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BERAG: Bayesian Ensemble Retrieval-Augmented Generation for Knowledge-based Visual Question Answering

arXiv:2604.22678v1 Announce Type: new Abstract: A common approach to question answering with retrieval-augmented generation (RAG) is to concatenate documents into a single context and pass it to a lan

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Beyond Chain-of-Thought: Rewrite as a Universal Interface for Generative Multimodal Embeddings

DGX agent

arXiv:2604.22280v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have emerged as a promising foundation for universal multimodal embeddings. Recent studies have shown that reas

safetyarxiv-cs-cv

27 Apr 2026

Hardware

Beyond Land Surface Temperature: Explainable Spatial Machine Learning Reveals Urban Morphology Effects on Human-Centric Heat Stress

arXiv:2604.22433v1 Announce Type: new Abstract: Heat exposure connects the built environment and public health, directly shaping the livability and sustainability of urban areas. Understanding the spa

hardwarearxiv-cs-lg

Tutorials

Beyond Patient Invariance: Learning Cardiac Dynamics via Action-Conditioned JEPAs

arXiv:2604.22618v1 Announce Type: new Abstract: Self-supervised learning in healthcare has largely relied on invariance-based objectives, which maximize similarity between different views of the same

tutorialsarxiv-cs-lg

BLAST: Benchmarking LLMs with ASP-based Structured Testing

arXiv:2604.22306v1 Announce Type: cross Abstract: Large Language Models (LLMs) have demonstrated remarkable performance across a broad spectrum of tasks, including natural language understanding, dial

Breaking Watermarks in the Frequency Domain: A Modulated Diffusion Attack Framework

arXiv:2604.22220v1 Announce Type: new Abstract: Digital image watermarking has advanced rapidly for copyright protection of generative AI, yet the comparatively limited progress in watermark attack te

researcharxiv-cs-cv

Bridging the Long-Tail Gap: Robust Retrieval-Augmented Relation Completion via Multi-Stage Paraphrase Infusion

model-releasesarxiv-cs-cl

arXiv:2604.22261v1 Announce Type: new Abstract: Large language models (LLMs) struggle with relation completion (RC), both with and without retrieval-augmented generation (RAG), particularly when the r

CAGE-SGG: Counterfactual Active Graph Evidence for Open-Vocabulary Scene Graph Generation

arXiv:2604.22274v1 Announce Type: new Abstract: Open-vocabulary scene graph generation (SGG) aims to describe visual scenes with flexible and fine-grained relation phrases beyond a fixed predicate voc

researcharxiv-cs-cv

Calibrated Principal Component Regression

arXiv:2510.19020v2 Announce Type: replace-cross Abstract: We propose a new method for statistical inference in generalized linear models. In the overparameterized regime, Principal Component Regressio

safetyarxiv-cs-lg

Calibrating Behavioral Parameters with Large Language Models

arXiv:2602.01022v2 Announce Type: replace-cross Abstract: Behavioral parameters such as loss aversion, herding, and extrapolation are central to asset pricing models but remain difficult to measure re

Call-Chain-Aware LLM-Based Test Generation for Java Projects

arXiv:2604.22046v1 Announce Type: cross Abstract: Large language models (LLMs) have recently shown strong potential for generating project-level unit tests. However, existing state-of-the-art approach

Can Large Language Models Adequately Perform Symbolic Reasoning Over Time Series?

arXiv:2508.03963v4 Announce Type: replace Abstract: Uncovering hidden symbolic laws from time series data, as an aspiration dating back to Kepler's discovery of planetary motion, remains a core challe

Can QPP Choose the Right Query Variant? Evaluating Query Variant Selection for RAG Pipelines

arXiv:2604.22661v1 Announce Type: cross Abstract: Large Language Models (LLMs) have made query reformulation ubiquitous in modern retrieval and Retrieval-Augmented Generation (RAG) pipelines, enabling

researcharxiv-cs-cl

Categorical Perception in Large Language Model Hidden States: Structural Warping at Digit-Count Boundaries

arXiv:2603.28258v2 Announce Type: replace-cross Abstract: Categorical perception (CP) -- enhanced discriminability at category boundaries -- is among the most studied phenomena in perceptual psycholog

Causal Concept Graphs in LLM Latent Space for Stepwise Reasoning

arXiv:2603.10377v2 Announce Type: replace-cross Abstract: Sparse autoencoders can localize where concepts live in language models, but not how they interact during multi-step reasoning. We propose Cau

CGC: Compositional Grounded Contrast for Fine-Grained Multi-Image Understanding

arXiv:2604.22498v1 Announce Type: cross Abstract: Although Multimodal Large Language Models (MLLMs) have advanced rapidly, they still face notable challenges in fine-grained multi-image understanding,

