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Bridging Brain and Semantics: A Hierarchical Framework for Semantically Enhanced fMRI-to-Video Reconstruction

arXiv:2605.14569v1 Announce Type: new Abstract: Reconstructing dynamic visual experiences as videos from functional magnetic resonance imaging (fMRI) is pivotal for advancing the understanding of neur

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Human DGX agent

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Bridging Legal Interpretation and Formal Logic: Faithfulness, Assumption, and the Future of AI Legal Reasoning

DGX agent

arXiv:2605.14049v1 Announce Type: new Abstract: The growing adoption of large language models in legal practice brings both significant promise and serious risk. Legal professionals stand to benefit f

applicationsarxiv-cs-ai

15 May 2026

Local Ai

Bridging the Rural Healthcare Gap: A Cascaded Edge-Cloud Architecture for Automated Retinal Screening

arXiv:2605.14108v1 Announce Type: cross Abstract: Diabetic Retinopathy (DR) is one of the leading causes of preventable blindness, yet rural regions often lack the specialists and infrastructure neede

local-aiarxiv-cs-ai

CA2: Code-Aware Agent for Automated Game Testing

arXiv:2605.13918v1 Announce Type: cross Abstract: Automated game testing is important for verifying game functionality, but it remains a costly and time-consuming process. Manual testing often misses

agentsarxiv-cs-lg

CAKE: Confidence in Assignments via K-partition Ensembles

arXiv:2602.18435v2 Announce Type: replace Abstract: Clustering is widely used for unsupervised structure discovery, yet it offers limited insight into how reliable each individual assignment is. Diagn

applicationsarxiv-cs-lg

CalibAnyView: Beyond Single-View Camera Calibration in the Wild

arXiv:2605.14615v1 Announce Type: new Abstract: Camera calibration is a fundamental prerequisite for reliable geometric perception, yet classical approaches rely on controlled acquisition setups that

applicationsarxiv-cs-cv

CaMeRL: Collision-Aware and Memory-Enhanced Reinforcement Learning for UAV Navigation in Multi-Scale Obstacle Environments

arXiv:2605.14810v1 Announce Type: new Abstract: In obstacle avoidance navigation of unmanned aerial vehicles (UAVs), variations in obstacle scale have received strangely less attention than obstacle n

researcharxiv-cs-ro

Tutorials

Can Stationary Distributions of Scale-Invariant Neural Networks Be Described by the Thermodynamics of an Ideal Gas?

arXiv:2511.07308v2 Announce Type: replace Abstract: Understanding the training dynamics of deep neural networks remains a major open problem, with physics-inspired approaches offering promising insigh

tutorialsarxiv-cs-lg

Can Visual Mamba Improve AI-Generated Image Detection? An In-Depth Investigation

model-releasesarxiv-cs-cv

arXiv:2605.14799v1 Announce Type: new Abstract: In recent years, computer vision has witnessed remarkable progress, fueled by the development of innovative architectures such as Convolutional Neural N

Capacitive Touchscreens at Risk: Recovering Handwritten Trajectory on Smartphone via Electromagnetic Emanations

arXiv:2512.11484v1 Announce Type: cross Abstract: This paper reveals and exploits a critical security vulnerability: the electromagnetic (EM) side channel of capacitive touchscreens leaks sufficient i

researcharxiv-cs-ai

Case-Based Calibration of Adaptive Reasoning and Execution for LLM Tool Use

arXiv:2605.15041v1 Announce Type: new Abstract: Tool use extends large language models beyond parametric knowledge, but reliable execution requires balancing appropriate reasoning depth with strict st

Cattle Trade: A Multi-Agent Benchmark for LLM Bluffing, Bidding, and Bargaining

arXiv:2605.14537v1 Announce Type: new Abstract: We introduce extsc{Cattle Trade, a multi-agent benchmark for evaluating large language models (LLMs) as agents in strategic reasoning under imperfect in

Causal Forcing++: Scalable Few-Step Autoregressive Diffusion Distillation for Real-Time Interactive Video Generation

arXiv:2605.15141v1 Announce Type: new Abstract: Real-time interactive video generation requires low-latency, streaming, and controllable rollout. Existing autoregressive (AR) diffusion distillation me

Causal Foundation Models with Continuous Treatments

arXiv:2605.15133v1 Announce Type: new Abstract: Causal inference, estimating causal effects from observational data, is a fundamental tool in many disciplines. Of particular importance across a variet

researcharxiv-cs-lg

Causal Multi-Task Demand Learning

arXiv:2602.09969v2 Announce Type: replace Abstract: We study a canonical multi-task demand-learning problem motivated by retail pricing, where a firm seeks to estimate heterogeneous linear price-respo

researcharxiv-cs-lg

Tutorials

Causal Time Series Generation via Diffusion Models

arXiv:2509.20846v3 Announce Type: replace Abstract: Time series generation (TSG) synthesizes realistic sequences and has achieved remarkable success. Among TSG, conditional models generate sequences g

tutorialsarxiv-cs-lg

CausalReasoningBenchmark: A Real-World Benchmark for Disentangled Evaluation of Causal Identification and Estimation

arXiv:2602.20571v2 Announce Type: replace Abstract: Many benchmarks for automated causal inference evaluate a system's performance based on a single numerical output, such as an Average Treatment Effe

