{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:TLDVSA5QR4674W35GAD5KVXSWG","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":"9944203cbfa3e40dbfaa7ef6e192a667e74053d5a36362bad43b962421b07c86","cross_cats_sorted":["cond-mat.mtrl-sci","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-06-06T05:37:49Z","title_canon_sha256":"1c983f7be3adeb0153caab23317a47d857a0381ae91d76e797cc31ef0e100bca"},"schema_version":"1.0","source":{"id":"2506.17242","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.17242","created_at":"2026-07-05T11:25:14Z"},{"alias_kind":"arxiv_version","alias_value":"2506.17242v1","created_at":"2026-07-05T11:25:14Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.17242","created_at":"2026-07-05T11:25:14Z"},{"alias_kind":"pith_short_12","alias_value":"TLDVSA5QR467","created_at":"2026-07-05T11:25:14Z"},{"alias_kind":"pith_short_16","alias_value":"TLDVSA5QR4674W35","created_at":"2026-07-05T11:25:14Z"},{"alias_kind":"pith_short_8","alias_value":"TLDVSA5Q","created_at":"2026-07-05T11:25:14Z"}],"graph_snapshots":[{"event_id":"sha256:f08e45a7c77438fd7eee0eb7df44cfb2cf3e07c1747a061c8165e4907d746fd9","target":"graph","created_at":"2026-07-05T11:25:14Z","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/2506.17242/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multi-well potentials are ubiquitous in science, modeling phenomena such as phase transitions, dynamic instabilities, and multimodal behavior across physics, chemistry, and biology. In contrast to non-smooth minimum-of-mixture representations, we propose a differentiable and convex formulation based on a log-sum-exponential (LSE) mixture of input convex neural network (ICNN) modes. This log-sum-exponential input convex neural network (LSE-ICNN) provides a smooth surrogate that retains convexity within basins and allows for gradient-based learning and inference.\n  A key feature of the LSE-ICNN ","authors_text":"Adrian Buganza Tepole, Jan N. Fuhg, Reese E. Jones","cross_cats":["cond-mat.mtrl-sci","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-06-06T05:37:49Z","title":"Differentiable neural network representation of multi-well, locally-convex potentials"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.17242","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:cf389fea76c2342da026d4b3e15bb60ebb48c594a292af039d206274ed49befd","target":"record","created_at":"2026-07-05T11:25:14Z","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":"9944203cbfa3e40dbfaa7ef6e192a667e74053d5a36362bad43b962421b07c86","cross_cats_sorted":["cond-mat.mtrl-sci","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2025-06-06T05:37:49Z","title_canon_sha256":"1c983f7be3adeb0153caab23317a47d857a0381ae91d76e797cc31ef0e100bca"},"schema_version":"1.0","source":{"id":"2506.17242","kind":"arxiv","version":1}},"canonical_sha256":"9ac75903b08f3dfe5b7d3007d556f2b1901c5feb0363ff23fba757c8e4f2635d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9ac75903b08f3dfe5b7d3007d556f2b1901c5feb0363ff23fba757c8e4f2635d","first_computed_at":"2026-07-05T11:25:14.133526Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:25:14.133526Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"r1/aSYSN4DG+ZCwd+hpXhjs0vg+8YFOjVbfuueceGkC5LISqz8/Bx4vL/gfHWNoDXGaDLlnzThRQy8fwTdZXBw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:25:14.133995Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.17242","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:cf389fea76c2342da026d4b3e15bb60ebb48c594a292af039d206274ed49befd","sha256:f08e45a7c77438fd7eee0eb7df44cfb2cf3e07c1747a061c8165e4907d746fd9"],"state_sha256":"943e9f10ba50789775fdc9dac785f9669590e504700b3d96e893f022016af621"}