Advanced Technology
AI & Machine Learning Research
Discover loss landscape dynamics, scaling laws, and optimization trajectories from ML training experiment data.
One of 34 industries across 8 sectors served by CDE — the Causal Dynamics Engine for Science and Engineering.
The Challenge
Why AI & Machine Learning Research teams still struggle to explain what is happening
Machine learning research generates rich experimental data: training loss trajectories, gradient statistics, hyperparameter sweep results, scaling experiment logs, and benchmark performance curves. Despite the field's rapid growth, the fundamental laws governing why certain architectures generalize, how training dynamics evolve, and what determines scaling behavior remain poorly understood. Research teams run thousands of experiments but extract governing relationships primarily through ad hoc analysis and visual inspection, lacking systematic methods for discovering the mathematical laws behind optimization landscapes.
Theoretical analysis relies on simplifying assumptions (infinite width limits, convex relaxations, independent noise) that diverge from practical settings. Empirical scaling law studies fit pre-specified functional forms, but these may not capture the true governing relationships. Phase transitions in training (sudden capability emergence, mode collapse events) are observed and catalogued but lack predictive governing equations. Each new architecture family requires its own bespoke empirical analysis, with limited transfer of governing principles across settings.
The CDE Approach
How CDE closes the explanation gap in ai & machine learning research
CDE treats ML experimental data as a scientific discovery problem, ingesting training logs, scaling experiment results, and benchmark trajectories to discover governing equations of learning dynamics. Rather than fitting pre-specified scaling law forms, it explores possible mathematical relationships between compute, data, architecture parameters, and performance outcomes. This can surface relationships researchers have not hypothesized, such as unknown interactions between learning rate schedules and architectural choices that determine generalization behavior.
Regime classification identifies phase transitions in training dynamics (loss plateaus, sudden capability emergence, mode collapse boundaries) and characterizes governing equations within each regime. Symbolic mode extracts closed-form scaling laws and training dynamics equations for principled decisions about compute allocation and architecture design. Every discovered scaling law includes deterministic replay, evidence provenance, and negative control validation.
Discovery Engine
How CDE applies here
Symbolic discovery produces closed-form scaling laws and optimization dynamics equations that can be validated against new experiments and used for extrapolation. Causal mode separates the effects of learning rate, batch size, architecture depth, and regularization on generalization, rather than relying on confounded hyperparameter correlations. Neuro-Symbolic mode handles high-dimensional experimental spaces, where the engine's internal methods capture interactions across hundreds of variables before structure extraction yields interpretable governing relationships.
Causal Graphs
Discovers directed causal structure from observational data — identifiable causal graphs, regime classifications, and intervention predictions.
Governing Equations
Extracts compact governing laws grounded in the causal structure — interpretable equations your team can read, verify, and compare against known theory.
Intervention Design
Proposes targeted experiments to resolve ambiguous causal edges — maximizing information gain where the causal structure is still uncertain.
Causal Validation
Negative controls, falsification tests, and identifiability analysis applied to every causal claim before promotion to the evidence ledger.
Typed Scientific Claims
What CDE discovers
Every discovery CDE produces is a typed scientific claim — not a black-box prediction, but a governed, reproducible, auditable piece of scientific knowledge with full provenance.
- Neural scaling law equations
- Training dynamics governing models
- Optimization landscape topology
- Generalization bound discovery
- Architecture-performance relationships
Governed Discovery
Make the finding reviewable
Every discovery CDE produces carries the review context around it: a Truth Dial setting, an evidence entry with replay context, and control results including bootstrap stability, out-of-distribution testing, and feature-shuffle validation.
For ai & machine learning research, that means teams can compare runs, justify decisions, and decide whether a finding is ready for internal use, external review, or regulated submission.
Same sector
Related industries
Semiconductor & Electronics
Discover device behavior laws, thermal dissipation equations, and process-yield relationships from semiconductor characterization data.
ViewRobotics & Autonomous Systems
Identify kinematic laws, control equations, and environmental interaction models for robotic systems directly from sensor data.
ViewQuantum Computing Research
Discover decoherence dynamics, gate error laws, and qubit interaction equations from quantum hardware characterization data.
ViewGet started
Put CDE on a real ai & machine learning research problem
Whether you are exploring ai & machine learning research data for the first time or scaling an existing research programme, CDE adapts to your workflow. Bring the dataset, the decision pressure, and the constraints. We will map the right discovery path.