All entries

Token Radius Attention for Efficient Video Generation

arXiv:2608.02504v1 Announce Type: new Abstract: Video Diffusion Transformers (VDiTs) enable high-fidelity generation but incur quadratic cost from dense 3D self-attention. Existing head- and block-lev

Hardware

Toward Geometry-Scalable Whole-Body Touch for Humanoids: A 3D-Printed Conformal EIT Skin

DGX agent

arXiv:2608.02080v1 Announce Type: new Abstract: Whole-body tactile sensing is a prerequisite for humanoids that operate in contact-rich human environments, but conventional taxel arrays scale poorly w

hardwarearxiv-cs-ro

4 Aug 2026

Toward Plasticity-Preserving KL Regularization for Capability Retention in LLM Reinforcement Learning

arXiv:2608.01743v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become a central paradigm for large language model (LLM) post-training, but optimization toward new objectives can deg

safetyarxiv-cs-cl

Toward Robust LLM-Based Judges: Taxonomic Bias Evaluation and Debiasing Optimization

arXiv:2603.08091v2 Announce Type: replace Abstract: Large language model (LLM)-based judges are widely adopted for automated evaluation and reward modeling, yet their judgments are often affected by j

Towards a theory of morphology-driven marking in the lexicon: The case of the state

arXiv:2604.03422v2 Announce Type: replace Abstract: All languages have a noun category, but its realisation varies considerably. Depending on the language, semantic and/or morphosyntactic differences

Towards Anomaly Detection on Relational Data

arXiv:2606.18621v2 Announce Type: replace Abstract: Relational databases are widely used for managing structured data in real-world systems. Detecting anomalies from such relational data is crucial fo

Towards Compact Unified Multimodal Tracking: Synergizing Knowledge Distillation with Structural Pruning

arXiv:2608.01488v1 Announce Type: new Abstract: Unified multimodal object tracking has achieved remarkable robustness by leveraging complementary sensor data (e.g., RGB, Thermal, Depth), yet the heavy

Towards Effective Federated Multimodal Graph Learning via Navigating Multifaceted Heterogeneity

arXiv:2608.00623v1 Announce Type: new Abstract: Multimodal-attributed graphs (MAGs), where nodes carry heterogeneous semantic content across multiple modalities while edges encode relational dependenc

researcharxiv-cs-lg

Towards General Language-Conditioned Latent Safety Filters

arXiv:2608.00315v1 Announce Type: cross Abstract: Robot policies are becoming increasingly general, with vision-language-action (VLA) models enabling a single policy to execute diverse tasks specified

TRACE: Ergodic Trajectory Optimization for Active Scene Reconstruction

arXiv:2608.02304v1 Announce Type: new Abstract: Existing active reconstruction systems with Gaussian-splatting maps select observations greedily, optimizing a single next-best-view (NBV) at each step

researcharxiv-cs-ro

Local Ai

TRACE-TS: Attribution-Grounded and Traceable Sensor-Language Reasoning for Human Activity Understanding

arXiv:2608.00200v1 Announce Type: cross Abstract: Wearable sensors capture fine-grained motion patterns that support rich behavioral understanding, yet most existing methods reduce these signals to ac

local-aiarxiv-cs-cl

Track-Guided Hierarchical Reinforcement Learning for Autonomous Vehicle Drifting with Minimum-Lap-Time Planning

arXiv:2608.00113v1 Announce Type: new Abstract: In Formula 1, drivers optimize racing lines within tire grip limits to minimize lap times; however, in rally racing, drivers intentionally break tractio

safetyarxiv-cs-ro

Training Deep Morphological Neural Networks as Universal Approximators

arXiv:2505.09710v4 Announce Type: replace Abstract: We investigate deep morphological neural networks (DMNNs), studying how changes in algebraic structure affect the expressivity and trainability of d

Training-Free Out-of-Distribution Detection for Pathology Whole-Slide Images

arXiv:2608.01407v1 Announce Type: new Abstract: Safe deployment of AI methods in medicine requires robust guardrails that detect when input data deviate from the training distribution to ensure that m

Training-Free versus Training-Based Intent Classification in LLMs: Accuracy, Robustness, and Failure Modes

arXiv:2608.02415v1 Announce Type: new Abstract: Intent classification in Large Language Models (LLMs) involves categorizing user prompts into predefined classes. For instance, given a user prompt, the

Training nGPT

arXiv:2608.01284v1 Announce Type: new Abstract: The normalized Transformer (nGPT) realizes hyperspherical representation learning by constraining model parameter vectors and activation vectors to the

