I-SAFE uses Wasserstein Coherence Metrics to audit distributional coherence of scientific AI models under structurally guided perturbations, revealing differences among DTI predictors that accuracy metrics miss.
Causal abstractions of neural networks
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CauSim turns scarce causal reasoning labels into scalable supervised data by having LLMs incrementally construct complex executable structural causal models.
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I-SAFE: Wasserstein Coherence Metrics for Structural Auditing of Scientific AI Models
I-SAFE uses Wasserstein Coherence Metrics to audit distributional coherence of scientific AI models under structurally guided perturbations, revealing differences among DTI predictors that accuracy metrics miss.
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CauSim: Scaling Causal Reasoning with Increasingly Complex Causal Simulators
CauSim turns scarce causal reasoning labels into scalable supervised data by having LLMs incrementally construct complex executable structural causal models.