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machine learning Oct 02 Faynt: Scaling and Optimizing Policies for Competitive Melee Competitive real-time strategy and fighting game AI have seen rapid progress, but Super Smash Bros. Melee remains a particularly challenging benchmark. Its 52-character roster, frame-perfect inputs, and continuous state space dema... machine learning Oct 02 Are We Recovering Mechanisms? Objective-Level Recovery Gaps in Mechanistic Interpretability Mechanistic interpretability seeks to recover the internal computations that produce model behavior. Circuit analysis represents a computation as a sparse subgraph of interacting model components. Detailed studies have reconstruct... machine learning Oct 01 Semifactual Credit-Augmented Policy Optimization Reinforcement learning with verifiable rewards improved reasoning capabilities of large language models yet predictions stayed sensitive to task-irrelevant prompt features. Researchers investigated this sensitivity through semifac... machine learning Sep 30 Breakdown of Local Denoising as Semantic Speciation The dynamics of generative models exhibit two apparently distinct temporal windows: a speciation window, in which a sample commits to a semantic class, and a nonlocality window, in which local context windows become insufficient f... machine learning Sep 30 LeapQuant: Efficient Linear Attention with Accurate Recurrent State Quantization Recent large language models increasingly adopt hybrid attention designs that replace the standard quadratic attention with linear alternatives. Models such as Gated DeltaNet (GDN) and Kimi Delta Attention (KDA) compress the entir... machine learning Sep 30 Breaking the Uniformity Trap: Scaling Video Diffusion Model via SplitMoE Video diffusion models have achieved remarkable fidelity in text-to-video synthesis, yet scaling these models to capture complex spatiotemporal dynamics remains computationally expensive. Diffusion Transformers (DiTs) have become... machine learning Sep 30 Multi-Agent Flow Matching with Decoupled Generative Guidance Generative modeling has become a cornerstone for producing diverse objects from complex, multimodal distributions. Diffusion models and flow matching frameworks have demonstrated remarkable success across domains ranging from lang... machine learning Sep 29 PDMD: Projected Distribution Matching Distillation for Video Diffusion Models Modern video diffusion models require many denoising evaluations over long spatiotemporal token sequences, making inference computationally expensive. Distribution Matching Distillation (DMD) was introduced to reduce the number of... machine learning Sep 29 Unifying Distributional Training for One-Step Visual Generation One-step visual generation requires efficient supervision that can shape a generator in a single forward pass. Prior distributional training methods match real and generated features in frozen representation spaces, but lack a uni... machine learning Sep 29 Improving Test-Time Scaling with Adaptive Looped Transformers Looped transformers have emerged as a parameter-efficient architecture by reusing a shared stack of layers across multiple input positions during a single forward pass. This reuse treats computation as a dynamic process that can b...