Position: Don't Just "Fix it in Post": A Science of AI Must Study Training Dynamics
Summary
This position paper argues that a scientific understanding of AI must go beyond post-hoc analysis and instead study the training dynamics that shape model behavior, with implications for predicting, intervening, and designing training procedures for desired properties like capabilities and safety.
View Cached Full Text
Cached at: 06/08/26, 09:13 AM
# Position: Don't Just "Fix it in Post": A Science of AI Must Study Training Dynamics Source: [https://arxiv.org/abs/2606.06533](https://arxiv.org/abs/2606.06533) [View PDF](https://arxiv.org/pdf/2606.06533) > Abstract:What would it mean to have a scientific understanding of AI? Models are not static objects: they are snapshots of time\-evolving processes shaped by data, objectives, architectures, and optimization dynamics\. Yet much of AI research treats models as fixed artifacts, analyzing behaviors after training rather than asking why they emerge\. This position paper argues that a science of AI must move beyond post\-hoc fixes and study the training dynamics that produce model behavior\. Such a science should support progressively stronger forms of understanding: predicting outcomes from early training signals, intervening when trajectories go wrong, and ultimately designing training procedures that more reliably produce desired properties\. Scaling laws have made prediction routine for loss; the challenge is extending this success to capabilities, biases, robustness, and safety\-relevant behaviors\. We articulate requirements for such theories grounded in the history and philosophy of science, examine progress in mechanistic interpretability, fairness, memorization, and simplicity bias, and identify concrete open problems\. ## Submission history From: Stella Biderman \[[view email](https://arxiv.org/show-email/12356afe/2606.06533)\] **\[v1\]**Wed, 3 Jun 2026 17:58:14 UTC \(97 KB\)
Similar Articles
Position: Stop Reactively Patching Your Model Every Time and Start Proactive Test-Driven AI Development
This position paper argues for shifting from reactive AI flywheel maintenance (patching errors as they occur) to a proactive test-driven approach that maps feedback to a test space of task conditions, showing mathematically that the proactive method achieves better long-term scaling with fewer iterations.
Position: Artificial Intelligence Needs Meta Intelligence -- the Case for Metacognitive AI
This position paper argues that incorporating metacognition as a design principle can lead to more accurate, secure, and efficient AI systems, and demonstrates the concept through a Federated Learning case study and a software framework for experimentation.
Interactive Evaluation Requires a Design Science
This position paper argues that interactive AI evaluation should be treated as a design science paradigm, proposing a two-axis taxonomy and reporting standards for assessing dynamic system behavior through trajectories.
@BetaTomorrow: https://x.com/BetaTomorrow/status/2066435380623385000
This thread discusses the concept of 'Jagged Intelligence' in AI, framing it as a consequence of AI learning being an ill-posed inverse problem, and argues that external stabilizers like scaffolding and verification are essential.
@DataScienceDojo: Most AI agents fail at the same tasks over and over. Not because the model is bad but because nobody told it how to wor…
A new paper introduces Self-Harness, a method where AI agents self-improve by analyzing their own failures, generating fixes, and testing them, leading to up to 21 percentage point improvements in pass rates.