUMU6QLYY2W/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CTZRFHNNFL5PBUPJUMU6QLYY2W/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:CTZRFHNNFL5PBUPJUMU6QLYY2W","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":"6efad944643158de319aca57d979d5d7e2b8b22516833b8a69c046bf82baa65f","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-15T18:14:38Z","title_canon_sha256":"0ee3a777f258d5163bddc72483a5e34197e61f122871ef3c28497e89734191b1"},"schema_version":"1.0","source":{"id":"2605.16520","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.16520","created_at":"2026-05-20T00:02:27Z"},{"alias_kind":"arxiv_version","alias_value":"2605.16520v1","created_at":"2026-05-20T00:02:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.16520","created_at":"2026-05-20T00:02:27Z"},{"alias_kind":"pith_short_12","alias_value":"CTZRFHNNFL5P","created_at":"2026-05-20T00:02:27Z"},{"alias_kind":"pith_short_16","alias_value":"CTZRFHNNFL5PBUPJ","created_at":"2026-05-20T00:02:27Z"},{"alias_kind":"pith_short_8","alias_value":"CTZRFHNN","created_at":"2026-05-20T00:02:27Z"}],"graph_snapshots":[{"event_id":"sha256:a49c8d8849882e00dcfafe58d725ad8ee585092208182967d1e7cd3e2c4603a1","target":"graph","created_at":"2026-05-20T00:02:27Z","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":[{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T19:33:23.081764Z","status":"skipped","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T19:21:56.949830Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2605.16520/integrity.json","findings":[],"snapshot_sha256":"90719cd96da22f686e5f97d5dfdeec0797e796e80052af70078e61576b59a0f6","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Sampling-based optimization (SBO), like cross-entropy method and evolutionary algorithms, has achieved many successes in solving non-convex problems without gradients, yet its convergence is poorly understood. In this paper, we establish a non-asymptotic convergence analysis for SBO through the lens of smoothing. Specifically, we recast SBO as gradient descent on a smoothed objective, mirroring noise-conditioned score ascent in diffusion models. Our first contribution is a landscape analysis of the smoothed objective, demonstrating how smoothing helps escape local minima and uncovering a funda","authors_text":"Chaoyi Pan, Guannan Qu, Guanya Shi, Zeji Yi","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-15T18:14:38Z","title":"Global Convergence of Sampling-Based Nonconvex Optimization through Diffusion-Style Smoothing"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.16520","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:c3edbb10ad8ecc66302a1e54698f5a922c03e24c76037e12a158d1b60ae3db2b","target":"record","created_at":"2026-05-20T00:02:27Z","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":"6efad944643158de319aca57d979d5d7e2b8b22516833b8a69c046bf82baa65f","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-15T18:14:38Z","title_canon_sha256":"0ee3a777f258d5163bddc72483a5e34197e61f122871ef3c28497e89734191b1"},"schema_version":"1.0","source":{"id":"2605.16520","kind":"arxiv","version":1}},"canonical_sha256":"14f3129dad2afaf0d1e9a329e82f18d5be28b4cc355c74cf70de29417d2ea0b8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"14f3129dad2afaf0d1e9a329e82f18d5be28b4cc355c74cf70de29417d2ea0b8","first_computed_at":"2026-05-20T00:02:27.091145Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:02:27.091145Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Xf+EepXpQ6PaHd89/J7AkyXQBwK78ZLLsF+PW3EDhFO2+6ZMAEoWr61TOlik7DR5LKeA3QEhbom0cH92m2lrCw==","signature_status":"signed_v1","signed_at":"2026-05-20T00:02:27.091905Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.16520","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c3edbb10ad8ecc66302a1e54698f5a922c03e24c76037e12a158d1b60ae3db2b","sha256:a49c8d8849882e00dcfafe58d725ad8ee585092208182967d1e7cd3e2c4603a1"],"state_sha256":"4139fe1e3bcfbf5450d3e9f87e8a3a3bdc6e758fb58d8ad9e5969a5a73025fb1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4MjRJmlzUOXkbLdHZk7JAetJEq0kcxj2XS68rrCSb+eJh5kK3fzfrTL+LAIDjWe9ohQC1zZmm184tHeUEBelCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-23T06:49:17.405040Z","bundle_sha256":"1336fc517093c31b929bce4943238be8701bc283209e5d7ddc4828ca6a6b9939"}}