Search: “models”

Sub-Token Routing in LoRA for Adaptation and Query-Aware KV Compression

arXiv:2604.21335v1 Announce Type: cross Abstract: Sub-token routing offers a finer control axis for transformer efficiency than the coarse units used in most prior work, such as tokens, pages, heads,

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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

The Recurrent Transformer: Greater Effective Depth and Efficient Decoding

model-releasesarxiv-cs-lg

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

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

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

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

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

Using Machine Mental Imagery for Representing Common Ground in Situated Dialogue

arXiv:2604.21144v1 Announce Type: cross Abstract: Situated dialogue requires speakers to maintain a reliable representation of shared context rather than reasoning only over isolated utterances. Curre

Verifying Machine Learning Interpretability Requirements through Provenance

arXiv:2604.21599v1 Announce Type: cross Abstract: Machine Learning (ML) Engineering is a growing field that necessitates an increase in the rigor of ML development. It draws many ideas from software e

VLAA-GUI: Knowing When to Stop, Recover, and Search, A Modular Framework for GUI Automation

arXiv:2604.21375v1 Announce Type: cross Abstract: Autonomous GUI agents face two fundamental challenges: early stopping, where agents prematurely declare success without verifiable evidence, and repet

ACT: Anti-Crosstalk Learning for Cross-Sectional Stock Ranking via Temporal Disentanglement and Structural Purification

arXiv:2604.20204v1 Announce Type: new Abstract: Cross-sectional stock ranking is a fundamental task in quantitative investment, relying on both temporal modeling of individual stocks and the capture o

AdaTracker: Learning Adaptive In-Context Policy for Cross-Embodiment Active Visual Tracking

arXiv:2604.20305v1 Announce Type: new Abstract: Realizing active visual tracking with a single unified model across diverse robots is challenging, as the physical constraints and motion dynamics vary

safetyarxiv-cs-ro

AI to Learn 2.0: A Deliverable-Oriented Governance Framework and Maturity Rubric for Opaque AI in Learning-Intensive Domains

arXiv:2604.19751v1 Announce Type: new Abstract: Generative AI is entering research, education, and professional work faster than current governance frameworks can specify how AI-assisted outputs shoul

Hardware

Amortized Vine Copulas for High-Dimensional Density and Information Estimation

arXiv:2604.20568v1 Announce Type: new Abstract: Modeling high-dimensional dependencies while keeping likelihoods tractable remains challenging. Classical vine-copula pipelines are interpretable but ca

hardwarearxiv-cs-lg

Automated Detection of Dosing Errors in Clinical Trial Narratives: A Multi-Modal Feature Engineering Approach with LightGBM

arXiv:2604.19759v1 Announce Type: new Abstract: Clinical trials require strict adherence to medication protocols, yet dosing errors remain a persistent challenge affecting patient safety and trial int

Avoiding Overthinking and Underthinking: Curriculum-Aware Budget Scheduling for LLMs

arXiv:2604.19780v1 Announce Type: new Abstract: Scaling test-time compute via extended reasoning has become a key paradigm for improving the capabilities of large language models (LLMs). However, exis

safetyarxiv-cs-cl

Agents

Behavioral Transfer in AI Agents: Evidence and Privacy Implications

arXiv:2604.19925v1 Announce Type: cross Abstract: AI agents powered by large language models are increasingly acting on behalf of humans in social and economic environments. Prior research has focused

agentsarxiv-cs-ai

Bio-inspired Color Constancy: From Gray Anchoring Theory to Gray Pixel Methods

arXiv:2604.20243v1 Announce Type: new Abstract: Color constancy is a fundamental ability of many biological visual systems and a crucial step in computer imaging systems. Bio-inspired modeling offers

researcharxiv-cs-cv

CARLA-Air: Fly Drones Inside a CARLA World -- A Unified Infrastructure for Air-Ground Embodied Intelligence

arXiv:2603.28032v2 Announce Type: replace-cross Abstract: The convergence of low-altitude economies, embodied intelligence, and air-ground cooperative systems creates growing demand for simulation inf

Chasing the Public Score: User Pressure and Evaluation Exploitation in Coding Agent Workflows

model-releasesarxiv-cs-cl

arXiv:2604.20200v1 Announce Type: new Abstract: Frontier coding agents are increasingly used in workflows where users supervise progress primarily through repeated improvement of a public score, namel

