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TSGate: Timestep-Aware Gated Attention for Diffusion Transformers

arXiv:2609.34539v1 Announce Type: new Abstract: Diffusion Transformers (DiTs) have emerged as the dominant architecture for high-fidelity image and video generation. Recent DiT systems increasingly us

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TT-VidT: Decoupling the Temporal Axis for Efficient Motion-Centric Video Pretraining

DGX agent

arXiv:2609.33419v1 Announce Type: new Abstract: Comparisons in video self-supervised learning often evaluate complete training recipes rather than isolating the method itself: architecture, objective,

researcharxiv-cs-cv

29 Sept 2026

TTRSD: Test-Time Reinforcement Learning with Self-Distillation for Vision-Language Models

arXiv:2609.33414v1 Announce Type: new Abstract: Test-time reinforcement learning enables vision-language models (VLMs) to adapt using unlabeled inputs. However, repeated sampling under fixed visual co

Tunable Leg Stiffness in a Monopedal Hopper for Energy-Efficient Vertical Hopping Across Varying Ground Profiles

arXiv:2508.02873v3 Announce Type: replace Abstract: We present the design and implementation of HASTA (Hopper with Adjustable Stiffness for Terrain Adaptation), a vertical hopping robot with real-time

researcharxiv-cs-ro

TwinS-GCN: Spectral conjugate for Spectral Graph Convolutional Networks

arXiv:2609.32940v1 Announce Type: new Abstract: Graph convolutional networks propagate information by repeated local aggregation through a graph shift operator; i.e., a K-layer network reaches K hops

Twist, Don't Tilt: Trajectory-Exact Constrained Decoding for Masked Diffusion Models

arXiv:2609.35609v1 Announce Type: new Abstract: Constrained decoding for Masked Diffusion Language Models (MDLMs) aims to ensure that generated outputs satisfy a specified structure or syntax constrai

Applications

Two Heads Are Better Than One: Aggregating Weaker LLMs for Better Forecasts

arXiv:2609.33257v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to forecast real-world events, but access to the strongest individual forecaster may be costly or oth

applicationsarxiv-cs-lg

Two-Sample Testing for Inhomogeneous Random Graphs in Non-Integral L_r Norms

arXiv:2609.33968v1 Announce Type: cross Abstract: Testing whether two populations of networks share the same edge probabilities is a basic problem in network inference. How hard it is depends on the n

Two-Stage Learned Decomposition for Scalable Routing on Multigraphs

arXiv:2605.05389v2 Announce Type: replace Abstract: Most neural methods for Vehicle Routing Problems (VRPs) are limited to Euclidean settings or simple graphs. In this work, we instead consider multig

Two-Stage Multi-View Gait Recognition with a Re-Embedding Network

arXiv:2609.32244v1 Announce Type: new Abstract: Gait recognition always remains challenging due to severe overfitting and the rigid view constraints common in single-stage approaches. We propose a two

researcharxiv-cs-cv

Two-Timescale Fine-tuning Provably Learns New Features for Two-Layer ReLU Networks

arXiv:2609.34667v1 Announce Type: cross Abstract: Fine-tuning pre-trained models on specialized tasks with scarce data is central to modern deep learning. Despite its empirical success, theoretical un

Type-Balanced Federated Learning for Visual Analog Meter Reading

arXiv:2609.31998v1 Announce Type: new Abstract: Analog dial meters are widely deployed in industrial applications and utility sites, where environments and meter types vary and inspection data may be

researcharxiv-cs-cv

Typed Decision Models: An Early Evidence Audit and Evaluation Checklist

arXiv:2609.32160v1 Announce Type: cross Abstract: Typed decision models (TDMs) return probability distributions over caller-defined options without generating text. TypeSafe released Jev, a commercial

Local Ai

Typed Temporal Interaction Features for Simulation-Backed Forecasting of Open-Source Game Release Incidents

arXiv:2609.31647v1 Announce Type: new Abstract: Open-source video-game quality depends on inter-actions among code, assets, configuration, tests, contributors, and issue workflows, yet conventional de

local-aiarxiv-cs-lg

Ultrafast is available today for GPT-6 Astra in Codex, ChatGPT Work, and the API, with GPT-6.1 Sol coming soon. To access it in Codex and Ch…

Ultrafast is available today for GPT-6 Astra in Codex, ChatGPT Work, and the API, with GPT-6.1 Sol coming soon. To access it in Codex and ChatGPT Work, we’re introducing Pro 500—a new plan with our hi

model-releasesopenai--x

UMR: Universal Manipulation Representation

model-releasesarxiv-cs-ro

arXiv:2609.34256v1 Announce Type: new Abstract: General-purpose embodied manipulation hinges on a unified action representation that generalizes across embodiments and scales readily. Yet existing pol

