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ConvFormer3D-TAP: Phase/Uncertainty-Aware Front-End Fusion for Cine CMR View Classification Pipelines

arXiv:2604.11389v1 Announce Type: new Abstract: Reliable recognition of standard cine cardiac MRI views is essential because each view determines which cardiac anatomy is visualized and which quantita

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Cooperation in Human and Machine Agents: Promise Theory Considerations

DGX agent

arXiv:2604.10505v1 Announce Type: new Abstract: Agent based systems are more common than we may think. A Promise Theory perspective on cooperation, in systems of human-machine agents, offers a unified

agentsarxiv-cs-ai

14 Apr 2026

CoPS: Conditional Prompt Synthesis for Zero-Shot Anomaly Detection

arXiv:2508.03447v2 Announce Type: replace Abstract: Recently, large pre-trained vision-language models have shown remarkable performance in zero-shot anomaly detection (ZSAD). With fine-tuning on a si

safetyarxiv-cs-cv

Tutorials

CoRe-ECG: Advancing Self-Supervised Representation Learning for 12-Lead ECG via Contrastive and Reconstructive Synergy

arXiv:2604.11359v1 Announce Type: new Abstract: Accurate interpretation of electrocardiogram (ECG) remains challenging due to the scarcity of labeled data and the high cost of expert annotation. Self-

tutorialsarxiv-cs-ai

COREY: A Prototype Study of Entropy-Guided Operator Fusion with Hadamard Reparameterization for Selective State Space Models

arXiv:2604.10597v1 Announce Type: cross Abstract: State Space Models (SSMs), represented by the Mamba family, provide linear-time sequence modeling and are attractive for long-context inference. Yet p

COSMIK-MPPI: Scaling Constrained Model Predictive Control to Collision Avoidance in Close-Proximity Dynamic Human Environments

arXiv:2604.10358v1 Announce Type: new Abstract: Ensuring safe physical interaction between torque-controlled manipulators and humans is essential for deploying robots in everyday environments. Model P

safetyarxiv-cs-ro

Cost-optimal Sequential Testing via Doubly Robust Q-learning

arXiv:2604.11165v1 Announce Type: cross Abstract: Clinical decision-making often involves selecting tests that are costly, invasive, or time-consuming, motivating individualized, sequential strategies

CoSToM:Causal-oriented Steering for Intrinsic Theory-of-Mind Alignment in Large Language Models

arXiv:2604.10031v1 Announce Type: cross Abstract: Theory of Mind (ToM), the ability to attribute mental states to others, is a hallmark of social intelligence. While large language models (LLMs) demon

CounterBench: Evaluating and Improving Counterfactual Reasoning in Large Language Models

model-releasesarxiv-cs-cl

arXiv:2502.11008v2 Announce Type: replace Abstract: Counterfactual reasoning is widely recognized as one of the most challenging and intricate aspects of causality in artificial intelligence. In this

Counting to Four is still a Chore for VLMs

arXiv:2604.10039v1 Announce Type: new Abstract: Vision--language models (VLMs) have achieved impressive performance on complex multimodal reasoning tasks, yet they still fail on simple grounding skill

CountLoop: Training-Free High-Instance Image Generation via Iterative Agent Guidance

arXiv:2508.16644v4 Announce Type: replace Abstract: Diffusion models excel at photorealistic synthesis but struggle with precise object counts, especially in high-density settings. We introduce COUNTL

agentsarxiv-cs-cv

COXNet: Cross-Layer Fusion with Adaptive Alignment and Scale Integration for RGBT Tiny Object Detection

arXiv:2508.09533v2 Announce Type: replace-cross Abstract: Detecting tiny objects in multimodal Red-Green-Blue-Thermal (RGBT) imagery is a critical challenge in computer vision, particularly in surveil

CPAM: Context-Preserving Adaptive Manipulation for Zero-Shot Real Image Editing

arXiv:2506.18438v2 Announce Type: replace Abstract: Editing natural images using textual descriptions in text-to-image diffusion models remains a significant challenge, particularly in achieving consi

Applications

CraftGraffiti: Exploring Human Identity with Custom Graffiti Art via Facial-Preserving Diffusion Models

arXiv:2508.20640v2 Announce Type: replace Abstract: Preserving facial identity under extreme stylistic transformation remains a major challenge in generative art. In graffiti, a high-contrast, abstrac

applicationsarxiv-cs-cv

Credit-Budgeted ICPC-Style Coding: When Agents Must Pay for Every Decision

arXiv:2604.10182v1 Announce Type: new Abstract: Current evaluations of autonomous coding agents assume an unrealistic, infinite-resource environment. However, real-world software engineering is a reso

