Search: “research”

Caries DETR: Tooth Structure-aware Prior and Lesion-aware Dynamic Loss Refinement for DETR Based Caries Detection

arXiv:2604.23718v1 Announce Type: new Abstract: As dental caries appear as subtle, low-contrast lesions in intraoral imaging, existing deep learning models face significant challenges in the early det

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Research

Characterizations of Admissible Objective Functions for Hierarchical Clustering

DGX agent

arXiv:2604.23628v1 Announce Type: cross Abstract: Hierarchical clustering is a fundamental task in data analysis, yet for a long time it lacked a principled objective function. Dasgupta [STOC 2016] in

researcharxiv-cs-lg

28 Apr 2026

Characterizing Vision-Language-Action Models across XPUs: Constraints and Acceleration for On-Robot Deployment

arXiv:2604.24447v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models are promising for generalist robot control, but on-robot deployment is bottlenecked by real-time inference under t

ChatR1: Reinforcement Learning for Conversational Reasoning and Retrieval Augmented Question Answering

arXiv:2510.13312v2 Announce Type: replace Abstract: We present ChatR1, a reasoning framework based on reinforcement learning (RL) for conversational question answering (CQA). Reasoning plays an import

CheXmix: Unified Generative Pretraining for Vision Language Models in Medical Imaging

arXiv:2604.22989v1 Announce Type: cross Abstract: Recent medical multimodal foundation models are built as multimodal LLMs (MLLMs) by connecting a CLIP-pretrained vision encoder to an LLM using LLaVA-

Model Releases

ClimAgent: LLM as Agents for Autonomous Open-ended Climate Science Analysis

model-releasesarxiv-cs-ai

arXiv:2604.16922v2 Announce Type: replace Abstract: Climate research is pivotal for mitigating global environmental crises, yet the accelerating volume of multi-scale datasets and the complexity of an

CLIP-Guided Data Augmentation for Night-Time Image Dehazing

arXiv:2604.05500v2 Announce Type: replace Abstract: Nighttime image dehazing faces a more complex degradation pattern than its daytime counterpart, as haze scattering couples with low illumination, no

Coarse-to-Real: Generative Rendering for Populated Dynamic Scenes

arXiv:2601.22301v2 Announce Type: replace Abstract: Traditional rendering pipelines rely on complex assets, accurate materials and lighting, and substantial computational resources to produce realisti

CodecSep: Prompt-Driven Universal Sound Separation on Neural Audio Codec Latents

arXiv:2509.11717v5 Announce Type: replace-cross Abstract: Text-guided sound separation enables flexible audio editing, assistive listening, and open-domain source extraction, but systems such as Audio

Computational Design and Experimental Validation of Photoactive PARP1 Inhibitors

arXiv:2604.24634v1 Announce Type: cross Abstract: Light-activated drugs are a promising way to treat localized diseases for which existing treatments have severe side effects. However, their developme

Conditional Score-Based Modeling of Effective Langevin Dynamics

arXiv:2604.23952v1 Announce Type: cross Abstract: Stochastic reduced-order models are widely used to represent the effective dynamics of complex systems, but estimating their drift and diffusion coeff

Conformal PM2.5 Mapping Under Spatial Covariate Shift: Satellite-Reanalysis Fusion for Africa's Green Industrial Transition

arXiv:2604.22787v1 Announce Type: cross Abstract: Africa's green industrialization imperative demands reliable infrastructure for monitoring air quality. We present a satellite-reanalysis PM2.5 fusion

Consistency of Lloyd's Algorithm Under Perturbations

arXiv:2309.00578v2 Announce Type: replace Abstract: In the context of unsupervised learning, Lloyd's algorithm is one of the most widely used clustering algorithms. It has inspired a plethora of work

Context-Integrated Adversarial Learning for Predictive Modelling of Stock Price Dynamics

arXiv:2604.22801v1 Announce Type: cross Abstract: It is a challenging task to forecast equity prices in fast moving financial markets as this becomes even more difficult when the predictive signal is

Contextual Linear Activation Steering of Language Models

arXiv:2604.24693v1 Announce Type: new Abstract: Linear activation steering is a powerful approach for eliciting the capabilities of large language models and specializing their behavior using limited

Continual Calibration: Coverage Can Collapse Before Accuracy in Lifelong LLM Fine-Tuning

arXiv:2604.23987v1 Announce Type: new Abstract: Continual learning for large language models is typically evaluated through accuracy retention under sequential fine-tuning. We argue that this perspect

