{"paper":{"title":"ALETHEIA: Autonomous Loop for Experimental Theory and HEP Inference Across-data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"hep-ex","authors_text":"Vincent Alexander Croft","submitted_at":"2026-06-09T15:59:36Z","abstract_excerpt":"ALETHEIA is a self-completing tool for monitoring the learning of manifolds in physics foundation models from data. It provides a method to automatically build physics foundation models for permutation-invariant per-event representations of unknown physics manifolds. This process is demonstrated here for dimension-six Standard Model Effective Field Theory (SMEFT) content of four operators in neutral-current Drell-Yan, whose input is unordered event-level features, and we drive it with an active-learning loop that separates two jobs that the literature usually conflates. Active learning complet"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.11024","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/2606.11024/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"}