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The Feedback Hamiltonian is the Score Function: A Diffusion-Model Framework for Quantum Trajectory Reversal

arXiv:2604.21210v1 Announce Type: cross Abstract: In continuously monitored quantum systems, the feedback protocol of Garcia-Pintos, Liu, and Gorshkov reshapes the arrow of time: a Hamiltonian H_{meas

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The First Challenge on Remote Sensing Infrared Image Super-Resolution at NTIRE 2026: Benchmark Results and Method Overview

DGX agent

arXiv:2604.21312v1 Announce Type: cross Abstract: This paper presents the NTIRE 2026 Remote Sensing Infrared Image Super-Resolution (x4) Challenge, one of the associated challenges of NTIRE 2026. The

model-releasesarxiv-cs-ai

24 Apr 2026

Agents

The Last Harness You'll Ever Build

arXiv:2604.21003v1 Announce Type: new Abstract: AI agents are increasingly deployed on complex, domain-specific workflows -- navigating enterprise web applications that require dozens of clicks and fo

agentsarxiv-cs-ai

The Path Not Taken: Duality in Reasoning about Program Execution

arXiv:2604.20917v1 Announce Type: cross Abstract: Large language models (LLMs) have shown remarkable capabilities across diverse coding tasks. However, their adoption requires a true understanding of

The Recurrent Transformer: Greater Effective Depth and Efficient Decoding

arXiv:2604.21215v1 Announce Type: new Abstract: Transformers process tokens in parallel but are temporally shallow: at position t, each layer attends to key-value pairs computed based on the previous

The Root Theorem of Context Engineering

model-releasesarxiv-cs-cl

arXiv:2604.20874v1 Announce Type: cross Abstract: Every system that maintains a large language model conversation beyond a single session faces two inescapable constraints: the context window is finit

The Sample Complexity of Multicalibration

arXiv:2604.21923v1 Announce Type: new Abstract: We study the minimax sample complexity of multicalibration in the batch setting. A learner observes n i.i.d. samples from an unknown distribution and mu

There Will Be a Scientific Theory of Deep Learning

arXiv:2604.21691v1 Announce Type: cross Abstract: In this paper, we make the case that a scientific theory of deep learning is emerging. By this we mean a theory which characterizes important properti

Thinking Like a Botanist: Challenging Multimodal Language Models with Intent-Driven Chain-of-Inquiry

arXiv:2604.20983v1 Announce Type: cross Abstract: Vision evaluations are typically done through multi-step processes. In most contemporary fields, experts analyze images using structured, evidence-bas

Tutorials

Thinking with Reasoning Skills: Fewer Tokens, More Accuracy

arXiv:2604.21764v1 Announce Type: new Abstract: Reasoning LLMs often spend substantial tokens on long intermediate reasoning traces (e.g., chain-of-thought) when solving new problems. We propose to su

tutorialsarxiv-cs-ai

'This Wasn't Made for Me': Recentering User Experience and Emotional Impact in the Evaluation of ASR Bias

arXiv:2604.21148v1 Announce Type: new Abstract: Studies on bias in Automatic Speech Recognition (ASR) tend to focus on reporting error rates for speakers of underrepresented dialects, yet less researc

safetyarxiv-cs-cl

Time, Causality, and Observability Failures in Distributed AI Inference Systems

arXiv:2604.21361v1 Announce Type: new Abstract: Distributed AI inference pipelines rely heavily on timestamp-based observability to understand system behavior. This work demonstrates that even small c

TimePre: Bridging Accuracy, Efficiency, and Stability in Probabilistic Time-Series Forecasting

model-releasesarxiv-cs-cv

arXiv:2511.18539v2 Announce Type: replace-cross Abstract: We propose TimePre, a simple framework that unifies the efficiency of Multilayer Perceptron (MLP)-based models with the distributional flexibi

TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale

arXiv:2604.21889v1 Announce Type: cross Abstract: Real-time detection and mitigation of technical anomalies are critical for large-scale cloud-native services, where even minutes of downtime can resul

applicationsarxiv-cs-ai

To See the Unseen: on the Generalization Ability of Transformers in Symbolic Reasoning

arXiv:2604.21632v1 Announce Type: new Abstract: We investigate the ability of decoder-only transformer models to perform abstract symbolic reasoning; specifically solving propositional logic reasoning

Tool Attention Is All You Need: Dynamic Tool Gating and Lazy Schema Loading for Eliminating the MCP/Tools Tax in Scalable Agentic Workflows

arXiv:2604.21816v1 Announce Type: new Abstract: The Model Context Protocol (MCP) has become a common interface for connecting large language model (LLM) agents to external tools, but its reliance on s