Chain-of-Memory: Lightweight Memory Construction with Dynamic Evolution for LLM Agents

arXiv:2601.14287v2 Announce Type: replace Abstract: External memory systems are pivotal for enabling Large Language Model (LLM) agents to maintain persistent knowledge and perform long-horizon decisio

ChangeQuery: Advancing Remote Sensing Change Analysis for Natural and Human-Induced Disasters from Visual Detection to Semantic Understanding

arXiv:2604.22333v1 Announce Type: cross Abstract: Rapid situational awareness is critical in post-disaster response. While remote sensing damage assessment is evolving from pixel-level change detectio

Characterizing pitch and roll torque coupling in insect-sized flapping-wing robots using a microfabricated gimbal

arXiv:2604.22121v1 Announce Type: cross Abstract: Sub-gram flapping-wing flying insect robots (FIRs) are challenging to model because of mechanical complexity in their wings, unsteady aerodynamic flow

researcharxiv-cs-ro

CharTide: Data-Centric Chart-to-Code Generation via Tri-Perspective Tuning and Inquiry-Driven Evolution

model-releasesarxiv-cs-cv

arXiv:2604.22192v1 Announce Type: new Abstract: Chart-to-code generation demands strict visual precision and syntactic correctness from Vision-Language Models (VLMs). However, existing approaches are

CLARITY: A Framework and Benchmark for Conversational Language Ambiguity and Unanswerability in Interactive NL2SQL Systems

model-releasesarxiv-cs-cl

arXiv:2604.22313v1 Announce Type: new Abstract: NL2SQL systems deployed in industry settings often encounter ambiguous or unanswerable queries, particularly in interactive scenarios with incomplete us

Closed Form Relations and Higher-Order Approximations of First and Second Derivatives of the Tangent Operator on SE(3)

arXiv:2604.22287v1 Announce Type: cross Abstract: The Lie group SE(3) of isometric orientation preserving transformation is used for modeling multibody systems, robots, and Cosserat continua. The use

researcharxiv-cs-ro

Clutter-Robust Vision-Language-Action Models through Object-Centric and Geometry Grounding

arXiv:2512.22519v2 Announce Type: replace Abstract: Recent Vision-Language-Action (VLA) models have made impressive progress toward general-purpose robotic manipulation by post-training large Vision-L

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Tutorials

CLVAE: A Variational Autoencoder for Long-Term Customer Revenue Forecasting

arXiv:2604.22636v1 Announce Type: cross Abstract: Predicting customers' long-term revenue from sparse and irregular transaction data is central to marketing resource allocation in non-contractual sett

tutorialsarxiv-cs-lg

CNSL-bench: Benchmarking the Sign Language Understanding Capabilities of MLLMs on Chinese National Sign Language

arXiv:2604.22367v1 Announce Type: cross Abstract: Sign language research has achieved significant progress due to the advances in large language models (LLMs). However, the intrinsic ability of LLMs t

Applications

CodeGraphVLP: Code-as-Planner Meets Semantic-Graph State for Non-Markovian Vision-Language-Action Models

arXiv:2604.22238v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models promise generalist robot manipulation, but are typically trained and deployed as short-horizon policies that assume

applicationsarxiv-cs-ro

CognitiveTwin: Robust Multi-Modal Digital Twins for Predicting Cognitive Decline in Alzheimer's Disease

arXiv:2604.22428v1 Announce Type: new Abstract: Predicting individual cognitive decline in Alzheimer's disease (AD) is difficult due to the heterogeneity of disease progression. Reliable clinical tool

Concave Statistical Utility Maximization Bandits via Influence-Function Gradients

arXiv:2604.22140v1 Announce Type: cross Abstract: We study stochastic multi-armed bandits in which the objective is a statistical functional of the long-run reward distribution, rather than expected r

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Applications

Conditional anomaly detection using soft harmonic functions: An application to clinical alerting

arXiv:2604.21956v1 Announce Type: new Abstract: Timely detection of concerning events is an important problem in clinical practice. In this paper, we consider the problem of conditional anomaly detect

applicationsarxiv-cs-lg

Conditional Diffusion Posterior Alignment for Sparse-View CT Reconstruction

arXiv:2604.21960v1 Announce Type: cross Abstract: Computed Tomography (CT) is a widely used imaging modality in medical and industrial applications. To limit radiation exposure and measurement time, t

safetyarxiv-cs-cv

Conformalized Super Learner

arXiv:2604.22391v1 Announce Type: cross Abstract: The Super Learner (SL) is a widely used ensemble method that combines predictions from a library of learners based on their predictive performance. In