Unbiased Top-k Estimation for On-Policy Distillation

arXiv:2609.34447v1 Announce Type: cross Abstract: On-policy distillation (OPD) is becoming an important component of large language model (LLM) post-training for transferring the reasoning capability

Uncertainty-Aware Selection of Online Algorithms with Simulator Ensembles

arXiv:2609.32170v1 Announce Type: new Abstract: The performance of online reinforcement learning depends critically on design choices, especially those that affect exploration. These choices are often

Uncertainty Quantification in Cardiac Model Personalisation from Ultrafast Ultrasound

arXiv:2609.35214v1 Announce Type: cross Abstract: Cardiac model personalisation requires inferring mechanical parameters that are not directly measurable in vivo. Ultrafast ultrasound shear wave elast

Uncertainty Quantification of Next Generation Reservoir Computing with Applications to Memory-Driven Dynamical Systems

arXiv:2609.32169v1 Announce Type: cross Abstract: Nonlinear dynamical systems with memory arise across science and engineering, yet uncertainty quantification for efficient forecasting methods such as

Uncovering Ordinal-Matching Bias in Audio-Visual LLMs

arXiv:2609.34223v1 Announce Type: new Abstract: This work aims to improve how audio-visual large language models (AVLLMs) associate speech with the correct visible speaker in multi-speaker scenes. We

Understanding and Exploiting Anisotropy in Post-Training

arXiv:2609.32792v1 Announce Type: new Abstract: LLM post-training combines supervised fine-tuning (SFT), a mode-covering forward-KL objective, with reinforcement learning (RL), a mode-seeking reverse-

Understanding and inverse design of implicit bias in stochastic learning: a geometric perspective

arXiv:2601.06597v3 Announce Type: replace Abstract: Can we design a model such that its stochastic training favours a desired class of solutions without enforcing an explicit penalty? Under suitable c

Understanding and Mitigating Under-Confidence in GNNs from the Final Layer

arXiv:2505.11335v4 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have demonstrated remarkable effectiveness on graph-based tasks. However, their predictive confidence is often miscalib

Understanding Generalization Requires Universal Induction

arXiv:2609.34458v1 Announce Type: cross Abstract: Classical statistical theory is insufficient to explain the successes of general-purpose AI models, because it depends on handcrafted inductive biases

Understanding Quantization of Optimizer States in LLM Pre-training: Dynamics of State Staleness and Effectiveness of State Resets

arXiv:2603.16731v2 Announce Type: replace Abstract: Quantizing optimizer states is becoming an important ingredient of memory-efficient large-scale pre-training, but the resulting optimizer dynamics r

Understanding the Subspace Stabilization of the Hessian and Gradient Covariance Matrix

arXiv:2609.31983v1 Announce Type: new Abstract: The phenomenon of the top subspace stabilization of the Hessian matrix is an surprising and critical aspect in study of the second-order information of

Understanding the Synergy between SFT, RLVR, and OPD in LLM Post-Training

arXiv:2609.31900v1 Announce Type: new Abstract: Modern LLM post-training composes supervised fine-tuning (SFT), reinforcement learning with verifiable rewards (RLVR), and on-policy distillation (OPD)

UnfoldCRF: Structured Mask Refinement with Image-Conditioned Latent Regions

model-releasesarxiv-cs-cv

arXiv:2609.33996v1 Announce Type: new Abstract: Learned mask refiners improve segmentation accuracy, but it is hard to tell how much of the improvement comes from explicit structure rather than from e

Uni-VLaT: Whole-Body Tactile Adaptation of VLA Policies for Humanoid Loco-Manipulation

arXiv:2609.35450v1 Announce Type: new Abstract: Physical contact often determines how a humanoid should respond during loco-manipulation, yet vision and proprioception alone are often insufficient to

tutorialsarxiv-cs-ro

UniBYD: A Unified Framework for Learning Robotic Manipulation Across Embodiments Beyond Imitation of Human Demonstrations

model-releasesarxiv-cs-ro

arXiv:2512.11609v3 Announce Type: replace Abstract: In embodied intelligence, the embodiment gap between robotic and human hands brings significant challenges for learning from human demonstrations. A

UniCache: Task- and Type-Aware KV Cache Compression for Unified Multimodal Models

arXiv:2609.32831v1 Announce Type: new Abstract: Unified multimodal models combine understanding, generation, and editing within a single network, offering a promising foundation for versatile multimod

