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QCalEval: Benchmarking Vision-Language Models for Quantum Calibration Plot Understanding

arXiv:2604.25884v1 Announce Type: cross Abstract: Quantum computing calibration depends on interpreting experimental data, and calibration plots provide the most universal human-readable representatio

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QFlash: Bridging Quantization and Memory Efficiency in Vision Transformer Attention

DGX agent

arXiv:2604.25306v1 Announce Type: new Abstract: FlashAttention improves efficiency through tiling, but its online softmax still relies on floating-point arithmetic for numerical stability, making full

researcharxiv-cs-lg

29 Apr 2026

Quantifying and Mitigating Socially Desirable Responding in LLMs: A Desirability-Matched Graded Forced-Choice Psychometric Study

model-releasesarxiv-cs-cl

arXiv:2602.17262v2 Announce Type: replace Abstract: Human self-report questionnaires are increasingly used in NLP to benchmark and audit large language models (LLMs), from persona consistency to safet

Quantum Dynamics via Score Matching on Bohmian Trajectories

arXiv:2604.25137v1 Announce Type: cross Abstract: We solve the time-dependent Schrodinger equation by learning the score function, the gradient of the log-probability density, on Bohmian trajectories.

Quantum-Inspired Robust and Scalable SAR Object Classification

arXiv:2604.25755v1 Announce Type: cross Abstract: SAR image classification naturally has to deal with huge noise and a high dynamic range particularly requiring robust classification models. Additiona

Query-Efficient Quantum Approximate Optimization via Graph-Conditioned Trust Regions

arXiv:2604.24803v1 Announce Type: new Abstract: In low-depth implementations of the Quantum Approximate Optimization Algorithm (QAOA), the dominant cost is often the number of objective evaluations ra

Agents

R^3-SQL: Ranking Reward and Resampling for Text-to-SQL

arXiv:2604.25325v1 Announce Type: cross Abstract: Modern Text-to-SQL systems generate multiple candidate SQL queries and rank them to judge a final prediction. However, existing methods face two limit

agentsarxiv-cs-cl

RABC-Net: Reliability-Aware Annotation-Free Skin Lesion Segmentation for Low-Resource Dermoscopy

arXiv:2604.05594v2 Announce Type: replace Abstract: Pixel-level annotation is costly in low-resource dermoscopy. We present RABC-Net, a reliability-aware annotation-free segmentation system that combi

local-aiarxiv-cs-cv

Rapid tracking through strongly scattering media with physics-informed neuromorphic speckle analysis

arXiv:2604.25310v1 Announce Type: new Abstract: This work addresses the critical problem of tracking fast-moving objects through strongly scattering media in a low-light environment. Different from ex

RbtAct: Rebuttal as Supervision for Actionable Review Feedback Generation

model-releasesarxiv-cs-cl

arXiv:2603.09723v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used across the scientific workflow, including to draft peer-review reports. However, many AI-generate

RCProb: Probabilistic Rule Extraction for Efficient Simplification of Tree Ensembles

arXiv:2604.25304v1 Announce Type: new Abstract: Tree ensembles are widely used in industrial machine learning due to their strong predictive performance and efficient training procedures. However, as

Agents

Recursive Multi-Agent Systems

arXiv:2604.25917v1 Announce Type: cross Abstract: Recursive or looped language models have recently emerged as a new scaling axis by iteratively refining the same model computation over latent states

agentsarxiv-cs-cl

Reference-Augmented Learning for Precise Tracking Policy of Tendon-Driven Continuum Robots

arXiv:2604.25698v1 Announce Type: new Abstract: Tendon-Driven Continuum Robots (TDCRs) pose significant control challenges due to their highly nonlinear, path-dependent dynamics and non-Markovian char

safetyarxiv-cs-ro

Refinement via Regeneration: Enlarging Modification Space Boosts Image Refinement in Unified Multimodal Models

arXiv:2604.25636v1 Announce Type: new Abstract: Unified multimodal models (UMMs) integrate visual understanding and generation within a single framework. For text-to-image (T2I) tasks, this unified ca

