interpretability
3 published articles
Explainable AI
The knob no one wanted to turn: dtControl2+ε lets you sacrifice optimality for clarity
A new extension to dtControl2 allows engineers to trade a precise amount of performance for smaller, more understandable decision trees. The tool, dtControl2+ε, guarantees ε-optimality while pruning trees to orders of magnitude fewer nodes.
2026-08-05
Interpretability
Your AI thinks it explained itself. A new test proves it was lying the whole time.
Natural-language autoencoders score explanations by reconstruction, but a new study shows this test is passed even when 98% of specific claims are false. The authors introduce RECAP, a training method that lets probes independently verify designated content, beating adversarial lie detection with an AUC of 0.95 versus chance-level 0.51 for standard methods.
2026-07-24
Neuroscience
The neuroscience AI that finally cracks open the brain's black box
Generative causal testing (GCT) distills LLM-based brain-prediction models into concise verbal explanations, then uses an LLM to write stories that causally test those claims in fMRI. In experiments, GCT confirmed known selectivity, teased apart neighboring place-processing regions, and discovered new prefrontal micro-regions tuned to concepts like dialogue and measurements.
2026-07-11