CricBench: A Multilingual Benchmark for Evaluating LLMs in Cricket Analytics

model-releasesarxiv-cs-ai

arXiv:2512.21877v3 Announce Type: replace-cross Abstract: Cricket is the second most popular sport worldwide, with billions of fans seeking advanced statistical insights unavailable through standard w

Critical-CoT: A Robust Defense Framework against Reasoning-Level Backdoor Attacks in Large Language Models

arXiv:2604.10681v1 Announce Type: cross Abstract: Large Language Models (LLMs), despite their impressive capabilities across domains, have been shown to be vulnerable to backdoor attacks. Prior backdo

CROP: Conservative Reward for Model-based Offline Policy Optimization

arXiv:2310.17245v2 Announce Type: replace-cross Abstract: Offline reinforcement learning (RL) aims to optimize a policy using collected data without online interactions. Model-based approaches are par

Cross-Cultural Bias in Mel-Scale Representations: Evidence and Alternatives from Speech and Music

arXiv:2604.10503v1 Announce Type: cross Abstract: Modern audio systems universally employ mel-scale representations derived from 1940s Western psychoacoustic studies, potentially encoding cultural bia

Cross-Cultural Value Awareness in Large Vision-Language Models

arXiv:2604.09945v1 Announce Type: cross Abstract: The rapid adoption of large vision-language models (LVLMs) in recent years has been accompanied by growing fairness concerns due to their propensity t

Cross-Validated Cross-Channel Self-Attention and Denoising for Automatic Modulation Classification

model-releasesarxiv-cs-lg

arXiv:2604.10054v1 Announce Type: new Abstract: This study addresses a key limitation in deep learning Automatic Modulation Classification (AMC) models, which perform well at high signal-to-noise rati

CSPO: Alleviating Reward Ambiguity for Structured Table-to-LaTeX Generation

arXiv:2604.10918v1 Announce Type: new Abstract: Tables contain rich structured information, yet when stored as images their contents remain 'locked' within pixels. Converting table images into LaTeX c

CuraLight: Debate-Guided Data Curation for LLM-Centered Traffic Signal Control

arXiv:2604.05663v2 Announce Type: replace Abstract: Traffic signal control (TSC) is a core component of intelligent transportation systems (ITS), aiming to reduce congestion, emissions, and travel tim

Curriculum-based Sample Efficient Reinforcement Learning for Robust Stabilization of a Quadrotor

arXiv:2501.18490v3 Announce Type: replace-cross Abstract: This article introduces a novel sample-efficient curriculum learning (CL) approach for training an end-to-end reinforcement learning (RL) poli

CUTEv2: Unified and Configurable Matrix Extension for Diverse CPU Architectures with Minimal Design Overhead

arXiv:2604.11615v1 Announce Type: cross Abstract: Matrix extensions have emerged as an essential feature in modern CPUs to address the surging demands of AI workloads. However, existing designs often

CWCD: Category-Wise Contrastive Decoding for Structured Medical Report Generation

arXiv:2604.10410v1 Announce Type: new Abstract: Interpreting chest X-rays is inherently challenging due to the overlap between anatomical structures and the subtle presentation of many clinically sign

CylinderDepth: Cylindrical Spatial Attention for Multi-View Consistent Self-Supervised Surround Depth Estimation

arXiv:2511.16428v3 Announce Type: replace Abstract: Self-supervised surround-view depth estimation enables dense, low-cost 3D perception with a 360{eg} field of view from multiple minimally overlappin

DA-PTQ: Drift-Aware Post-Training Quantization for Efficient Vision-Language-Action Models

arXiv:2604.11572v1 Announce Type: new Abstract: Vision-Language-Action models (VLAs) have demonstrated strong potential for embodied AI, yet their deployment on resource-limited robots remains challen

researcharxiv-cs-ro

Daily Predictions of F10.7 and F30 Solar Indices with Deep Learning

arXiv:2604.10045v1 Announce Type: cross Abstract: The F10.7 and F30 solar indices are the solar radio fluxes measured at wavelengths of 10.7 cm and 30 cm, respectively, which are key indicators of sol

researcharxiv-cs-lg

Dark-EvGS: Event Camera as an Eye for Radiance Field in the Dark

arXiv:2507.11931v2 Announce Type: replace Abstract: In low-light environments, conventional cameras often struggle to capture clear multi-view images of objects due to dynamic range limitations and mo

DarwinNet: An Evolutionary Network Architecture for Agent-Driven Protocol Synthesis

arXiv:2604.01236v2 Announce Type: replace-cross Abstract: Traditional network architectures suffer from severe protocol ossification and structural fragility due to their reliance on static, human-def