CC-Pan: Channel-wise Compression based Diffusion for Efficient Pan-Sharpening

arXiv:2602.04473v2 Announce Type: replace Abstract: Recently, diffusion models have brought novel insights to pan-sharpening and notably boosted fusion precision. However, most existing models perform

Chain-of-Procedure: Hierarchical Visual-Language Reasoning for Procedural QA

model-releasesarxiv-cs-cl

arXiv:2605.14928v1 Announce Type: new Abstract: Recent advances in vision-language models (VLMs) have achieved impressive results on standard image-text tasks, yet their potential for visual procedure

Change of measure through the Legendre transform

arXiv:2202.05568v2 Announce Type: replace-cross Abstract: PAC-Bayes generalisation bounds are derived via change-of-measure inequalities that transfer concentration properties from a reference measure

researcharxiv-cs-lg

Tutorials

Characterizing the visual representation of objects from the child's view

arXiv:2605.14990v1 Announce Type: new Abstract: Children acquire object category representations from their everyday experiences in the first few years of life. What do the inputs to this learning pro

tutorialsarxiv-cs-cv

CHASM: Cross-frequency Harmonized Axis-Separable Mixing for Spectral Token Operators

arXiv:2605.14727v1 Announce Type: new Abstract: Spectral token mixers based on Fourier transforms provide an efficient way to model global interactions in visual feature maps. Existing designs often e

safetyarxiv-cs-cv

Chinese Short-Form Creative Content Generation via Explanation-Oriented Multi-Objective Optimization

arXiv:2511.15408v2 Announce Type: replace-cross Abstract: Chinese demonstrates high semantic compactness and rich metaphorical expressiveness, enabling limited text to convey dense meanings while incr

ChromaFlow: A Negative Ablation Study of Orchestration Overhead in Tool-Augmented Agent Evaluation

arXiv:2605.14102v1 Announce Type: new Abstract: Autonomous language-model agents increasingly combine planning, tool use, document processing, browsing, code execution, and verification loops. These c

Chrono-Gymnasium: An Open-Source, Gymnasium-Compatible Distributed Simulation Framework

arXiv:2605.14911v1 Announce Type: new Abstract: High-fidelity physics simulation is essential for closing the sim-to-real gap in robotics and complex mechanical systems. However, the computational ove

agentsarxiv-cs-ro

CineMesh4D: Personalized 4D Whole Heart Reconstruction from Sparse Cine MRI

arXiv:2605.13994v1 Announce Type: cross Abstract: Accurate 3D+t whole-heart mesh reconstruction from cine MRI is a clinically crucial yet technically challenging task. The difficulty of this task aris

ClawForge: Generating Executable Interactive Benchmarks for Command-Line Agents

arXiv:2605.14133v1 Announce Type: new Abstract: Interactive agent benchmarks face a tension between scalable construction and realistic workflow evaluation. Hand-authored tasks are expensive to extend

ClickRemoval: An Interactive Open-Source Tool for Object Removal in Diffusion Models

arXiv:2605.14461v1 Announce Type: new Abstract: Existing object removal tools often rely on manual masks or text prompts, making precise removal difficult for non-expert users in complex scenes and of

Closing the Gap on the Sample Complexity of 1-Identification

arXiv:2601.15620v2 Announce Type: replace Abstract: The 1-identification problem is a fundamental pure-exploration problem in multi-armed bandits. An agent aims to determine whether there exists an ar

agentsarxiv-cs-lg

CLOVER: Closed-Loop Value Estimation & Ranking for End-to-End Autonomous Driving Planning

arXiv:2605.15120v1 Announce Type: cross Abstract: End-to-end autonomous driving planners are commonly trained by imitating a single logged trajectory, yet evaluated by rule-based planning metrics that

Co-Me: Confidence-Guided Token Merging for Visual Geometric Transformers

arXiv:2511.14751v2 Announce Type: replace Abstract: We propose Confidence-Guided Token Merging (Co-Me), an acceleration mechanism for visual geometric transformers without retraining or finetuning the

