{"ID":2827338,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.19744","arxiv_id":"2512.19744","title":"DeepBridge: A Unified and Production-Ready Framework for Multi-Dimensional Machine Learning Validation","abstract":"We present DeepBridge, an 80K-line Python library that unifies multi-dimensional validation, automatic compliance verification, knowledge distillation, and synthetic data generation. DeepBridge offers: (i) 5 validation suites (fairness with 15 metrics, robustness with weakness detection, uncertainty via conformal prediction, resilience with 5 drift types, hyperparameter sensitivity), (ii) automatic EEOC/ECOA/GDPR verification, (iii) multi-format reporting system (interactive/static HTML, PDF, JSON), (iv) HPM-KD framework for knowledge distillation with meta-learning, and (v) scalable synthetic data generation via Dask. Through 6 case studies (credit scoring, hiring, healthcare, mortgage, insurance, fraud) we demonstrate that DeepBridge: reduces validation time by 89% (17 min vs. 150 min with fragmented tools), automatically detects fairness violations with complete coverage (10/10 features vs. 2/10 from existing tools), generates audit-ready reports in minutes. HPM-KD demonstrates consistent superiority across compression ratios 2.3--7x (CIFAR100): +1.00--2.04pp vs. Direct Training (p\u003c0.05), confirming that Knowledge Distillation is effective at larger teacher-student gaps. Usability study with 20 participants shows SUS score 87.5 (top 10%, ``excellent''), 95% success rate, and low cognitive load (NASA-TLX 28/100). DeepBridge is open-source under MIT license at https://github.com/deepbridge/deepbridge, with complete documentation at https://deepbridge.readthedocs.io","short_abstract":"We present DeepBridge, an 80K-line Python library that unifies multi-dimensional validation, automatic compliance verification, knowledge distillation, and synthetic data generation. DeepBridge offers: (i) 5 validation suites (fairness with 15 metrics, robustness with weakness detection, uncertainty via conformal predi...","url_abs":"https://arxiv.org/abs/2512.19744","url_pdf":"https://arxiv.org/pdf/2512.19744v1","authors":"[\"Gustavo Coelho Haase\",\"Paulo Henrique Dourado da Silva\"]","published":"2025-12-18T01:32:32Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"stat.AP\"]","methods":"[]","project_urls":"[\"https://deepbridge.readthedocs.io\"]","has_code":false,"code_links":[{"ID":605795,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2827338,"paper_url":"https://arxiv.org/abs/2512.19744","paper_title":"DeepBridge: A Unified and Production-Ready Framework for Multi-Dimensional Machine Learning Validation","repo_url":"https://github.com/deepbridge/deepbridge","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0},{"ID":605796,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2827338,"paper_url":"https://arxiv.org/abs/2512.19744","paper_title":"DeepBridge: A Unified and Production-Ready Framework for Multi-Dimensional Machine Learning Validation","repo_url":"https://github.com/DeepBridge-Validation/DeepBridge","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}