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ai Sep 30 Imagine3D-LLM: Teaching MLLMs to Imagine 3D Scenes Before Answering Multimodal large language models have made rapid progress on single-image tasks, yet they still falter when asked to reason about a scene from multiple viewpoints. A model that sees three photos of a room from different angles oft... 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... ai Sep 30 Learning Meta-Skills for Agent Harness Design in Test-Time AI4AI Agent performance depends on both reasoning ability and the environment in which it acts. Most research focuses on improving the model itself, but this paper from Apodex and the University of Illinois Urbana-Champaign takes a diff... 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... ai Sep 29 TokenCast: Forecasting Token Consumption During LLM Agent Execution When a large language model (LLM) agent executes the same task, token consumption can vary by over an order of magnitude across runs. The agent chooses its next steps based on tool feedback and intermediate results, while the grow... 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...