“DLMs augmented with polynomial-length chain-of-thought (CoT) can simulate any parallel sampling algorithm using an optimal number of sequential steps.”

“The proposed sampler consistently improves sample quality under the same NFE budget and can be competitive with, and sometimes outperform, state-of-the-art higher-order samplers.”

“The paper introduces a new dataset named "FireRescue" and proposes an improved model named FRS-YOLO.”

“The paper demonstrates that implicit score matching achieves the same rates of convergence as denoising score matching and allows for Hessian estimation without the curse of dimensionality.”

“The modular reduction allows us to exploit any SLC sampling algorithm in order to traverse the backwards path, and we establish novel guarantees with short proofs for both uni-modal and multi-modal densities.”

“The findings reveal a surprising dichotomy: while the number of samples needed to accurately tilt a bounded random vector increases polynomially in the tilt amount, it increases at a super polynomial rate for unbounded distributions.”

“The paper claims an enhanced convergence rate of order $\mathcal{O}(h)$ in the $L^2$-Wasserstein distance, significantly improving the existing order-half convergence.”

“The paper derives a fundamental identity that connects the (path-) derivative of a (possibly) non-Euclidean energy score to the score of the noisy marginal.”

“TV-RAG realizes a dual-level reasoning routine that can be grafted onto any LVLM without re-training or fine-tuning.”

“EPD-Solver leverages the Mean Value Theorem for vector-valued functions to approximate the integral solution more accurately.”

“Rather than simulate intelligence through statistical tokens, this system operationalizes thought itself — every output carries its structural history and constraints.”

“Diffusion models have recently emerged as powerful learners for simulation-based inference (SBI), enabling fast and accurate estimation of latent parameters from simulated and real data.”

“We present a practical framework, \emph{Inverted Causality Focusing Algorithm} (ICFA), that treats search as a target-conditioned reweighting process.”

“We develop a generative perspective on hyper-parameter tuning that combines two ideas: (i) optimization-based approximations to Bayesian posteriors via randomized, weighted objectives (weighted Bayesian bootstrap), and (ii) amortization of repeated optimization across many hyper-parameter settings by learning a transport map from hyper-parameters (including random weights) to the corresponding optimizer.”

“The research focuses on the Reweighted Annealed Leap-Point Sampler.”

“The paper is available on ArXiv, suggesting peer review is not yet complete but the research is publicly accessible.”

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