ConeSep: Cone-based Robust Noise-Unlearning Compositional Network for Composed Image Retrieval

model-releasesarxiv-cs-cv

arXiv:2604.20358v1 Announce Type: new Abstract: The Composed Image Retrieval (CIR) task provides a flexible retrieval paradigm via a reference image and modification text, but it heavily relies on exp

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation

arXiv:2604.20596v1 Announce Type: new Abstract: Federated learning (FL) enables training of a global model while keeping raw data on end-devices. Despite this, FL has shown to leak private user inform

Applications

Efficient INT8 Single-Image Super-Resolution via Deployment-Aware Quantization and Teacher-Guided Training

arXiv:2604.20291v1 Announce Type: new Abstract: Efficient single-image super-resolution (SISR) requires balancing reconstruction fidelity, model compactness, and robustness under low-bit deployment, w

applicationsarxiv-cs-cv

Evidence of Layered Positional and Directional Constraints in the Voynich Manuscript: Implications for Cipher-Like Structure

model-releasesarxiv-cs-cl

arXiv:2604.19762v1 Announce Type: new Abstract: The Voynich Manuscript (VMS) exhibits a script of uncertain origin whose grapheme sequences have resisted linguistic analysis. We present a systematic a

Exploiting LLM-as-a-Judge Disposition on Free Text Legal QA via Prompt Optimization

arXiv:2604.20726v1 Announce Type: cross Abstract: This work explores the role of prompt design and judge selection in LLM-as-a-Judge evaluations of free text legal question answering. We examine wheth

FeDa4Fair: Client-Level Federated Datasets for Fairness Evaluation

arXiv:2506.21095v4 Announce Type: replace-cross Abstract: Federated Learning (FL) enables collaborative training while preserving privacy, yet it introduces a critical challenge: the 'illusion of fair

Fextsuperscript{2}LP-AP: Fast & Flexible Label Propagation with Adaptive Propagation Kernel

model-releasesarxiv-cs-lg

arXiv:2604.20736v1 Announce Type: new Abstract: Semi-supervised node classification is a foundational task in graph machine learning, yet state-of-the-art Graph Neural Networks (GNNs) are hindered by

Forecasting Individual NetFlows using a Predictive Masked Graph Autoencoder

arXiv:2604.20483v1 Announce Type: cross Abstract: In this paper, we propose a proof-of-concept Graph Neural Network model that can successfully predict network flow-level traffic (NetFlow) by accurate

From Diffusion to Flow: Efficient Motion Generation in MotionGPT3

arXiv:2603.26747v2 Announce Type: replace Abstract: Recent text-driven motion generation methods span both discrete token-based approaches and continuous-latent formulations. MotionGPT3 exemplifies th

researcharxiv-cs-cv

From Signal Degradation to Computation Collapse: Uncovering the Two Failure Modes of LLM Quantization

arXiv:2604.19884v1 Announce Type: cross Abstract: Post-Training Quantization (PTQ) is critical for the efficient deployment of Large Language Models (LLMs). While 4-bit quantization is widely regarded

Graph2Counsel: Clinically Grounded Synthetic Counseling Dialogue Generation from Client Psychological Graphs

arXiv:2604.20382v1 Announce Type: new Abstract: Rising demand for mental health support has increased interest in using Large Language Models (LLMs) for counseling. However, adapting LLMs to this high

safetyarxiv-cs-cl

KANMixer: a minimal KAN-centered mixer for long-term time series forecasting

model-releasesarxiv-cs-lg

arXiv:2508.01575v2 Announce Type: replace Abstract: Long-term time series forecasting (LTSF) underpins critical applications from energy management to weather prediction, yet achieving reliable multi-

Local Ai

LEAD: Breaking the No-Recovery Bottleneck in Long-Horizon Reasoning

arXiv:2603.06870v2 Announce Type: replace Abstract: Long-horizon execution in Large Language Models (LLMs) remains unstable even when high-level strategies are provided. Evaluating on controlled algor

local-aiarxiv-cs-ai

Learning to Evolve: A Self-Improving Framework for Multi-Agent Systems via Textual Parameter Graph Optimization

arXiv:2604.20714v1 Announce Type: new Abstract: Designing and optimizing multi-agent systems (MAS) is a complex, labor-intensive process of 'Agent Engineering.' Existing automatic optimization methods

