Non-Uniform Noise
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Non-Uniform Noise-to-Signal Ratio in the REINFORCE Policy-Gradient Estimator
arXiv:2602.01460v3 Announce Type: replace-cross Abstract: Policy-gradient methods are widely used in reinforcement learning, yet training often becomes unstable or slows down as learning progresses. We study this phenomenon through the noise-to-signal ratio (NSR) of a policy-gradient estimator, defined as the estimator variance (noise) normalized by the squared norm of the true gradient (signal). Our main result is that, for (i) finite-horizon linear systems with Gaussian policies and linear...
Characterizing the Effect of Noise in Language Generation in the Limit
Announce Type: replace Abstract: Kleinberg and Mullainathan recently proposed a formal framework for studying the phenomenon of language generation, called language generation in the limit. In this model, an adversary gives an enumeration of example strings from an unknown target language, and the algorithm is tasked with correctly generating unseen strings from the target language within finite time. Refined notions of non-uniform and uniform generation were later introduced by Li, Raman,...
Differentiable Optimization of Linear Differential Microphone Arrays: A Joint Geometry and Filter Design Framework
arXiv:2412.05123v2 Announce Type: replace Abstract: This paper presents a differentiable optimization framework for the design of constrained Linear Differential Microphone Arrays (LDMAs). The proposed method leverages a non-uniform delay-and-sum beamformer as a light-weight base system model, proving its ability to achieve the optimal beampattern of LDMAs by jointly optimizing microphone positions and filter weights. The formulation enables the optimized design of a filter with a...
InfoMerge: Information-aware Token Compression for Efficient Video Large Language Models
arXiv:2606.02161v1 Announce Type: new Abstract: Video Large Language Models (Video-LLMs) achieve strong performance in video understanding, but their excessive visual tokens bring substantial computational overhead. Existing training-free compression methods improve inference efficiency by reducing visual tokens, yet they often rely on local adjacent-frame similarity for temporal redundancy estimation or allocate token budgets mainly according to segment length. Such designs are sensitive to...
A Quantitative Approximation Framework for Flow Distillation in Diffusion Models
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SoulsOnly.tff – A font for humans not AI and keyboard firmware to type in it
A font whose rendered glyphs spell readable text while the stored character stream (what copy-paste, HTML/PDF extraction, and scrapers see) is noise. The font is the decoder, applied only at the rendering layer, and the cipher is driven by an ordinary keyboard: you type normal keys, the keyboard emits the noise stream, and only this font renders it back into words. This is a craft and statement project, not a claim of unbreakable security.