Toward a Multi-Layer ML-Based Security Framework for Industrial IoT

arXiv:2603.24111v3 Announce Type: replace-cross Abstract: The Industrial Internet of Things (IIoT) introduces significant security challenges as resource-constrained devices become increasingly integr

applicationsarxiv-cs-lg

Toward Efficient Membership Inference Attacks against Federated Large Language Models: A Projection Residual Approach

arXiv:2604.21197v1 Announce Type: new Abstract: Federated Large Language Models (FedLLMs) enable multiple parties to collaboratively fine-tune LLMs without sharing raw data, addressing challenges of l

Towards a Systematic Risk Assessment of Deep Neural Network Limitations in Autonomous Driving Perception

arXiv:2604.20895v1 Announce Type: cross Abstract: Safety and security are essential for the admission and acceptance of automated and autonomous vehicles. Deep neural networks (DNNs) are widely used f

safetyarxiv-cs-lg

Towards Multimodal Active Learning: Efficient Learning with Limited Paired Data

arXiv:2510.03247v2 Announce Type: replace-cross Abstract: Active learning (AL) is a principled strategy to reduce annotation cost in data-hungry deep learning. However, existing AL algorithms focus al

Towards Universal Tabular Embeddings: A Benchmark Across Data Tasks

arXiv:2604.21696v1 Announce Type: new Abstract: Tabular foundation models aim to learn universal representations of tabular data that transfer across tasks and domains, enabling applications such as t

TRACES: Tagging Reasoning Steps for Adaptive Cost-Efficient Early-Stopping

arXiv:2604.21057v1 Announce Type: new Abstract: The field of Language Reasoning Models (LRMs) has been very active over the past few years with advances in training and inference techniques enabling L

TraceScope: Interactive URL Triage via Decoupled Checklist Adjudication

arXiv:2604.21840v1 Announce Type: cross Abstract: Modern phishing campaigns increasingly evade snapshot-based URL classifiers using interaction gates (e.g., checkbox/slider challenges), delayed conten

Transfer Learning for Loan Recovery Prediction under Distribution Shifts with Heterogeneous Feature Spaces

arXiv:2604.02832v2 Announce Type: replace-cross Abstract: Accurate forecasting of recovery rates (RR) is central to credit risk management and regulatory capital determination. In many loan portfolios

applicationsarxiv-cs-lg

Tutorials

Transferable Physics-Informed Representations via Closed-Form Head Adaptation

arXiv:2604.21761v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) have garnered significant interest for their potential in solving partial differential equations (PDEs) that go

tutorialsarxiv-cs-lg

Transferable SCF-Acceleration through Solver-Aligned Initialization Learning

arXiv:2604.21657v1 Announce Type: new Abstract: Machine learning methods that predict initial guesses from molecular geometry can reduce this cost, but matrix-prediction models fail when extrapolating

Transformer-Progressive Mamba Network for Lightweight Image Super-Resolution

arXiv:2511.03232v2 Announce Type: replace Abstract: Recently, Mamba-based super-resolution (SR) methods have demonstrated the ability to capture global receptive fields with linear complexity, address

researcharxiv-cs-cv

Transient Turn Injection: Exposing Stateless Multi-Turn Vulnerabilities in Large Language Models

arXiv:2604.21860v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly integrated into sensitive workflows, raising the stakes for adversarial robustness and safety. This pape

TRAVELFRAUDBENCH: A Configurable Evaluation Framework for GNN Fraud Ring Detection in Travel Networks

arXiv:2604.21093v1 Announce Type: cross Abstract: We introduce TravelFraudBench (TFG), a configurable benchmark for evaluating graph neural networks (GNNs) on fraud ring detection in travel platform g

Trust but Verify: Introducing DAVinCI -- A Framework for Dual Attribution and Verification in Claim Inference for Language Models

arXiv:2604.21193v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable fluency and versatility across a wide range of NLP tasks, yet they remain prone to factual ina

applicationsarxiv-cs-ai

Trust-SSL: Additive-Residual Selective Invariance for Robust Aerial Self-Supervised Learning

arXiv:2604.21349v1 Announce Type: cross Abstract: Self-supervised learning (SSL) is a standard approach for representation learning in aerial imagery. Existing methods enforce invariance between augme

Trustworthy Clinical Decision Support Using Meta-Predicates and Domain-Specific Languages

arXiv:2604.21263v1 Announce Type: new Abstract: extbf{Background:} Regulatory frameworks for AI in healthcare, including the EU AI Act and FDA guidance on AI/ML-based medical devices, require clinical

