All entries

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

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

Research

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

DGX agent

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

researcharxiv-cs-ai

10 Apr 2026

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

Applications

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

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

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

researcharxiv-cs-lg

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

researcharxiv-cs-lg

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

researcharxiv-cs-cl

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

researcharxiv-cs-cl

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

researcharxiv-cs-lg

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

Applications

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

researcharxiv-cs-cl

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

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

Local Ai

Data Selection for Multi-turn Dialogue Instruction Tuning

arXiv:2604.07892v1 Announce Type: new Abstract: Instruction-tuned language models increasingly rely on large multi-turn dialogue corpora, but these datasets are often noisy and structurally inconsiste

local-aiarxiv-cs-cl

Database Querying under Missing Values Governed by Missingness Mechanisms

arXiv:2604.06520v1 Announce Type: cross Abstract: We address the problems of giving a semantics to- and doing query answering (QA) on a relational database (RDB) that has missing values (MVs). The cau

Applications

DBMF: A Dual-Branch Multimodal Framework for Out-of-Distribution Detection

arXiv:2604.08261v1 Announce Type: new Abstract: The complex and dynamic real-world clinical environment demands reliable deep learning (DL) systems. Out-of-distribution (OOD) detection plays a critica

applicationsarxiv-cs-cv

DDP-SA: Scalable Privacy-Preserving Federated Learning via Distributed Differential Privacy and Secure Aggregation

model-releasesarxiv-cs-lg

arXiv:2604.07125v1 Announce Type: cross Abstract: This article presents DDP-SA, a scalable privacy-preserving federated learning framework that jointly leverages client-side local differential privacy

DeCo: Frequency-Decoupled Pixel Diffusion for End-to-End Image Generation

arXiv:2511.19365v2 Announce Type: replace-cross Abstract: Pixel diffusion aims to generate images directly in pixel space in an end-to-end fashion. This approach avoids the limitations of VAE in the t

Decompose, Look, and Reason: Reinforced Latent Reasoning for VLMs

arXiv:2604.07518v1 Announce Type: new Abstract: Vision-Language Models often struggle with complex visual reasoning due to the visual information loss in textual CoT. Existing methods either add the c

safetyarxiv-cs-cl

Deep Learning-Powered Visual SLAM Aimed at Assisting Visually Impaired Navigation

arXiv:2510.20549v2 Announce Type: replace Abstract: Despite advancements in SLAM technologies, robust operation under challenging conditions such as low-texture, motion-blur, or challenging lighting r

safetyarxiv-cs-cv

Demystifying OPD: Length Inflation and Stabilization Strategies for Large Language Models

arXiv:2604.08527v1 Announce Type: new Abstract: On-policy distillation (OPD) trains student models under their own induced distribution while leveraging supervision from stronger teachers. We identify

safetyarxiv-cs-cl

Agents

Density-Driven Optimal Control: Convergence Guarantees for Stochastic LTI Multi-Agent Systems

arXiv:2604.08495v1 Announce Type: cross Abstract: This paper addresses the decentralized non-uniform area coverage problem for multi-agent systems, a critical task in missions with high spatial priori

agentsarxiv-cs-ro

Depression Detection at the Point of Care: Automated Analysis of Linguistic Signals from Routine Primary Care Encounters

arXiv:2604.06193v1 Announce Type: cross Abstract: Depression is underdiagnosed in primary care, yet timely identification remains critical. Recorded clinical encounters, increasingly common with digit

Designing Safe and Accountable GenAI as a Learning Companion with Women Banned from Formal Education

arXiv:2604.07253v1 Announce Type: cross Abstract: In gender-restrictive and surveilled contexts, where access to formal education may be restricted for women, pursuing education involves safety and pr

safetyarxiv-cs-ai

Detecting HIV-Related Stigma in Clinical Narratives Using Large Language Models

arXiv:2604.07717v1 Announce Type: new Abstract: Human immunodeficiency virus (HIV)-related stigma is a critical psychosocial determinant of health for people living with HIV (PLWH), influencing mental

Development of ML model for triboelectric nanogenerator based sign language detection system

arXiv:2604.06220v1 Announce Type: cross Abstract: Sign language recognition (SLR) is vital for bridging communication gaps between deaf and hearing communities. Vision-based approaches suffer from occ

DHFP-PE: Dual-Precision Hybrid Floating Point Processing Element for AI Acceleration

arXiv:2604.04507v2 Announce Type: replace-cross Abstract: The rapid adoption of low-precision arithmetic in artificial intelligence and edge computing has created a strong demand for energy-efficient

researcharxiv-cs-ro

Diagnosing and Mitigating Sycophancy and Skepticism in LLM Causal Judgment

arXiv:2601.08258v3 Announce Type: replace Abstract: Large language models increasingly fail in a way that scalar accuracy cannot diagnose: they produce a sound reasoning trace and then abandon it unde

DietDelta: A Vision-Language Approach for Dietary Assessment via Before-and-After Images

arXiv:2604.06352v1 Announce Type: cross Abstract: Accurate dietary assessment is critical for precision nutrition, yet most image-based methods rely on a single pre-consumption image and provide only