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Clickbait detection: quick inference with maximum impact

arXiv:2604.08148v1 Announce Type: new Abstract: We propose a lightweight hybrid approach to clickbait detection that combines OpenAI semantic embeddings with six compact heuristic features capturing s

Local Ai

Clinical Cognition Alignment for Gastrointestinal Diagnosis with Multimodal LLMs

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DGX agent

arXiv:2603.20698v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) have demonstrated remarkable potential in medical image analysis. However, their application in gastr

local-aiarxiv-cs-cl

10 Apr 2026

CMP: Robust Whole-Body Tracking for Loco-Manipulation via Competence Manifold Projection

arXiv:2604.07457v1 Announce Type: new Abstract: While decoupled control schemes for legged mobile manipulators have shown robustness, learning holistic whole-body control policies for tracking global

safetyarxiv-cs-ro

CNN-based Surface Temperature Forecasts with Ensemble Numerical Weather Prediction

arXiv:2507.18937v3 Announce Type: replace-cross Abstract: Due to limited computational resources, medium-range temperature forecasts typically rely on low-resolution numerical weather prediction (NWP)

safetyarxiv-cs-ai

Code Sharing In Prediction Model Research: A Scoping Review

arXiv:2604.06212v1 Announce Type: cross Abstract: Analytical code is essential for reproducing diagnostic and prognostic prediction model research, yet code availability in the published literature re

Hardware

CodecSight: Leveraging Video Codec Signals for Efficient Streaming VLM Inference

arXiv:2604.06036v3 Announce Type: replace-cross Abstract: Video streaming analytics is a crucial workload for vision-language model serving, but the high cost of multimodal inference limits scalabilit

hardwarearxiv-cs-cv

Cognitive Mismatch in Multimodal Large Language Models for Discrete Symbol Understanding

model-releasesarxiv-cs-cv

arXiv:2603.18472v2 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) perform strongly on natural images, yet their ability to understand discrete visual symbols remains u

Commander-GPT: Dividing and Routing for Multimodal Sarcasm Detection

arXiv:2506.19420v2 Announce Type: replace Abstract: Multimodal sarcasm understanding is a high-order cognitive task. Although large language models (LLMs) have shown impressive performance on many dow

Compact Example-Based Explanations for Language Models

arXiv:2601.03786v2 Announce Type: replace Abstract: Training data influence estimation methods quantify the contribution of training documents to a model's output, making them a promising source of in

Complementary Filtering on SO(3) for Attitude Estimation with Scalar Measurements

arXiv:2604.08099v1 Announce Type: cross Abstract: Attitude estimation using scalar measurements, corresponding to partial vectorial observations, arises naturally when inertial vectors are not fully o

researcharxiv-cs-ro

CompoDistill: Attention Distillation for Compositional Reasoning in Multimodal LLMs

arXiv:2510.12184v2 Announce Type: replace Abstract: Recently, efficient Multimodal Large Language Models (MLLMs) have gained significant attention as a solution to their high computational complexity,

applicationsarxiv-cs-cv

Component-Adaptive and Lesion-Level Supervision for Improved Small Structure Segmentation in Brain MRI

arXiv:2604.08015v1 Announce Type: new Abstract: We propose a unified objective function, termed CATMIL, that augments the base segmentation loss with two auxiliary supervision terms operating at diffe

researcharxiv-cs-cv

Compressible Softmax-Attended Language under Incompressible Attention

arXiv:2604.04384v2 Announce Type: replace-cross Abstract: Softmax attention defines an interaction through d_h head dimensions, but not all dimensions carry equal weight once real text passes throug

Computational bottlenecks for denoising diffusions

arXiv:2503.08028v3 Announce Type: replace-cross Abstract: Denoising diffusions sample from a probability distribution mu in R^d by constructing a stochastic process $({hat{oldsymbol x

Agents

Computer Environments Elicit General Agentic Intelligence in LLMs

arXiv:2601.16206v3 Announce Type: replace-cross Abstract: Agentic intelligence in large language models (LLMs) requires not only model intrinsic capabilities but also interactions with external enviro

agentsarxiv-cs-ai

Concave Certificates: Geometric Framework for Distributionally Robust Risk and Complexity Analysis

arXiv:2601.01311v2 Announce Type: replace-cross Abstract: Distributionally Robust (DR) optimization aims to certify worst-case risk within a Wasserstein uncertainty set. Current certifications typical

applicationsarxiv-cs-lg

Local Ai

Concentrated siting of AI data centers drives regional power-system stress under rising global compute demand

arXiv:2604.06198v1 Announce Type: cross Abstract: The rapid rise of generative artificial intelligence (AI) is driving unprecedented growth in global computational demand, placing increasing pressure

local-aiarxiv-cs-ai

ConceptTracer: Interactive Analysis of Concept Saliency and Selectivity in Neural Representations

arXiv:2604.07019v1 Announce Type: cross Abstract: Neural networks deliver impressive predictive performance across a variety of tasks, but they are often opaque in their decision-making processes. Des

