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TrajTok: Learning Trajectory Tokens enables better Video Understanding

arXiv:2602.22779v2 Announce Type: replace Abstract: Tokenization in video models, typically through patchification, generates an excessive and redundant number of tokens. This severely limits video ef

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TRAM: Training Approximate Multiplier Structures for Low-Power AI Accelerators

DGX agent

arXiv:2605.08231v1 Announce Type: cross Abstract: Reducing power consumption in AI accelerators is increasingly important. Approximate computing can reduce power consumption while keeping the accuracy

researcharxiv-cs-ai

12 May 2026

Transcoda: End-to-End Zero-Shot Optical Music Recognition via Data-Centric Synthetic Training

arXiv:2605.10835v1 Announce Type: new Abstract: Optical Music Recognition (OMR), the task of transcribing sheet music into a structured textual representation, is currently bottlenecked by a lack of l

Transfer Learning for Dead Fuel Moisture Prediction Using Time-Warping Recurrent Neural Networks

arXiv:2605.08379v1 Announce Type: cross Abstract: This paper proposes a time-warping transfer learning method, a technique for temporally rescaling the learned dynamics of a recurrent neural network (

Transfer Learning of Multiobjective Indirect Low-Thrust Trajectories Using Diffusion Models and Markov Chain Monte Carlo

arXiv:2605.09125v1 Announce Type: cross Abstract: Preliminary low-thrust spacecraft mission design is a global search problem characterized by a complex solution landscape, multiple objectives, and nu

Transformation-Augmented GRPO for Enhancing Exploration in Reasoning of Large Language Models

arXiv:2601.22478v3 Announce Type: replace Abstract: Group Relative Policy Optimization (GRPO) has become the dominant method for reinforcement learning with verifiable rewards in large language models

Transformer autoencoder with local attention for sparse and irregular time series with application on risk estimation

arXiv:2605.08914v1 Announce Type: cross Abstract: This paper introduces a framework specifically designed for sparse and irregular time series {risk estimation}. It is based on a Transformer Autoencod

applicationsarxiv-cs-ai

Transformers Can Implement Preconditioned Richardson Iteration for In-Context Gaussian Kernel Regression

arXiv:2605.08475v1 Announce Type: cross Abstract: Mechanistic accounts of in-context learning (ICL) have identified iterative algorithms for linear regression and related linear prediction tasks, ofte

Transformers Provably Learn Sparse XOR with Polylogarithmic Parameters

arXiv:2502.07553v2 Announce Type: replace Abstract: Learning sparse parity functions has become a theoretical testbed for studying feature learning in neural networks. However, existing analyses prima

Transforming the Use of Earth Observation Data: Exascale Training of a Generative Compression Model with Historical Priors for up to 10,000x Data Reduction

arXiv:2605.08633v1 Announce Type: cross Abstract: Earth observation is becoming one of the largest data-producing activities in science, yet current pipelines still treat compression as a storage and

TransmissiveGS: Residual-Guided Disentangled Gaussian Splatting for Transmissive Scene Reconstruction and Rendering

arXiv:2605.10705v1 Announce Type: new Abstract: Transmissive scenes are ubiquitous in daily life, yet reconstructing and rendering them remains highly challenging due to the inherent entanglement betw

applicationsarxiv-cs-cv

Trapping Attacker in Dilemma: Examining Internal Correlations and External Influences of Trigger for Defending GNN Backdoors

arXiv:2605.08278v1 Announce Type: cross Abstract: GNNs have become a standard tool for learning on relational data, yet they remain highly vulnerable to backdoor attacks. Prior defenses often depend o

TripleWin: Fixed-Point Equilibrium Pricing for Data-Model Coupled Markets

arXiv:2511.03368v2 Announce Type: replace Abstract: The rise of the machine learning (ML) model economy has intertwined markets for training datasets and pre-trained models. However, most pricing appr

safetyarxiv-cs-lg

Trustworthy AI: Ensuring Reliability and Accountability from Models to Agents

arXiv:2605.08964v1 Announce Type: new Abstract: In this thesis, we develop algorithms with theoretical guarantees for ensuring reliability and accountability of Machine Learning (ML) systems. As ML sy

safetyarxiv-cs-lg

TSNN: A Non-parametric and Interpretable Framework for Traffic Time Series Forecasting

arXiv:2605.09208v1 Announce Type: new Abstract: Although many complex models were proposed to analyze time series data, some studies have demonstrated remarkable performance with simpler structures. A

applicationsarxiv-cs-lg

TTCD:Transformer Integrated Temporal Causal Discovery from Non-Stationary Time Series Data

arXiv:2605.08111v1 Announce Type: cross Abstract: The widespread availability of complex time series data in various domains such as environmental science, epidemiology, and economics demands robust c