COAL: Counterfactual and Observation-Enhanced Alignment Learning for Discriminative Referring Multi-Object Tracking

arXiv:2605.14795v1 Announce Type: new Abstract: Referring Multi-Object Tracking (RMOT) faces a fundamental structural contradiction between the high-discriminability demand and the sparse semantic sup

safetyarxiv-cs-cv

CoCo-InEKF: State Estimation with Learned Contact Covariances in Dynamic, Contact-Rich Scenarios

arXiv:2605.15122v1 Announce Type: cross Abstract: Robust state estimation for highly dynamic motion of legged robots remains challenging, especially in dynamic, contact-rich scenarios. Traditional app

applicationsarxiv-cs-lg

CoCoEdit: Content-Consistent Image Editing via Region Regularized Reinforcement Learning

model-releasesarxiv-cs-cv

arXiv:2602.14068v2 Announce Type: replace Abstract: Image editing has achieved impressive results with the development of large-scale generative models. However, existing models mainly focus on the ed

Coding Agent Is Good As World Simulator

arXiv:2605.14398v1 Announce Type: new Abstract: World models have emerged as a powerful paradigm for building interactive simulation environments, with recent video-based approaches demonstrating impr

Cognitive-Uncertainty Guided Knowledge Distillation for Accurate Classification of Student Misconceptions

arXiv:2605.14752v1 Announce Type: cross Abstract: Accurately identifying student misconceptions is crucial for personalized education but faces three challenges: (1) data scarcity with long-tail distr

Collaborative Yet Personalized Policy Training: Single-Timescale Federated Actor-Critic

arXiv:2605.14423v1 Announce Type: cross Abstract: Despite the popularity of the actor-critic method and the practical needs of collaborative policy training, existing works typically either overlook e

safetyarxiv-cs-ai

Collider-Bench: Benchmarking AI Agents with Particle Physics Analysis Reproduction

arXiv:2605.13950v1 Announce Type: cross Abstract: Autonomous language-model agents are increasingly evaluated on long-horizon tool-use tasks, but existing benchmarks rarely capture the complexity and

Communication-Efficient Federated Fine-Tuning

model-releasesarxiv-cs-lg

arXiv:2505.04535v3 Announce Type: replace Abstract: Federated Learning (FL) enables the utilization of vast, previously inaccessible data sources. At the same time, pre-trained Language Models (LMs) h

Comparative Evaluation of Machine Learning Approaches for Minority-Class Financial Distress Prediction Under Class Imbalance Constraints

arXiv:2605.14067v1 Announce Type: new Abstract: Financial distress prediction remains a significant challenge in enterprise risk analysis due to the highly imbalanced nature of real-world financial da

applicationsarxiv-cs-lg

Comparing Developer and LLM Biases in Code Evaluation

arXiv:2603.24586v2 Announce Type: replace-cross Abstract: As LLMs are increasingly used as judges in code applications, they should be evaluated in realistic interactive settings that capture partial

safetyarxiv-cs-cl

Complacent, Not Sycophantic: Reframing Large Language Models and Designing AI Literacy for Complacent Machines

arXiv:2605.14544v1 Announce Type: new Abstract: Large language models are often described as sycophantic, in the sense that they appear to flatter users or mirror their beliefs. We argue that this lab

safetyarxiv-cs-ai

Local Ai

Composable Crystals: Controllable Materials Discovery via Concept Learning

arXiv:2605.14769v1 Announce Type: new Abstract: De novo crystal generation, a central task in materials discovery, aims to generate crystals that are simultaneously valid, stable, unique, and novel. E

local-aiarxiv-cs-lg

Compositional Sparsity as an Inductive Bias for Neural Architecture Design

arXiv:2605.14764v1 Announce Type: cross Abstract: Identifying the structural priors that enable Deep Neural Networks (DNNs) to overcome the curse of dimensionality is a fundamental challenge in machin

safetyarxiv-cs-ai

Compositional Video Generation via Inference-Time Guidance

arXiv:2605.14988v1 Announce Type: new Abstract: Text-to-video diffusion models generate realistic videos, but often fail on prompts requiring fine-grained compositional understanding, such as relation

Computational Imaging Priors for Wireless Capsule Endoscopy: Monte Carlo-Guided Hemoglobin Mapping for Rare-Anomaly Detection

arXiv:2605.15062v1 Announce Type: new Abstract: Background. RGB-trained capsule-endoscopy classifiers underperform on small-vessel vascular findings by conflating hemoglobin contrast with bile and ill

Concurrency without Model Changes: Future-based Asynchronous Function Calling for LLMs

arXiv:2605.15077v1 Announce Type: cross Abstract: Function calling, also known as tool use, is a core capability of modern LLM agents but is typically constrained by synchronous execution semantics. U

Local Ai

Conditional Attribute Estimation with Autoregressive Sequence Models

arXiv:2605.14004v1 Announce Type: new Abstract: Generative models are often trained with a next-token prediction objective, yet many downstream applications require the ability to estimate or control

local-aiarxiv-cs-ai