The Sinkhorn treatment effect is a new entropic optimal transport measure of divergence between counterfactual distributions that admits first- and second-order pathwise differentiability, debiased estimators, and asymptotically valid tests for distributional treatment effects.
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2026 4verdicts
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ContextualJailbreak uses evolutionary search over simulated primed dialogues with novel mutations to reach 90-100% attack success on open LLMs and transfers to some closed frontier models at 15-90% rates.
Latent Flow Matching models exhibit inherent stability to data reduction and model shrinkage due to the flow matching objective, enabling reduced-dataset training and two-stage inference with over 2x speedup while preserving output quality.
Generalizing two DPP-based Monte Carlo estimators to continuous domains provides variance rates of O(N^{-(1+1/d)}) for a fixed DPP method and O(1/N) for a tailored DPP method, along with new sampling algorithms.
citing papers explorer
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Sinkhorn Treatment Effects: A Causal Optimal Transport Measure
The Sinkhorn treatment effect is a new entropic optimal transport measure of divergence between counterfactual distributions that admits first- and second-order pathwise differentiability, debiased estimators, and asymptotically valid tests for distributional treatment effects.
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ContextualJailbreak: Evolutionary Red-Teaming via Simulated Conversational Priming
ContextualJailbreak uses evolutionary search over simulated primed dialogues with novel mutations to reach 90-100% attack success on open LLMs and transfers to some closed frontier models at 15-90% rates.
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Exploring and Exploiting Stability in Latent Flow Matching
Latent Flow Matching models exhibit inherent stability to data reduction and model shrinkage due to the flow matching objective, enabling reduced-dataset training and two-stage inference with over 2x speedup while preserving output quality.
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On two ways to use determinantal point processes for Monte Carlo integration
Generalizing two DPP-based Monte Carlo estimators to continuous domains provides variance rates of O(N^{-(1+1/d)}) for a fixed DPP method and O(1/N) for a tailored DPP method, along with new sampling algorithms.