arXiv:2605.22891v1 Announce Type: new Abstract: Evaluation in scientific reconstruction is dominated by pointwise metrics - RMSE, MAE, per-event resolution - under the implicit assumption that lower error means better reconstruction. We show that this assumption fails structurally for inverse problems with multimodal posteriors. By the law of total variance, point estimators trained to minimize MSE or MAE produce a marginal spectrum strictly narrower than the truth whenever the posterior has nonzero width. The resulting bias is independent of architecture, training, and dataset size, and it co
The proliferation of AI systems across critical scientific domains necessitates robust and accurate evaluation methodologies, highlighting the current deficiencies in standard metrics.
This research reveals a fundamental flaw in common AI evaluation metrics that systematically biases scientific reconstruction, impacting fields from drug discovery to climate modeling.
The understanding that standard metrics like RMSE and MAE can inherently mislead in multimodal inverse problems requires a paradigm shift in how AI models for scientific reconstruction are evaluated and designed.
- · Researchers developing novel, robust evaluation protocols
- · AI models specifically designed for multimodal posterior distributions
- · Scientific fields relying on high-fidelity inverse problem solutions
- · Developers solely relying on pointwise metrics for model evaluation
- · Scientific reconstruction models trained exclusively to minimize MSE/MAE
- · Traditional AI evaluation methodologies
AI development in scientific domains will shift towards more sophisticated, distribution-aware evaluation metrics.
This re-evaluation could lead to a 're-ranking' of AI models, where previously 'successful' models are found to be suboptimal under new, more accurate evaluation standards.
Improved evaluation protocols could unlock significant advancements in scientific discovery by enabling the development of truly unbiased and accurate AI models for inverse problems.
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Read at arXiv cs.LG