Consequentialist Objectives and Catastrophe

arXiv:2603.15017v3 Announce Type: replace Abstract: Because human preferences are too complex to codify, AIs operate with misspecified objectives. Optimizing such objectives often produces undesirable

Context-Fidelity Boosting: Enhancing Faithful Generation through Watermark-Inspired Decoding

arXiv:2604.22335v1 Announce Type: new Abstract: Large language models (LLMs) often produce content that contradicts or overlooks information provided in the input context, a phenomenon known as faithf

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Tutorials

Context-Sensitive Abstractions for Reinforcement Learning with Parameterized Actions

arXiv:2512.20831v2 Announce Type: replace Abstract: Real-world sequential decision-making often involves parameterized action spaces that require both, decisions regarding discrete actions and decisio

tutorialsarxiv-cs-ai

Contexts are Never Long Enough: Structured Reasoning for Scalable Question Answering over Long Document Sets

arXiv:2604.22294v1 Announce Type: cross Abstract: Real-world document question answering is challenging. Analysts must synthesize evidence across multiple documents and different parts of each documen

Applications

Contrastive Semantic Projection: Faithful Neuron Labeling with Contrastive Examples

arXiv:2604.22477v1 Announce Type: new Abstract: Neuron labeling assigns textual descriptions to internal units of deep networks. Existing approaches typically rely on highly activating examples, often

applicationsarxiv-cs-cv

Controllable Spoken Dialogue Generation: An LLM-Driven Grading System for K-12 Non-Native English Learners

arXiv:2604.22542v1 Announce Type: cross Abstract: Large language models (LLMs) often fail to meet the pedagogical needs of K-12 English learners in non-native contexts due to a proficiency mismatch. T

Agents

Cost-Effective Communication: An Auction-based Method for Language Agent Interaction

arXiv:2511.13193v2 Announce Type: replace Abstract: Multi-agent systems (MAS) built on large language models (LLMs) often suffer from inefficient 'free-for-all' communication, leading to exponential t

agentsarxiv-cs-ai

CRAFT: Clustered Regression for Adaptive Filtering of Training data

arXiv:2604.22693v1 Announce Type: cross Abstract: Selecting a small, high-quality subset from a large corpus for fine-tuning is increasingly important as corpora grow to tens of millions of datapoints

Cross-Domain Offshore Wind Power Forecasting: Transfer Learning Through Meteorological Clusters

arXiv:2601.19674v2 Announce Type: replace-cross Abstract: Ambitious decarbonisation targets are rapidly increasing the commission of new offshore wind farms. For these newly commissioned plants to run

Cross-Session Decoding of Neural Spiking Data via Task-Conditioned Latent Alignment

arXiv:2601.19963v2 Announce Type: replace-cross Abstract: Training a high-performing neural decoder can be difficult when only limited data are available from a recording session. To address this chal

Agents

Cross-Stage Coherence in Hierarchical Driving VQA: Explicit Baselines and Learned Gated Context Projectors

arXiv:2604.22560v1 Announce Type: cross Abstract: Graph Visual Question Answering (GVQA) for autonomous driving organizes reasoning into ordered stages, namely Perception, Prediction, and Planning, wh

agentsarxiv-cs-ai

CrystalX: High-accuracy Crystal Structure Analysis Using Deep Learning

arXiv:2410.13713v2 Announce Type: replace Abstract: Atomic structure analysis of crystalline materials is a paramount endeavor in both chemical and material sciences. This sophisticated technique nece

da4ml: Distributed Arithmetic for Real-time Neural Networks on FPGAs

arXiv:2507.04535v2 Announce Type: replace-cross Abstract: Neural networks with a latency requirement on the order of microseconds, like the ones used at the CERN Large Hadron Collider, are typically d

Data-Driven Analysis of AI in Medical Device Software in China: Trends of Deep Learning and Traditional AI Based on Regulatory Data

arXiv:2411.07378v3 Announce Type: replace Abstract: Artificial intelligence (AI) in medical device software (MDSW) represents a transformative clinical technology, attracting increasing attention with

Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy

arXiv:2604.22562v1 Announce Type: cross Abstract: Client contribution estimation in Federated Learning is necessary for identifying clients' importance and for providing fair rewards. Current methods

Decoding High-Dimensional Finger Motion from EMG Using Riemannian Features and RNNs

model-releasesarxiv-cs-lg

arXiv:2604.22499v1 Announce Type: new Abstract: Continuous estimation of high-dimensional finger kinematics from forearm surface electromyography (EMG) could enable natural control for hand prostheses

Decomposed Attention Fusion in MLLMs for Training-Free Video Reasoning Segmentation

arXiv:2510.19592v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) demonstrate strong video understanding by attending to visual tokens relevant to textual queries. To direct

researcharxiv-cs-cv