Tutorials

Conditional flow matching for physics-constrained inverse problems with finite training data

arXiv:2603.14135v3 Announce Type: replace-cross Abstract: This study presents a conditional flow matching framework for solving physics-constrained Bayesian inverse problems. In this setting, samples

tutorialsarxiv-cs-lg

Conformal Margin Risk Minimization: An Envelope Framework for Robust Learning under Label Noise

arXiv:2604.06468v1 Announce Type: new Abstract: Most methods for learning with noisy labels require privileged knowledge such as noise transition matrices, clean subsets or pretrained feature extracto

applicationsarxiv-cs-lg

Local Ai

ConfusionPrompt: Practical Private Inference for Online Large Language Models

arXiv:2401.00870v5 Announce Type: replace-cross Abstract: State-of-the-art large language models (LLMs) are typically deployed as online services, requiring users to transmit detailed prompts to cloud

local-aiarxiv-cs-ai

Consistency-Guided Decoding with Proof-Driven Disambiguation for Three-Way Logical Question Answering

arXiv:2604.06196v1 Announce Type: cross Abstract: Three-way logical question answering (QA) assigns True/False/Unknown to a hypothesis H given a premise set S. While modern large language models

ConsistRM: Improving Generative Reward Models via Consistency-Aware Self-Training

model-releasesarxiv-cs-cl

arXiv:2604.07484v1 Announce Type: cross Abstract: Generative reward models (GRMs) have emerged as a promising approach for aligning Large Language Models (LLMs) with human preferences by offering grea

Contextual Earnings-22: A Speech Recognition Benchmark with Custom Vocabulary in the Wild

model-releasesarxiv-cs-cl

arXiv:2604.07354v1 Announce Type: new Abstract: The accuracy frontier of speech-to-text systems has plateaued on academic benchmarks.1 In contrast, industrial benchmarks and adoption in high-stakes do

Contextualising (Im)plausible Events Triggers Figurative Language

arXiv:2604.07885v1 Announce Type: new Abstract: This work explores the connection between (non-)literalness and plausibility at the example of subject-verb-object events in English. We design a system

safetyarxiv-cs-cl

Continual Visual Anomaly Detection on the Edge: Benchmark and Efficient Solutions

arXiv:2604.06435v1 Announce Type: cross Abstract: Visual Anomaly Detection (VAD) is a critical task for many applications including industrial inspection and healthcare. While VAD has been extensively

Agents

Continuous-Time Dynamics of the Difference-of-Convex Algorithm

arXiv:2604.06926v1 Announce Type: cross Abstract: We study the continuous-time structure of the difference-of-convex algorithm (DCA) for smooth DC decompositions with a strongly convex component. In d

agentsarxiv-cs-lg

Contraction-Aligned Analysis of Soft Bellman Residual Minimization with Weighted Lp-Norm for Markov Decision Problem

arXiv:2604.06837v1 Announce Type: new Abstract: The problem of solving Markov decision processes under function approximation remains a fundamental challenge, even under linear function approximation

Contrastive Decoding Mitigates Score Range Bias in LLM-as-a-Judge

arXiv:2510.18196v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are commonly used as evaluators in various applications, but the reliability of the outcomes remains a challenge.

safetyarxiv-cs-ai

Controller Design for Structured State-space Models via Contraction Theory

arXiv:2604.07069v1 Announce Type: cross Abstract: This paper presents an indirect data-driven output feedback controller synthesis for nonlinear systems, leveraging Structured State-space Models (SSMs

ConvoLearn: A Dataset for Fine-Tuning Dialogic AI Tutors

arXiv:2601.08950v3 Announce Type: replace Abstract: Despite their growing adoption in education, LLMs remain misaligned with the core principle of effective tutoring: the dialogic construction of know