safetyarxiv-cs-cv

Reinforcement Learning for Testing Interdependent Requirements in Autonomous Vehicles: An Empirical Study

arXiv:2502.15792v2 Announce Type: replace-cross Abstract: Autonomous vehicles (AVs) make driving decisions without humans, making dependability assurance critical. Scenario-based testing is widely use

safetyarxiv-cs-lg

Reinforcement Learning Using known Invariances

arXiv:2511.03473v2 Announce Type: replace Abstract: In many real-world reinforcement learning (RL) problems, the environment exhibits inherent symmetries that can be exploited to improve learning effi

applicationsarxiv-cs-lg

Relational In-Context Learning via Synthetic Pre-training with Structural Prior

arXiv:2603.03805v2 Announce Type: replace Abstract: Relational Databases (RDBs) are the backbone of modern business, yet they lack foundation models comparable to those in text or vision. A key obstac

applicationsarxiv-cs-lg

RELIC: Evaluating Complex Reasoning via the Recognition of Languages In-Context

arXiv:2506.05205v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used to solve complex tasks where they must retrieve and compose many pieces of in-context information

applicationsarxiv-cs-cl

Representation Paradigms in AI-based 3D Radiological Image Reconstruction: A Systematic Review

arXiv:2504.11349v3 Announce Type: replace Abstract: The demand for high-quality medical imaging in clinical practice and assisted diagnosis has made 3D image reconstruction in radiological imaging a k

ResetEdit: Precise Text-guided Editing of Generated Image via Resettable Starting Latent

arXiv:2604.25128v1 Announce Type: new Abstract: Recent advances in diffusion models have enabled high-quality image generation, leading to increasing demand for post-generation editing that modifies l

safetyarxiv-cs-cv

Residual-loss Anomaly Analysis of Physics-Informed Neural Networks: An Inverse Method for Change-point Detection in Nonlinear Dynamical Systems with Regime Switching

arXiv:2604.25655v1 Announce Type: cross Abstract: Nonlinear dynamical systems with regime transitions are typically described by ordinary differential equations with jumping parameters parameters. Tra

ReSim: Reliable World Simulation for Autonomous Driving

arXiv:2506.09981v2 Announce Type: replace Abstract: How can we reliably simulate future driving scenarios under a wide range of ego driving behaviors? Recent driving world models, developed exclusivel

safetyarxiv-cs-cv

Rethinking Efficiency in Neural Combinatorial Optimization: Batched Preference Optimization with Mamba

arXiv:2602.20730v2 Announce Type: replace Abstract: We study efficiency as a first-class objective in Neural Combinatorial Optimization (NCO) and present ECO, an efficient learning framework that comb

local-aiarxiv-cs-lg

Rethinking Entropy Interventions in RLVR: An Entropy Change Perspective

arXiv:2510.10150v3 Announce Type: replace Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) serves as a cornerstone technique for enhancing the reasoning capabilities of Large Language M

safetyarxiv-cs-lg

Rethinking Layer Redundancy in Large Language Models: Calibration Objectives and Search for Depth Pruning

arXiv:2604.24938v1 Announce Type: cross Abstract: Depth pruning improves the inference efficiency of large language models by removing Transformer blocks. Prior work has focused on importance criteria

researcharxiv-cs-cl

Revisiting the Past: Data Unlearning with Model State History

arXiv:2506.20941v3 Announce Type: replace Abstract: Large language models are trained on massive corpora of web data, which may include private data, copyrighted material, factually inaccurate data, o

RISE: Self-Improving Robot Policy with Compositional World Model

arXiv:2602.11075v2 Announce Type: replace Abstract: Despite the sustained scaling on model capacity and data acquisition, Vision-Language-Action (VLA) models remain brittle in contact-rich and dynamic

safetyarxiv-cs-ro

Robust Deepfake Detection: Mitigating Spatial Attention Drift via Calibrated Complementary Ensembles

arXiv:2604.25889v1 Announce Type: new Abstract: Current deepfake detection models achieve state-of-the-art performance on pristine academic datasets but suffer severe spatial attention drift under rea

