Search: “research”

Best-of-Both-Worlds Multi-Dueling Bandits: Unified Algorithms for Stochastic and Adversarial Preferences under Condorcet and Borda Objectives

arXiv:2603.18972v3 Announce Type: replace Abstract: Multi-dueling bandits, where a learner selects m geq 2 arms per round and observes only the winner, arise naturally in many applications including r

Research

Better Together: Evaluating the Complementarity of Earth Embedding Models

DGX agent

arXiv:2605.18667v1 Announce Type: new Abstract: Earth embedding models transform Earth observation data into embeddings uniquely tied to locations on the Earth's surface. These models are typically ev

researcharxiv-cs-cv

19 May 2026

Beyond Accuracy: Robustness, Interpretability and Expressiveness of EEG Foundation Models

arXiv:2605.17562v1 Announce Type: cross Abstract: EEG foundation models (EEG-FMs) have been evaluated predominantly on clean, in-distribution accuracy, leaving their robustness, interpretability and r

Beyond Correctness: Harmonizing Process and Outcome Rewards through RL Training

arXiv:2509.03403v2 Announce Type: replace-cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) improves final-answer accuracy on reasoning tasks, but it does not reliably improve reas

Beyond Euclidean Prototypes: Spectral Disentanglement and Geodesic Matching for Few-Shot Medical Image Segmentation

arXiv:2605.17904v1 Announce Type: new Abstract: Few-Shot Medical Image Segmentation (FSMIS) aims to delineate novel anatomical targets from one or a few annotated support images, addressing the annota

Beyond Execution: Static-Analysis Rewards and Hint-Conditioned Diffusion RL for Code Generation

arXiv:2605.17174v1 Announce Type: cross Abstract: Reinforcement Learning (RL) is an important paradigm for aligning Diffusion Language Models (DLMs) toward functional correctness in code generation. H

Beyond Linear Superposition: Discovering Climate Features in AI Weather Models with KAN-SAE

arXiv:2605.17493v1 Announce Type: cross Abstract: Deep learning weather prediction models achieve remarkable predictive skill yet remain largely opaque: we know little about how they represent physica

Beyond MMSE: Enhancing PnP Restoration with ProxiMAP

arXiv:2605.16396v1 Announce Type: new Abstract: Plug-and-Play (PnP) methods have become standard tools for solving imaging inverse problems by replacing the intractable maximum a posteriori (MAP) deno

Beyond Morphology: Quantifying the Diagnostic Power of Color Features in Cancer Classification

arXiv:2605.18522v1 Announce Type: cross Abstract: In histopathology, human experts primarily rely on color as a means of enhancing contrast to interpret tissue morphology, whereas machine vision model

Beyond Patches: Global-aware Autoregressive Model for Multimodal Few-Shot Font Generation

arXiv:2601.01593v2 Announce Type: replace Abstract: Manual font design is an intricate process that transforms a stylistic visual concept into a coherent glyph set. This challenge persists in automate

Beyond Square Roots: Explicit Memory-Efficient Factorization for Multi-Epoch Private Learning

arXiv:2605.18379v1 Announce Type: new Abstract: Correlated-noise mechanisms are among the most promising approaches for improving the utility of differentially private model training, but rigorous gua

Bi-Level Chaotic Fusion Based Graph Convolutional Network for Stock Market Prediction Interval

arXiv:2605.16324v1 Announce Type: new Abstract: Financial market forecasting is inherently uncertain, yet most deep learning approaches rely on point predictions that provide only single-value estimat

Bidirectional Optical sensors for Actuation Tracking (BOAT) in soft lattice systems

arXiv:2605.18482v1 Announce Type: new Abstract: The growing adoption of lattice-based structures in soft robotics creates a need for advanced sensing solutions capable of monitoring their global defor

researcharxiv-cs-ro

BioArtlas: Computational Clustering of Multi-Dimensional Complexity in Bioart

arXiv:2511.19162v2 Announce Type: replace-cross Abstract: Bioart's hybrid nature spanning art, science, technology, ethics, and politics defies traditional single-axis categorization. I present BioArt

Brain Vascular Age Prediction Using Cerebral Blood Flow Velocity and Machine Learning Algorithms

arXiv:2605.16969v1 Announce Type: new Abstract: Defining vascular age in terms of physiological function has become one focal point of the extensive studies to categorize and track chronological age.