Local Ai

Data-Driven Automated Identification of Optimal Feature-Representative Images in Infrared Thermography Using Statistical and Morphological Metrics

arXiv:2604.09728v1 Announce Type: new Abstract: Infrared thermography (IRT) is a widely used non-destructive testing technique for detecting structural features such as subsurface defects. However, mo

local-aiarxiv-cs-cv

Data-Efficient Semantic Segmentation of 3D Point Clouds via Open-Vocabulary Image Segmentation-based Pseudo-Labeling

arXiv:2604.11007v1 Announce Type: new Abstract: Semantic segmentation of 3D point cloud scenes is a crucial task for various applications. In real-world scenarios, training segmentation models often f

Data-Efficient Surgical Phase Segmentation in Small-Incision Cataract Surgery: A Controlled Study of Vision Foundation Models

arXiv:2604.10514v1 Announce Type: cross Abstract: Surgical phase segmentation is central to computer-assisted surgery, yet robust models remain difficult to develop when labeled surgical videos are sc

Data Mixing Agent: Learning to Re-weight Domains for Continual Pre-training

arXiv:2507.15640v2 Announce Type: replace-cross Abstract: Continual pre-training on small-scale task-specific data is an effective method for improving large language models in new target fields, yet

DDO-RM for LLM Preference Optimization: A Minimal Held-Out Benchmark against DPO

model-releasesarxiv-cs-lg

arXiv:2604.11119v1 Announce Type: cross Abstract: This paper reorganizes the current manuscript around the DPO versus DDO-RM preference-optimization project and focuses on two parts: the algorithmic v

Dead Cognitions: A Census of Misattributed Insights

model-releasesarxiv-cs-ai

arXiv:2604.10288v1 Announce Type: new Abstract: This essay identifies a failure mode of AI chat systems that we term attribution laundering: the model performs substantive cognitive work and then rhet

DecAlign: Hierarchical Cross-Modal Alignment for Decoupled Multimodal Representation Learning

arXiv:2503.11892v3 Announce Type: replace Abstract: Multimodal representation learning aims to capture both shared and complementary semantic information across multiple modalities. However, the intri

safetyarxiv-cs-cv

DecepGPT: Schema-Driven Deception Detection with Multicultural Datasets and Robust Multimodal Learning

model-releasesarxiv-cs-ai

arXiv:2603.23916v2 Announce Type: replace-cross Abstract: Multimodal deception detection aims to identify deceptive behavior by analyzing audiovisual cues for forensics and security. In these high-sta

Applications

deCIFer: Crystal Structure Prediction from Powder Diffraction Data using Autoregressive Language Models

arXiv:2502.02189v4 Announce Type: replace Abstract: Novel materials drive advancements in fields ranging from energy storage to electronics, with crystal structure characterization forming a crucial y

applicationsarxiv-cs-lg

Decision-Theoretic Safety Assessment of Persona-Driven Multi-Agent Systems in O-RAN

arXiv:2604.09682v1 Announce Type: cross Abstract: Autonomous network management in Open Radio Access Networks requires intelligent decision making across conflicting objectives, yet existing LLM based

Decoding Ancient Oracle Bone Script via Generative Dictionary Retrieval

arXiv:2604.09668v1 Announce Type: cross Abstract: Understanding humanity's earliest writing systems is crucial for reconstructing civilization's origins, yet many ancient scripts remain undeciphered.

Decompose, Mix, Adapt: A Unified Framework for Parameter-Efficient Neural Network Recombination and Compression

arXiv:2603.27383v2 Announce Type: replace Abstract: Parameter Recombination (PR) methods aim to efficiently compose the weights of a neural network for applications like Parameter-Efficient FineTuning

Decomposing and Reducing Hidden Measurement Error in LLM Evaluation Pipelines

model-releasesarxiv-cs-cl

arXiv:2604.11581v1 Announce Type: new Abstract: LLM evaluations drive which models get deployed, which safety standards get adopted, and which research conclusions get published. Yet these scores carr

Applications

Deconstructing Subset Construction -- Reducing While Determinizing

arXiv:2505.10319v2 Announce Type: replace-cross Abstract: We present a novel perspective on the NFA canonization problem, which introduces intermediate minimization steps to reduce the exploration spa

applicationsarxiv-cs-lg

Decoupled Generative Modeling for Human-Object Interaction Synthesis

arXiv:2512.19049v2 Announce Type: replace Abstract: Synthesizing realistic human-object interaction (HOI) is essential for 3D computer vision and robotics, underpinning animation and embodied control.

Decoupled Similarity for Task-Aware Token Pruning in Large Vision-Language Models

arXiv:2604.11240v1 Announce Type: new Abstract: Token pruning has emerged as an effective approach to reduce the substantial computational overhead of Large Vision-Language Models (LVLMs) by discardin

DeCoVec: Building Decoding Space based Task Vector for Large Language Models via In-Context Learning

arXiv:2604.11129v1 Announce Type: new Abstract: Task vectors, representing directions in model or activation spaces that encode task-specific behaviors, have emerged as a promising tool for steering l

researcharxiv-cs-cl