Tutorials

Coordinate-Based Dual-Constrained Autoregressive Motion Generation

arXiv:2604.08088v1 Announce Type: new Abstract: Text-to-motion generation has attracted increasing attention in the research community recently, with potential applications in animation, virtual reali

tutorialsarxiv-cs-cv

Cost-Efficient Multi-Scale Fovea for Semantic-Based Visual Search Attention

arXiv:2604.03836v2 Announce Type: replace-cross Abstract: Semantics are one of the primary sources of top-down preattentive information. Modern deep object detectors excel at extracting such valuable

researcharxiv-cs-cv

Countering the Over-Reliance Trap: Mitigating Object Hallucination for LVLMs via a Self-Validation Framework

arXiv:2601.22451v2 Announce Type: replace-cross Abstract: Despite progress in Large Vision Language Models (LVLMs), object hallucination remains a critical issue in image captioning task, where models

Cram Less to Fit More: Training Data Pruning Improves Memorization of Facts

arXiv:2604.08519v1 Announce Type: new Abstract: Large language models (LLMs) can struggle to memorize factual knowledge in their parameters, often leading to hallucinations and poor performance on kno

CrashSight: A Phase-Aware, Infrastructure-Centric Video Benchmark for Traffic Crash Scene Understanding and Reasoning

model-releasesarxiv-cs-cv

arXiv:2604.08457v1 Announce Type: new Abstract: Cooperative autonomous driving requires traffic scene understanding from both vehicle and infrastructure perspectives. While vision-language models (VLM

Cross-Lingual Transfer and Parameter-Efficient Adaptation in the Turkic Language Family: A Theoretical Framework for Low-Resource Language Models

arXiv:2604.06202v1 Announce Type: cross Abstract: Large language models (LLMs) have transformed natural language processing, yet their capabilities remain uneven across languages. Most multilingual mo

Tutorials

Cross-Modal Emotion Transfer for Emotion Editing in Talking Face Video

arXiv:2604.07786v1 Announce Type: new Abstract: Talking face generation has gained significant attention as a core application of generative models. To enhance the expressiveness and realism of synthe

tutorialsarxiv-cs-cv

Cross-Tokenizer LLM Distillation through a Byte-Level Interface

arXiv:2604.07466v1 Announce Type: new Abstract: Cross-tokenizer distillation (CTD), the transfer of knowledge from a teacher to a student language model when the two use different tokenizers, remains

CRPS-Optimal Binning for Univariate Conformal Regression

arXiv:2603.22000v2 Announce Type: replace Abstract: We propose a method for non-parametric conditional distribution estimation based on partitioning covariate-sorted observations into contiguous bins

CryoSplat: Gaussian Splatting for Cryo-EM Homogeneous Reconstruction

model-releasesarxiv-cs-cv

arXiv:2508.04929v4 Announce Type: replace-cross Abstract: As a critical modality for structural biology, cryogenic electron microscopy (cryo-EM) facilitates the determination of macromolecular structu

CSA-Graphs: A Privacy-Preserving Structural Dataset for Child Sexual Abuse Research

arXiv:2604.07132v1 Announce Type: cross Abstract: Child Sexual Abuse Imagery (CSAI) classification is an important yet challenging problem for computer vision research due to the strict legal and ethi

safetyarxiv-cs-ai

CubeGraph: Efficient Retrieval-Augmented Generation for Spatial and Temporal Data

arXiv:2604.06616v1 Announce Type: cross Abstract: Hybrid queries combining high-dimensional vector similarity search with spatio-temporal filters are increasingly critical for modern retrieval-augment

applicationsarxiv-cs-ai

Current LLMs still cannot 'talk much' about grammar modules: Evidence from syntax

arXiv:2603.20114v4 Announce Type: replace Abstract: We aim to examine the extent to which Large Language Models (LLMs) can 'talk much' about grammar modules, providing evidence from syntax core proper

CycleChart: A Unified Consistency-Based Learning Framework for Bidirectional Chart Understanding and Generation

model-releasesarxiv-cs-cl

arXiv:2512.19173v2 Announce Type: replace Abstract: Current chart-related tasks, such as chart generation (NL2Chart), chart schema parsing, chart data parsing, and chart question answering (ChartQA),

Daily and Weekly Periodicity in Large Language Model Performance and Its Implications for Research

arXiv:2602.15889v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used in research as both tools and objects of study. Much of this work assumes that LLM performa

DailyArt: Discovering Articulation from Single Static Images via Latent Dynamics

arXiv:2604.07758v1 Announce Type: new Abstract: Articulated objects are essential for embodied AI and world models, yet inferring their kinematics from a single closed-state image remains challenging

researcharxiv-cs-cv

Data Leakage in Automotive Perception: Practitioners' Insights

arXiv:2604.06899v1 Announce Type: cross Abstract: Data leakage is the inadvertent transfer of information between training and evaluation datasets that poses a subtle, yet critical, risk to the reliab

safetyarxiv-cs-lg