Training Small LLMs as Spatial Multi-Agent Policies

arXiv:2608.01425v1 Announce Type: cross Abstract: Training LLM-based multi-agent systems with multi-agent reinforcement learning is rapidly gaining traction, and a parallel line of work argues that su

Agents

Trajectories That Segment Themselves: Agent-Declared Boundaries as a Training Unit

arXiv:2608.02302v1 Announce Type: cross Abstract: Long-horizon coding-agent trajectories are poorly matched to the credit units available to train on: a single action has no stable value, an episode l

agentsarxiv-cs-lg

TRAM: Enhancing Multimodal Reasoning with Trajectory-Derived Auxiliary Memory

arXiv:2608.01922v1 Announce Type: new Abstract: Multimodal Large Reasoning Models (MLRMs) have achieved strong performance on tasks requiring visual understanding and multi-step inference. However, as

Applications

Transfer Learning of CATE with Kernel Ridge Regression

arXiv:2502.11331v4 Announce Type: replace-cross Abstract: The proliferation of data has sparked significant interest in leveraging findings from one study to estimate treatment effects in a different

applicationsarxiv-cs-lg

Transformer Geometry Observatory TGO-III: Semantic Geometry Observatory

arXiv:2608.01876v1 Announce Type: new Abstract: With the widespread adoption of Vision Transformers in modern AI, the need to analyze their inherent representational behavior has become increasingly i

Transformers perform adaptive partial pooling

arXiv:2602.03980v2 Announce Type: replace Abstract: Any language model must decide what to say in novel contexts based on information from similar contexts. But what about contexts that are not novel

TravKAN: Fast and Interpretable Nonlinear Traversability Analysis with Kolmogorov-Arnold Networks

arXiv:2608.02320v1 Announce Type: cross Abstract: Traversability analysis is a fundamental capability for autonomous mobile robots operating in unstructured environments. While modern machine learning

TreeProbe : A Tibetan Medicine Benchmark for Cultural Bias in LLMs

arXiv:2608.00640v1 Announce Type: new Abstract: Large language models are increasingly viewed as a potential means of mitigating global health inequities, yet their outputs often reflect dominant high

TrimMoE A communication aware and adaptive depth framework for distributed edge inference

arXiv:2608.00573v1 Announce Type: cross Abstract: Serving Mixture-of-Experts (MoE) large language models across distributed edge servers is bottlenecked by the cross-server expert transmission. The ex

Trust or Check? Understanding the (Evolutionary) Dynamics of User Trust in AI Systems

arXiv:2603.24742v2 Announce Type: replace-cross Abstract: As the capabilities and adoption of Artificial Intelligence (AI) systems grow, trust in these AI systems is an increasingly urgent concern. Mu

Trustworthiness Costs of Domain Adaptation in Small Language Models:A Cross-Architecture Empirical Study

arXiv:2608.00042v1 Announce Type: new Abstract: Domain adaptation of small language models (SLMs) has emerged as a practical strategy for deploying capable NLP systems in resource-constrained, high-st

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability

arXiv:2608.02238v1 Announce Type: cross Abstract: Ensuring trust in AI systems is essential for the safe and ethical integration of machine learning systems into high-stakes domains such as digital he

Local Ai

TS-MAMP: A Remanufactured Agricultural Robot Powered by Second-Life EV Components and NMS-Free On-Device Weed Detection

arXiv:2608.02270v1 Announce Type: new Abstract: Agriculture 4.0 robotic systems improve field efficiency yet remain too capital-intensive for the fragmented smallholdings that dominate global agricult

local-aiarxiv-cs-ro

TS-Reasoner: Aligning Time Series Foundation Models with LLM Reasoning

arXiv:2510.03519v2 Announce Type: replace Abstract: Time series reasoning is crucial to decision-making in diverse domains, including finance, energy, and scientific discovery. While existing time ser

safetyarxiv-cs-cl

Tunneling the Loss Landscape: Bypassing Memorization with Monte Carlo Parameter Swapping

arXiv:2608.01833v1 Announce Type: cross Abstract: Grokking is a striking phenomenon in neural network training, where a model can undergo a prolonged period of pure memorization before abrupt generali

TurboClear: One-Step Object-Effect Removal via Region-Calibrated Distribution Matching and Fusion

arXiv:2608.01288v1 Announce Type: new Abstract: Recently, diffusion-based removal methods have achieved promising visual quality in removing both target objects and their associated effects. However,