LiteResearcher: A Scalable Agentic RL Training Framework for Deep Research Agent

arXiv:2604.17931v2 Announce Type: replace Abstract: Reinforcement Learning (RL) has emerged as a powerful training paradigm for LLM-based agents. However, scaling agentic RL for deep research remains

LLM-guided phase diagram construction through high-throughput experimentation

arXiv:2604.20304v1 Announce Type: cross Abstract: Constructing phase diagrams for multicomponent alloys requires extensive experimental measurements and is a time-consuming task. Here we investigate w

Measuring Creativity in the Age of Generative AI: Distinguishing Human and AI-Generated Creative Performance in Hiring and Talent Systems

arXiv:2604.19799v1 Announce Type: cross Abstract: Generative AI is rapidly transforming how organizations create value and evaluate talent. While large language models enhance baseline output quality,

MLG-Stereo: ViT Based Stereo Matching with Multi-Stage Local-Global Enhancement

model-releasesarxiv-cs-cv

arXiv:2604.20393v1 Announce Type: new Abstract: With the development of deep learning, ViT-based stereo matching methods have made significant progress due to their remarkable robustness and zero-shot

Normalizing Flows with Iterative Denoising

arXiv:2604.20041v1 Announce Type: cross Abstract: Normalizing Flows (NFs) are a classical family of likelihood-based methods that have received revived attention. Recent efforts such as TARFlow have s

Optimizing Data Augmentation for Real-Time Small UAV Detection: A Lightweight Context-Aware Approach

arXiv:2604.19999v1 Announce Type: new Abstract: Visual detection of Unmanned Aerial Vehicles (UAVs) is a critical task in surveillance systems due to their small physical size and environmental challe

researcharxiv-cs-cv

Physics-Enhanced Deep Learning for Proactive Thermal Runaway Forecasting in Li-Ion Batteries

arXiv:2604.20175v1 Announce Type: cross Abstract: Accurate prediction of thermal runaway in lithium-ion batteries is essential for ensuring the safety, efficiency, and reliability of modern energy sto

safetyarxiv-cs-ai

PipeMFL-240K: A Large-scale Dataset and Benchmark for Object Detection in Pipeline Magnetic Flux Leakage Imaging

arXiv:2602.07044v2 Announce Type: replace-cross Abstract: Pipeline integrity is critical to industrial safety and environmental protection, with Magnetic Flux Leakage (MFL) detection being a primary n

Pre-Execution Query Slot-Time Prediction in Cloud Data Warehouses: A Feature-Scoped Machine Learning Approach

arXiv:2604.20145v1 Announce Type: cross Abstract: Cloud data warehouses bill compute based on slot-time consumed. In shared multi-tenant environments, query cost is highly variable and hard to estimat

QuantaAlpha: An Evolutionary Framework for LLM-Driven Alpha Mining

arXiv:2602.07085v2 Announce Type: replace-cross Abstract: Financial markets are noisy and non-stationary, making alpha mining highly sensitive to noise in backtesting results and sudden market regime

REVNET: Rotation-Equivariant Point Cloud Completion via Vector Neuron Anchor Transformer

model-releasesarxiv-cs-cv

arXiv:2601.08558v2 Announce Type: replace Abstract: Incomplete point clouds captured by 3D sensors often result in the loss of both geometric and semantic information. Most existing point cloud comple

RSRCC: A Remote Sensing Regional Change Comprehension Benchmark Constructed via Retrieval-Augmented Best-of-N Ranking

arXiv:2604.20623v1 Announce Type: cross Abstract: Traditional change detection identifies where changes occur, but does not explain what changed in natural language. Existing remote sensing change cap

Local Ai

SAKE: Self-aware Knowledge Exploitation-Exploration for Grounded Multimodal Named Entity Recognition

arXiv:2604.20146v1 Announce Type: cross Abstract: Grounded Multimodal Named Entity Recognition (GMNER) aims to extract named entities and localize their visual regions within image-text pairs, serving

local-aiarxiv-cs-cl