Turbo-GS: Accelerating 3D Gaussian Fitting for High-Quality Radiance Fields

arXiv:2412.13547v3 Announce Type: replace Abstract: Novel-view synthesis plays a crucial role in computer vision with applications in 3D reconstruction, mixed reality, and robotics. Recent approaches,

Two Ways to De-Bias an LLM-as-a-Judge: A Continuous-Score Comparison of Hierarchical Bayesian Calibration and Neural-ODE Score Transport

model-releasesarxiv-cs-cl

arXiv:2605.09227v1 Announce Type: new Abstract: [Abridged] Using a Large Language Model (LLM) as an automatic rater (LLM-as-a-judge) is cheap but potentially biased: some judges run lenient, others st

UAV-Assisted Scan-to-Simulation for Landslides Using Physics-Informed Gaussian Splatting

arXiv:2605.10715v1 Announce Type: new Abstract: Landslide monitoring and simulation play an important role in urban safety assessment and disaster prevention. Existing landslide simulation pipelines t

UFO: A Unified Flow-Oriented Framework for Robust Continual Graph Learning

arXiv:2605.09862v1 Announce Type: cross Abstract: Graph learning research has increasingly shifted toward continual graph learning (CGL), which better reflects real-world scenarios where graphs evolve

UIESNN: A Scale-Aware Spiking Network for Underwater Image Enhancement

arXiv:2605.08376v1 Announce Type: new Abstract: Underwater image enhancement (UIE) is a practically important yet underexplored application of spiking neural networks (SNNs), where the dominant degrad

UM-Text: A Unified Multimodal Model for Image Understanding and Visual Text Editing

arXiv:2601.08321v3 Announce Type: replace Abstract: With the rapid advancement of image generation, visual text editing using natural language instructions has received increasing attention. The main

UMEDA: Unified Multi-modal Efficient Data Fusion for Privacy-Preserving Graph Federated Learning via Spectral-Gated Attention and Diffusion-Based Operator Alignment

arXiv:2605.08288v1 Announce Type: cross Abstract: Device-free localization trains models from heterogeneous wireless and visual sensors (e.g., Wi-Fi, LiDAR) distributed across edge devices. Federated

Uncertainty-Aware and Decoder-Aligned Learning for Video Summarization

arXiv:2605.09507v1 Announce Type: new Abstract: Video summarization aims to produce a compact representation of a long video by selecting a subset of temporally important segments that best reflect hu

Uncertainty-Aware Token Importance Estimation in Spiking Transformers

arXiv:2605.09276v1 Announce Type: cross Abstract: Spiking transformers have shown strong potential for neuromorphic vision, yet their token processing across multiple spiking steps still introduces su

Uncertainty Estimation for Heterophilic Graphs Through the Lens of Information Theory

arXiv:2505.22152v2 Announce Type: replace Abstract: While uncertainty estimation for graphs recently gained traction, most methods rely on homophily and deteriorate in heterophilic settings. We addres

Uncertainty-Guided Dual-Domain Learning for Reliable Skin Lesion Segmentation

arXiv:2605.09600v1 Announce Type: cross Abstract: Accurate skin lesion segmentation is vital for dermoscopic Computer-Aided Diagnosis. However, visual ambiguity and morphological irregularity often de

Uncovering Intra-expert Activation Sparsity for Efficient Mixture-of-Expert Model Execution

arXiv:2605.08575v1 Announce Type: cross Abstract: Mixture of Experts (MoE) architecture has become the standard for state-of-the-art large language models, owing to its computational efficiency throug

Hardware

Understand and Accelerate Memory Processing Pipeline for Disaggregated LLM Inference

arXiv:2603.29002v2 Announce Type: replace-cross Abstract: Modern large language models (LLMs) increasingly depends on efficient long-context processing and generation mechanisms, including sparse atte

hardwarearxiv-cs-ai

Understanding Asynchronous Inference Methods for Vision-Language-Action Models

arXiv:2605.08168v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models offer a promising path to generalist robot control, but their inference latency causes observation staleness when

Uni-Hand: Universal Hand Motion Forecasting in Egocentric Views

arXiv:2511.12878v4 Announce Type: replace Abstract: Forecasting how human hands move in egocentric views is critical for applications like augmented reality and human-robot policy transfer. Recently,