Unified Trajectory Matching Policy Optimization: Diverse T2I Generation and VLA Generalization

arXiv:2609.34688v1 Announce Type: new Abstract: Reward-maximizing reinforcement learning (RL) is widely used to post-train stochastic diffusion and flow policies for text-to-image (T2I) generation. Ho

Unified Visual-Tactile-Action Modeling from Human Demonstrations for Dexterous Manipulation

arXiv:2609.34182v1 Announce Type: new Abstract: Dexterous manipulation requires tactile feedback.However, robot tactile demonstrations are difficult to scale,because dexterous-hand teleoperation provi

researcharxiv-cs-ro

Uniform Race: Parameter-Free Approximate Rejection Sampling

arXiv:2609.34639v1 Announce Type: new Abstract: We study approximate sampling: given N independent samples from a proposal distribution mu, the goal is to select one whose distribution is close to a t

Unifying Deep Predicate Invention with Pre-trained Foundation Models

arXiv:2512.17992v2 Announce Type: replace Abstract: Long-horizon robotic tasks are hard due to continuous state-action spaces and sparse feedback. Symbolic world models help by decomposing tasks into

tutorialsarxiv-cs-ro

Unifying Distributional Training for One-Step Visual Generation

arXiv:2609.35763v1 Announce Type: new Abstract: Distributional training provides collective supervision for one-step visual generation by matching real and generated features in frozen representation

UniPool: Learning Expert-to-Layer Ownership from Brief Global Access

arXiv:2605.06665v2 Announce Type: replace Abstract: Most Mixture-of-Experts (MoE) transformers preassign each expert to one layer for the whole of training. We study whether a brief global-access phas

tutorialsarxiv-cs-lg

Universal Approximation of Measure-to-Measure Operators by Pushforwards

arXiv:2609.35483v1 Announce Type: new Abstract: Many learning tasks map an input distribution to an output distribution. A natural way to model such an operator is to transform each input sample using

Universal Dynamic Portfolios

arXiv:2609.34643v1 Announce Type: new Abstract: Cover's Universal Portfolio (Cover, 1991) matches the performance of the best constant rebalanced portfolio in hindsight. We generalize this framework t

Universality and Generalization of Causal Transformers Across Context Lengths

arXiv:2609.35055v1 Announce Type: new Abstract: Long contexts are central to modern transformer systems, but most expressivity results choose a different network for each fixed sequence length. We stu

Unlocking Few-Step Diffusion for Faithful Previews

arXiv:2609.34406v1 Announce Type: cross Abstract: Sampling latency compounds in diffusion workflows, where users generate and discard many candidates before keeping one. Surprisingly, the poor outputs

tutorialsarxiv-cs-cv

Unlocking Geodesic Gromov-Wasserstein Distances for 3D Modeling

arXiv:2609.32824v1 Announce Type: new Abstract: extit{Gromov-Wasserstein Distances} (GWDs) provide quantitative ways of comparing probabilistic distributions defined on different metric spaces by appl

researcharxiv-cs-cv

UNMATCH: Selective Unbalanced Token-Patch Matching for Forensic Image-Claim Verification

model-releasesarxiv-cs-cv

arXiv:2609.31766v1 Announce Type: new Abstract: Contextual image misuse pairs an image with a misleading claim. We study image-claim correspondence in fact-checked pairs containing out-of-context reus

Hardware

UnStep: Training-Free Acceleration of Causal Video Diffusion with Fewer Steps Than Distillation

arXiv:2609.32518v1 Announce Type: new Abstract: Distilling bidirectional multi-step video diffusion transformers into few-step causal models has become a common approach for streaming video generation

hardwarearxiv-cs-cv

Unsupervised spiking feature learning for event-based pedestrian crossing detection: approaching supervised accuracy without labelled training data

model-releasesarxiv-cs-cv

arXiv:2609.31671v1 Announce Type: new Abstract: Event cameras are well suited to pedestrian crossing detection, and spiking neural networks (SNNs) can process their output natively, but current SNN de

Unveiling Implicit Advantage Symmetry: Why GRPO Struggles with Exploration and Difficulty Adaptation

arXiv:2602.05548v4 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR), particularly GRPO, has become the standard for eliciting LLM reasoning. However, its efficien

UOPD: Uncertainty-Aware Intervention for On-Policy Distillation of Multi-Turn Agents

arXiv:2609.34036v1 Announce Type: new Abstract: On-policy distillation (OPD) trains a student on its own rollouts using dense supervision from a teacher. In multi-turn environments, a mistake at a cri