Breaking the accuracy-resource dilemma: a lightweight adaptive video inference enhancement

arXiv:2601.14568v2 Announce Type: replace-cross Abstract: Existing video inference (VI) enhancement methods typically aim to improve performance by scaling up model sizes and employing sophisticated n

Bridging the Modality Bottleneck in Pathology MIL through Virtual Molecular Staining

arXiv:2605.16392v1 Announce Type: cross Abstract: Multiple instance learning (MIL) is the dominant framework for whole-slide image analysis in computational pathology, typically combining a frozen pat

Bridging the Version Gap: Multi-version Training Improves ICD Code Prediction, Especially for Rare Codes

arXiv:2605.17755v1 Announce Type: cross Abstract: Clinical coding maps clinical documentation to standardized medical codes, an essential yet time-consuming administrative task that could benefit from

Bug or Feature^2: Weight Drift, Activation Sparsity, and Spikes

arXiv:2605.17659v1 Announce Type: new Abstract: The design of modern neural architectures has converged through incremental empirical choices, yet the mechanisms governing their training dynamics rema

Byzantine-Resilient Federated Learning via QUBO-Based Client Selection on Quantum Annealers

arXiv:2605.16438v1 Announce Type: cross Abstract: Federated Learning (FL) trains a global model across decentralized clients while preserving data privacy, but at scale it is vulnerable to malicious u

CAB: Accelerating Flow and Diffusion Sampling via Rectification and Corrected Adams-Bashforth

arXiv:2605.16736v1 Announce Type: new Abstract: Flow and diffusion models achieve high-fidelity, high-resolution image synthesis, but often require many function evaluations (NFEs) at sampling time. E

CADS: Conformal Adaptive Decision System for Cost-Efficient Image Classification

arXiv:2605.16401v1 Announce Type: new Abstract: While high-capacity AI models have advanced state-of-the-art performance, their practical deployment is often hindered by high inference costs, environm

Calibeating for general proper losses: A Bregman divergence approach

arXiv:2605.17269v1 Announce Type: new Abstract: This work introduces a general framework for calibeating based on regret minimization. As compared to Foster and Hart's seminal calibeating work which h

CAM-VFD: Cross-Attention Multimodal Video Forgery Detection

arXiv:2605.17133v1 Announce Type: cross Abstract: The rapid advancement of Deepfake technologies and video manipulation tools poses a critical challenge to multimedia forensics, judicial evidence inte

Can LLMs Refuse Questions They Do Not Know? Measuring Knowledge-Aware Refusal in Factual Tasks

arXiv:2510.01782v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) should refuse to answer questions beyond their knowledge. This capability, which we term knowledge-aware refusal,

Can machine learning for quantum-gas experiments be explainable?

arXiv:2605.18689v1 Announce Type: cross Abstract: Virtually all aspects of many-body atomic physics are challenging: experiments are technically demanding, datasets have become enormous, and the memor

Capturing LLM Capabilities via Evidence-Calibrated Query Clustering

arXiv:2605.17110v1 Announce Type: new Abstract: Query clustering organizes queries into groups that reflect shared latent capability demands, enabling capability-aware LLM evaluation. Existing cluster

CAR-SAM: Cross-Attention Reconstruction for Post-Training Quantization of the Segment Anything Model

arXiv:2605.16901v1 Announce Type: new Abstract: Segment Anything Models (SAMs) are extensively used in computer vision for universal image segmentation, but deploying them on resource-constrained devi

CAST: Causal Anchored Simplex Transport for Distribution-Valued Time Series

arXiv:2605.16919v1 Announce Type: cross Abstract: Many decision-facing stochastic systems are observed through aggregate distributions rather than scalar trajectories: queue occupancies, mobility shar

CATA: Continual Machine Unlearning via Conflict-Averse Task Arithmetic

arXiv:2605.18610v1 Announce Type: cross Abstract: Vision-language models (VLMs) have shown remarkable ability in aligning visual and textual representations, enabling a wide range of multimodal applic

Catastrophic Overfitting, Entropy Gap and Participation Ratio: A Noiseless l^p Norm Solution for Fast Adversarial Training

arXiv:2505.02360v2 Announce Type: replace-cross Abstract: Adversarial training is a cornerstone of robust deep learning, but fast methods like the Fast Gradient Sign Method (FGSM) often suffer from Ca

CATRF: Codec-Adaptive TriPlane Radiance Fields for Volumetric Content Delivery

arXiv:2605.18054v1 Announce Type: cross Abstract: Volumetric media promises next-generation content delivery applications, but its bandwidth demand remains a key bottleneck. Implicit and hybrid volume