Tumor-anchored deep feature random forests for out-of-distribution detection in lung cancer segmentation

arXiv:2512.08216v3 Announce Type: replace-cross Abstract: Accurate segmentation of lung tumors from 3D computed tomography (CT) scans is essential for automated treatment planning and response assessm

safetyarxiv-cs-cv

Tuning for TraceTarnish: Techniques, Trends, and Testing Tangible Traits

arXiv:2512.03465v3 Announce Type: replace-cross Abstract: In this study, we more rigorously evaluated our attack script extit{TraceTarnish}, which leverages adversarial stylometry principles to anonym

TV Subgradient-Guided Multi-Source Fusion for Spectral Imaging in Dual-Camera CASSI Systems

arXiv:2509.10897v2 Announce Type: replace Abstract: Balancing spectral, spatial, and temporal resolutions is a key challenge in spectral imaging. The Dual-Camera Coded Aperture Snapshot Spectral Imagi

applicationsarxiv-cs-cv

UAU-Net: Uncertainty-aware Representation Learning and Evidential Classification for Facial Action Unit Detection

arXiv:2604.21227v1 Announce Type: new Abstract: Facial action unit (AU) detection remains challenging because it involves heterogeneous, AU-specific uncertainties arising at both the representation an

researcharxiv-cs-cv

UbiQVision: Quantifying Uncertainty in XAI for Image Recognition

arXiv:2512.20288v2 Announce Type: replace-cross Abstract: Recent advances in deep learning have led to its widespread adoption across diverse domains, including medical imaging. This progress is drive

researcharxiv-cs-ai

Local Ai

Ufil: A Unified Framework for Infrastructure-based Localization

arXiv:2604.21471v1 Announce Type: new Abstract: Infrastructure-based localization enhances road safety and traffic management by providing state estimates of road users. Development is hindered by fra

local-aiarxiv-cs-ro

Local Ai

UHR-DETR: Efficient End-to-End Small Object Detection for Ultra-High-Resolution Remote Sensing Imagery

arXiv:2604.21435v1 Announce Type: new Abstract: Ultra-High-Resolution (UHR) imagery has become essential for modern remote sensing, offering unprecedented spatial coverage. However, detecting small ob

local-aiarxiv-cs-cv

UKP_Psycontrol at SemEval-2026 Task 2: Modeling Valence and Arousal Dynamics from Text

arXiv:2604.21534v1 Announce Type: new Abstract: This paper presents our system developed for SemEval-2026 Task 2. The task requires modeling both current affect and short-term affective change in chro

Unbiased Prevalence Estimation with Multicalibrated LLMs

arXiv:2604.21549v1 Announce Type: new Abstract: Estimating the prevalence of a category in a population using imperfect measurement devices (diagnostic tests, classifiers, or large language models) is

Understanding and Mitigating Spurious Signal Amplification in Test-Time Reinforcement Learning for Math Reasoning

arXiv:2604.21327v1 Announce Type: cross Abstract: Test-time reinforcement learning (TTRL) always adapts models at inference time via pseudo-labeling, leaving it vulnerable to spurious optimization sig

UniGenDet: A Unified Generative-Discriminative Framework for Co-Evolutionary Image Generation and Generated Image Detection

arXiv:2604.21904v1 Announce Type: new Abstract: In recent years, significant progress has been made in both image generation and generated image detection. Despite their rapid, yet largely independent

safetyarxiv-cs-cv

Unlocking Multi-Spectral Data for Multi-Modal Models with Guided Inputs and Chain-of-Thought Reasoning

model-releasesarxiv-cs-cv

arXiv:2604.21032v1 Announce Type: new Abstract: Multi-spectral imagery is a valuable input signal for Remote Sensing applications, such as land-use and land-cover classification and environmental moni

Local Ai

Unlocking the Power of Critical Factors for 3D Visual Geometry Estimation

arXiv:2604.21713v1 Announce Type: new Abstract: Feed-forward visual geometry estimation has recently made rapid progress. However, an important gap remains: multi-frame models usually produce better c

local-aiarxiv-cs-cv

Unlocking the Power of Large Language Models for Multi-table Entity Matching

arXiv:2604.21238v1 Announce Type: new Abstract: Multi-table entity matching (MEM) addresses the limitations of dual-table approaches by enabling simultaneous identification of equivalent entities acro

Unsharp Measurement with Adaptive Gaussian POVMs for Quantum-Inspired Image Processing

model-releasesarxiv-cs-cv

arXiv:2604.04685v2 Announce Type: replace-cross Abstract: We propose a data-adaptive probabilistic intensity remapping framework for structure-preserving transformation of grayscale images. The sugges

Unsupervised Learning of Inter-Object Relationships via Group Homomorphism

arXiv:2604.20925v1 Announce Type: new Abstract: While current deep learning models achieve high performance by learning statistical correlations from vast datasets,which stands in stark contrast to hu