Applications

TWINS: A Tactile Wearable Isomorphic Arm Networked System for Contact-Rich Manipulation Learning

arXiv:2608.01733v1 Announce Type: new Abstract: Recent advances in robot learning for manipulation have increased the importance of collecting real-world demonstration data. However, existing robotic

applicationsarxiv-cs-ro

Two Sides of the Same Coin: Co-Evolving Search for Cross-Task Attacks on Vision-Language Models

arXiv:2608.02137v1 Announce Type: new Abstract: Vision-language models (VLMs) exhibit strong generalization across multimodal tasks but remain vulnerable to adversarial perturbations. Existing attacks

Two-Stage Bengali Sentiment Classification: Domain Adaptation Through Continual Learning and Parameter-Efficient Fine-Tuning

arXiv:2608.01471v1 Announce Type: new Abstract: Understanding sentiment in low-resource languages remains a key challenge for Natural Language Processing (NLP), particularly when domain-specific data

UAV-Based Environmental Monitoring of Rip-Current Indicators Using Wavelet-Derived Texture Features

arXiv:2608.02448v1 Announce Type: new Abstract: Rip currents are recurrent coastal natural hazards that threaten beachgoers and create operational challenges for lifeguards and coastal managers. Relia

UCBound-Net: Uncertainty-Guided Boundary-Aware Continual Learning for Domain-Incremental Ultrasound Segmentation

model-releasesarxiv-cs-cv

arXiv:2608.01518v1 Announce Type: new Abstract: Continual learning in clinical imaging faces a dual challenge: a model must assimilate knowledge from new anatomical domains while retaining representat

UDT: Reconciling U-Nets and Diffusion Transformers with Data-Adaptive Token Reduction

arXiv:2608.01298v1 Announce Type: new Abstract: Diffusion Transformers (DiTs) have emerged as a core architecture in generative modeling due to their scalability and adaptability to multimodal tasks.

Agents

UEmbed: Unified Sparse and Dense Multimodal Embeddings

arXiv:2608.02583v1 Announce Type: cross Abstract: Sparse retrieval underpins modern search systems, from web search to retrieval-augmented generation. Existing work has introduced Learned Sparse Retri

agentsarxiv-cs-cl

Local Ai

Uncertainty-Aware Simulation-Based Inference for Operations Research with Large Language Models

arXiv:2608.00019v1 Announce Type: new Abstract: Deploying large language models (LLMs) for operations research (OR) tasks remains challenging because correctness depends on a coherent modeling process

local-aiarxiv-cs-lg

Uncertainty-guided active learning for surrogate prediction of stream-finishing wear fields

arXiv:2608.00593v1 Announce Type: cross Abstract: In stream finishing, the wear experienced by a workpiece depends strongly on its orientation within the rotating abrasive media. Determining suitable

researcharxiv-cs-lg

Uncertainty Is Not Enough: Value-of-Information Routing for Mixtures of LoRA Experts

arXiv:2608.02528v1 Announce Type: new Abstract: Mixtures of low-rank adaptation experts increase parameter-efficient capacity by routing each input through a subset of adapters. Recent dynamic routers

Applications

Uncertainty Quantification for Visual Object Pose Estimation: S-Lemma Ellipsoidal Bounds

arXiv:2511.21666v2 Announce Type: replace-cross Abstract: Quantifying the uncertainty of an object's pose estimate is essential for robust control and planning. Although pose estimation is a well-stud

applicationsarxiv-cs-cv

Uncovering and Mitigating Positional Blind Spots in Vision-Language-Action Models

arXiv:2608.01573v1 Announce Type: new Abstract: Recent Vision-Language-Action (VLA) models achieve promising performance in robotic manipulation, typically measured by success rates aggregated over pr

safetyarxiv-cs-ro

Understanding and Correcting Low-Frequency Bias in EEG Foundation Model

arXiv:2608.01898v1 Announce Type: new Abstract: Increasing EEG pretraining data scale or model capacity does not consistently improve downstream performance. We identify a persistent low-frequency bia

Understanding and Overcoming Cross-modal Fusion Bias in Multimodal Anomaly Detection From A Fisher Information Perspective

arXiv:2608.00986v1 Announce Type: new Abstract: Current advancements in Multimodal Anomaly Detection (MAD) are largely driven by enhancing multimodal fusion, particularly through the integration of RG

Understanding Sparse Attention Selectivity in Long-Context Foundation Models via Counterfactual Evaluation

arXiv:2608.01676v1 Announce Type: new Abstract: Sparse attention is widely deployed in long-context serving stacks, yet no framework audits how discarding blocks changes the influence of specific cont

Understanding Synergistic Interactions among Pathology Foundation Models via Adaptive Fusion

arXiv:2608.01370v1 Announce Type: new Abstract: Pathology foundation models (PFMs) provide strong tile-level representations via self-supervised pre-training on large-scale pathology images. Yet, PFMs