CoreGuard: Safeguarding Foundational Capabilities of LLMs Against Model Stealing in Edge Deployment

arXiv:2410.13903v3 Announce Type: replace-cross Abstract: Proprietary large language models (LLMs) exhibit strong generalization capabilities across diverse tasks and are increasingly deployed on edge

Credal Concept Bottleneck Models for Epistemic-Aleatoric Uncertainty Decomposition

arXiv:2604.24170v1 Announce Type: new Abstract: Concept Bottleneck Models (CBMs) predict through human-interpretable concepts, but they typically output point concept probabilities that conflate epist

Culture-Aware Machine Translation in Large Language Models: Benchmarking and Investigation

arXiv:2604.24361v1 Announce Type: new Abstract: Large language models (LLMs) have achieved strong performance in general machine translation, yet their ability in culture-aware scenarios remains poorl

Decentralized Heterogeneous Multi-Robot Collaborative Exploration for Indoor and Outdoor 3D Environments

arXiv:2604.23693v1 Announce Type: new Abstract: Heterogeneous multi-robot systems feature significant adaptability for complex environments. However, effective collaboration that fully exploits the ro

researcharxiv-cs-ro

Deep Clustering for Climate: Analyzing Teleconnections through Learned Categorical States

arXiv:2604.22909v1 Announce Type: new Abstract: Understanding and representing complex climate variability is essential for both scientific analysis and predictive modeling. However, identifying meani

DepthKV: Layer-Dependent KV Cache Pruning for Long-Context LLM Inference

arXiv:2604.24647v1 Announce Type: cross Abstract: Long-context reasoning is a critical capability of large language models (LLMs), enabling applications such as long-document understanding, summarizat

Designing Robots to Support Parent-Child Connections: Opportunities Through Robot-Mediated Communication

arXiv:2604.23976v1 Announce Type: new Abstract: The sense of family connectedness may support positive outcomes including individual well-being, resilience, and healthy family functioning. However, as

researcharxiv-cs-ro

Diagnostic-Driven Layer-Wise Compensation for Post-Training Quantization of Encoder-Decoder ASR Models

arXiv:2601.02455v2 Announce Type: replace-cross Abstract: Deploying Automatic Speech Recognition (ASR) models on memory-constrained edge devices requires aggressive low-bit weight quantization. Layer-

Dialysis Risk Prediction and Treatment Effect Estimation for AKI patients using Longitudinal Electronic Health Records

arXiv:2604.24547v1 Announce Type: new Abstract: Progression to dialysis or end-stage renal disease is a rare but clinically important outcome. Clinicians need evidence on how medication exposures infl

DiffuSAM: Diffusion-Based Prompt-Free SAM2 for Few-Shot and Source-Free Medical Image Segmentation

arXiv:2604.24719v1 Announce Type: new Abstract: Segmentation models such as Segment Anything Model (SAM) and SAM2 achieve strong prompt-driven zero-shot performance. However, their training on natural

Diffusion-Guided Feature Selection via Nishimori Temperature: Noise-Based Spectral Embedding

arXiv:2604.24692v1 Announce Type: new Abstract: We propose Noise-Based Spectral Embedding (NBSE), a physics-informed framework for selecting informative features from high-dimensional data without gre

Diffusion Model as a Generalist Segmentation Learner

arXiv:2604.24575v1 Announce Type: new Abstract: Diffusion models are primarily trained for image synthesis, yet their denoising trajectories encode rich, spatially aligned visual priors. In this paper

Discriminator-Guided Adaptive Diffusion for Source-Free Test-Time Adaptation under Image Corruptions

arXiv:2604.23636v1 Announce Type: new Abstract: In this work, we study Source-Free Unsupervised Domain Adaptation under corruption-induced domain shifts, where performance degradation is caused by nat

DNNs, Dataset Statistics, and Correlation Functions

arXiv:2511.21715v2 Announce Type: replace-cross Abstract: This paper argues that dataset structure is important in image recognition tasks (among other tasks). Specifically, we focus on the nature and

DRP: Distilled Reasoning Pruning with Skill-aware Step Decomposition for Efficient Large Reasoning Models

arXiv:2505.13975v4 Announce Type: replace Abstract: While Large Reasoning Models (LRMs) have demonstrated success in complex reasoning tasks through long chain-of-thought (CoT) reasoning, their infere