Uni-Synergy: Bridging Understanding and Generation for Personalized Reasoning via Co-operative Reinforcement Learning

arXiv:2605.10445v1 Announce Type: new Abstract: Unified Multimodal Models (UMMs) excel in general tasks but struggle to bridge the gap between personalized understanding and generation. Prior works la

Unified Approach for Weakly Supervised Multicalibration

arXiv:2605.09857v1 Announce Type: cross Abstract: Multicalibration requires predicted scores to agree with label probabilities across rich families of subgroups and score-dependent tests, but existing

Unified Modeling of Lane and Lane Topology for Driving Scene Reasoning

arXiv:2605.08911v1 Announce Type: new Abstract: Autonomous vehicles need to perceive not only physical elements in the driving scene, such as lane lines and traffic lights, but also logical elements l

Unified Noise Steering for Efficient Human-Guided VLA Adaptation

arXiv:2605.10821v1 Announce Type: new Abstract: Diffusion-based vision-language-action (VLA) models have emerged as strong priors for robotic manipulation, yet adapting them to real-world distribution

safetyarxiv-cs-ro

Uniform Inductive Spatio-Temporal Kriging

arXiv:2603.05301v2 Announce Type: replace Abstract: Inductive spatio-temporal kriging infers signals at unobserved locations from observed sensors, but real-world observations are often incomplete and

safetyarxiv-cs-ai

Unifying Perspectives: Plausible Counterfactual Explanations on Global, Group-wise, and Local Levels

arXiv:2405.17642v3 Announce Type: replace-cross Abstract: The growing complexity of AI systems has intensified the need for transparency through Explainable AI (XAI). Counterfactual explanations (CFs)

Unifying Scientific Communication: Fine-Grained Correspondence Across Scientific Media

arXiv:2605.05831v2 Announce Type: replace Abstract: The communication of scientific knowledge has become increasingly multimodal, spanning text, visuals, and speech through materials such as research

UniShield: Unified Face Attack Detection via KG-Informed Multimodal Reasoning

arXiv:2605.08709v1 Announce Type: new Abstract: Unified face attack detection (UAD) requires recognizing physical spoofing and digital forgery within a shared decision space, yet existing discriminati

Unison: Harmonizing Motion, Speech, and Sound for Human-Centric Audio-Video Generation

arXiv:2605.08729v1 Announce Type: new Abstract: Motion, speech, and sound effects are fundamental elements of human-centric videos, yet their heterogeneous temporal characteristics make joint generati

Local Ai

UniUncer: Unified Dynamic Static Uncertainty for End to End Driving

arXiv:2603.07686v2 Announce Type: replace-cross Abstract: End-to-end (E2E) driving has become a cornerstone of both industry deployment and academic research, offering a single learnable pipeline that

local-aiarxiv-cs-cv

Universal Feature Selection with Noisy Observations and Weak Symmetry Conditions

arXiv:2605.09396v1 Announce Type: cross Abstract: This paper relaxes the restrictive symmetry conditions adopted in [4], [5] and extends their universal feature selection framework to accommodate nois

Unlearners Can Lie: Evaluating and Improving Honesty in LLM Unlearning

arXiv:2605.08765v1 Announce Type: cross Abstract: Unlearning in large language models (LLMs) aims to remove harmful training data while preserving overall utility. However, we find that existing metho

safetyarxiv-cs-ai

Unlocking air traffic flow prediction through microscopic aircraft-state modeling

arXiv:2605.10083v1 Announce Type: new Abstract: Short-term air traffic flow prediction in terminal airspace is essential for proactive air traffic management. Existing approaches predominantly model t

applicationsarxiv-cs-lg

Unmasking On-Policy Distillation: Where It Helps, Where It Hurts, and Why

arXiv:2605.10889v1 Announce Type: cross Abstract: On-policy distillation offers dense, per-token supervision for training reasoning models; however, it remains unclear under which conditions this sign

Unpredictability dissociates from structured control in language agents

arXiv:2605.09692v1 Announce Type: new Abstract: Unpredictable behavior is often taken as evidence of control, yet stochastic dispersion and structured action control need not coincide. This paper test

Unsupervised Process Reward Models

arXiv:2605.10158v1 Announce Type: new Abstract: Process Reward Models (PRMs) are a powerful mechanism for steering large language model reasoning by providing fine-grained, step-level supervision. How

safetyarxiv-cs-lg

Unveiling High-Probability Generalization in Decentralized SGD

arXiv:2605.10205v1 Announce Type: new Abstract: Decentralized stochastic gradient descent (D-SGD) is an efficient method for large-scale distributed learning. Existing generalization studies mainly ad