ChronoSC: Task-Oriented Semantic Communication via Temporal-to-Color Encoding

arXiv:2605.16388v1 Announce Type: new Abstract: Semantic communication (SC) aims to reduce transmission overhead by conveying task-relevant information rather than raw data. However, existing SC appro

CLEAR-HPV: Interpretable Concept Discovery for HPV-Associated Morphology in Whole-Slide Histologyhttps://arxiv.org/submit/7596892/preview

arXiv:2602.05126v2 Announce Type: replace Abstract: Human papillomavirus (HPV) status is a critical determinant of prognosis and treatment response in head and neck and cervical cancers. Although atte

Clipped Gradient Methods for Nonsmooth Convex Optimization under Heavy-Tailed Noise: A Refined Analysis

arXiv:2512.23178v3 Announce Type: replace-cross Abstract: Optimization under heavy-tailed noise has become popular recently, since it better fits many modern machine learning tasks, as captured by emp

Coarse Semantic Injection for LLM-Conditioned Structured Indoor Prediction

arXiv:2605.16832v1 Announce Type: new Abstract: Large language models (LLMs) have recently been used as structured decoders for indoor understanding from 3D point-token inputs. However, point cloud en

CodeScaler: Scaling Code LLM Training and Test-Time Inference via Reward Models

arXiv:2602.17684v2 Announce Type: replace-cross Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) has driven recent progress in code large language models by leveraging execution-based f

CogBlender: Towards Continuous Cognitive Intervention in Text-to-Image Generation

arXiv:2603.09286v2 Announce Type: replace Abstract: Beyond conveying semantic information, images also possess cognitive properties that elicit specific psychological responses from viewers, such as m

Color as the Impetus: Transforming Few-Shot Learner

arXiv:2507.22136v3 Announce Type: replace Abstract: Humans possess innate meta-learning capabilities, partly attributable to their exceptional color perception. In this paper, we pioneer an innovative

Colossal Biosciences is growing chickens in a 3D-printed artificial eggshell

The baby chicks were shifting and starting to pip—or trying to hatch. But not from an egg. Instead, these chickens were growing inside transparent 3D-printed plastic cups at the Dallas headquarters of

researchmit-tech-review

Compositional Adversarial Training for Robust Visual Watermarking

arXiv:2605.16720v1 Announce Type: new Abstract: Robust watermarking is typically trained with random post-processing augmentation, but random sampling under-covers the combinatorial space of realistic

Concepts Worth Having: Refining VLM-Guided Concept Bottleneck Models with Minimal Annotations

arXiv:2605.16405v1 Announce Type: new Abstract: Concept-bottleneck models (CBMs) are neural classifiers that compute predictions from high-level concepts extracted from the input. CBMs ensure stakehol

Concise and Logically Consistent Conformal Sets for Neuro-Symbolic Concept-Based Models

arXiv:2605.18202v1 Announce Type: cross Abstract: Neuro-Symbolic Concept-based Models (NeSy-CBMs) are a family of architectures that integrate neural networks with symbolic reasoning for enhanced reli

Confidence Geometry Reveals Trace-Level Correctness in Large Language Model Reasoning

arXiv:2605.16824v1 Announce Type: cross Abstract: Large language models (LLMs) generate not only reasoning text, but also token-level confidence trajectories that record how uncertainty evolves during

researcharxiv-cs-cl

ConflictRAG: Detecting and Resolving Knowledge Conflicts in Retrieval Augmented Generation

arXiv:2605.17301v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) systems implicitly assume mutual consistency among retrieved documents -- an assumption that frequently fails in

Content-Style Identification via Differential Independence

arXiv:2605.17827v1 Announce Type: cross Abstract: Generative analysis often models multi-domain observations as nonlinear mixtures of domain-invariant content variables and domain-specific style varia

CosFly-Track: A Large-Scale Multi-Modal Dataset for UAV Visual Tracking via Multi-Constraint Trajectory Optimization

arXiv:2605.17776v1 Announce Type: new Abstract: Recent aerial vision-language navigation (VLN) datasets have grown rapidly, but they primarily address goal-oriented navigation to static destinations,

researcharxiv-cs-ro

CoUn: Empowering Machine Unlearning via Contrastive Learning

arXiv:2509.16391v3 Announce Type: replace-cross Abstract: Machine unlearning (MU) aims to remove the influence of specific 'forget' data from a trained model while preserving its knowledge of the rema