Dual Control of Linear Systems from Bilinear Observations with Belief Space Model Predictive Control

arXiv:2604.24663v1 Announce Type: cross Abstract: We study finite-horizon quadratic control of linear systems with bilinear observations, in which the control input affects not only the state dynamics

ECoLAD: Deployment-Oriented Evaluation for Automotive Time-Series Anomaly Detection

arXiv:2603.10926v1 Announce Type: cross Abstract: Time-series anomaly detectors are commonly compared on workstation-class hardware under unconstrained execution. In-vehicle monitoring, however, requi

Edit Where You Mean: Region-Aware Adapter Injection for Mask-Free Local Image Editing

arXiv:2604.23763v1 Announce Type: new Abstract: Large diffusion transformers (DiTs) follow global editing instructions well but consistently leak local edits into unrelated regions, because joint-atte

Efficient Image Annotation via Semi-Supervised Object Segmentation with Label Propagation

arXiv:2604.22992v1 Announce Type: new Abstract: Reliable object perception is necessary for general-purpose service robots. Open-vocabulary detectors struggle to generalize beyond a few classes and fu

Efficient learning by implicit exploration in bandit problems with side observations

arXiv:2604.24555v1 Announce Type: new Abstract: We consider online learning problems under a partial observability model capturing situations where the information conveyed to the learner is between f

Efficient VQ-QAT and Mixed Vector/Linear quantized Neural Networks

arXiv:2604.23172v1 Announce Type: new Abstract: In this work, we developed and tested 3 techniques for vector quantization (VQ) based model weight compression. To mitigate codebook collapse and enable

Emotion-Conditioned Short-Horizon Human Pose Forecasting with a Lightweight Predictive World Model

arXiv:2604.23532v1 Announce Type: cross Abstract: Short-term human pose prediction plays a crucial role in interactive systems, assistive robots, and emotion-aware human-computer interaction[1-3]. Whi

Energy-Aware Routing to Large Reasoning Models

arXiv:2601.00823v2 Announce Type: replace Abstract: Large reasoning models (LRMs) have heterogeneous inference energy costs based on which model is used and how much it reasons. To reduce energy, it i

Enhanced Privacy and Communication Efficiency in Non-IID Federated Learning with Adaptive Quantization and Differential Privacy

arXiv:2604.23426v1 Announce Type: new Abstract: Federated learning (FL) is a distributed machine learning method where multiple devices collaboratively train a model under the management of a central

Enhancing molecular dynamics with equivariant machine-learned densities

arXiv:2604.24563v1 Announce Type: cross Abstract: Machine-learning interatomic potentials (MLIPs) have enabled molecular dynamics at near ab initio accuracy, yet remain limited to energies and forces

Epicure: Multidimensional Flavor Structure in Food Ingredient Embeddings

arXiv:2604.22776v1 Announce Type: cross Abstract: A chef's intuition about flavor, texture, and cultural identity represents tacit knowledge that is difficult to articulate yet central to culinary pra

Evaluating Remote Sensing Image Captions Beyond Metric Biases

arXiv:2604.22855v1 Announce Type: new Abstract: The core objective of image captioning is to achieve lossless semantic compression from visual signals into textual modalities. However, the reliance on

Evaluation Framework for Highlight Explanations of Context Utilisation in Language Models

arXiv:2510.02629v3 Announce Type: replace Abstract: Context utilisation, the ability of Language Models (LMs) to incorporate relevant information from the provided context when generating responses, r

Evaluation of Prompt Injection Defenses in Large Language Models

arXiv:2604.23887v1 Announce Type: cross Abstract: LLM-powered applications routinely embed secrets in system prompts, yet models can be tricked into revealing them. We built an adaptive attacker that

Model Releases

EXCEEDS: Extracting Complex Events via Nugget-based Grid Modeling in Scientific Domain

model-releasesarxiv-cs-cl

arXiv:2406.14075v2 Announce Type: replace Abstract: It is crucial to understand a specific domain by events. Extensive event extraction research has been conducted in many domains such as news, financ

Explaining Sources of Uncertainty in Automated Fact-Checking

arXiv:2505.17855v2 Announce Type: replace Abstract: Understanding sources of a model's uncertainty regarding its predictions is crucial for effective human-AI collaboration. Prior work proposes using

Explicit integral representations and quantitative bounds for two-layer ReLU networks

arXiv:2604.23260v1 Announce Type: cross Abstract: An approach to construct explicit integral representations for two-layer ReLU networks is presented, which provides relatively simple representations