applicationsarxiv-cs-cv

Robust Graph Matching through Semantic Relationship Generation for SLAM

arXiv:2604.25404v1 Announce Type: new Abstract: Graph-based representations such as Scene Graphs enable localization in structured indoor environments by matching a locally observed graph, constructed

researcharxiv-cs-ro

Robustness Evaluation of a Foundation Segmentation Model Under Simulated Domain Shifts in Abdominal CT: Implications for Health Digital Twin Deployment

arXiv:2604.25685v1 Announce Type: cross Abstract: Foundation segmentation models such as the Segment Anything Model (SAM) have demonstrated strong generalization across natural images; however, their

applicationsarxiv-cs-cv

Safe-Support Q-Learning: Learning without Unsafe Exploration

arXiv:2604.25379v1 Announce Type: new Abstract: Ensuring safety during reinforcement learning (RL) training is critical in real-world applications where unsafe exploration can lead to devastating outc

safetyarxiv-cs-lg

SaliencyDecor: Enhancing Neural Network Interpretability through Feature Decorrelation

arXiv:2604.25315v1 Announce Type: new Abstract: Gradient-based saliency methods are widely used to interpret deep neural networks, yet they often produce noisy and unstable explanations that poorly al

SAMe: A Semantic Anatomy Mapping Engine for Robotic Ultrasound

arXiv:2604.25646v1 Announce Type: new Abstract: Robotic ultrasound has advanced local image-driven control, contact regulation, and view optimization, yet current systems lack the anatomical understan

local-aiarxiv-cs-cv

SARE: Sample-wise Adaptive Reasoning for Training-free Fine-grained Visual Recognition

arXiv:2603.17729v3 Announce Type: replace Abstract: Recent advances in Large Vision-Language Models (LVLMs) have enabled training-free Fine-Grained Visual Recognition (FGVR). However, effectively expl

SARU: A Shadow-Aware and Removal Unified Framework for Remote Sensing Images with New Benchmarks

arXiv:2604.25432v1 Announce Type: new Abstract: Shadows are a prevalent problem in remote sensing imagery (RSI), degrading visual quality and severely limiting the performance of downstream tasks like

Scalable Secure Biometric Authentication without Auxiliary Identifiers

arXiv:2604.25071v1 Announce Type: cross Abstract: The prevalence of biometric authentication has been on the rise due to its ease of use and elimination of weak passwords. To date, most biometric auth

local-aiarxiv-cs-cv

Scaling Probabilistic Transformer via Efficient Cross-Scale Hyperparameter Transfer

model-releasesarxiv-cs-cl

arXiv:2604.25409v1 Announce Type: new Abstract: Probabilistic Transformer (PT), a white-box probabilistic model for contextual word representation, has demonstrated substantial similarity to standard

Agents

SciDER: Scientific Data-centric End-to-end Researcher

arXiv:2603.01421v2 Announce Type: replace-cross Abstract: Automated scientific discovery with large language models is transforming the research lifecycle from ideation to experimentation, yet existin

agentsarxiv-cs-cl

SecureScan: An AI-Driven Multi-Layer Framework for Malware and Phishing Detection Using Logistic Regression and Threat Intelligence Integration

arXiv:2602.10750v2 Announce Type: replace-cross Abstract: The growing sophistication of modern malware and phishing campaigns has diminished the effectiveness of traditional signature-based intrusion

Self-DACE++: Robust Low-Light Enhancement via Efficient Adaptive Curve Estimation

arXiv:2604.25367v1 Announce Type: new Abstract: In this paper, we present Self-DACE++, an improved unsupervised and lightweight framework for Low-Light Image Enhancement (LLIE), building upon our prev

Semantic-aware Random Convolution and Source Matching for Domain Generalization in Medical Image Segmentation

arXiv:2512.01510v3 Announce Type: replace Abstract: We tackle the challenging problem of single-source domain generalization (DG) for medical image segmentation, where we train a network on one domain

Sensitivity-Based Tube NMPC for Cooperative Aerial Structures Under Parametric Uncertainty

arXiv:2604.25766v1 Announce Type: new Abstract: This paper presents a sensitivity-based tube Nonlinear Model Predictive Control (NMPC) framework for cooperative aerial chains under bounded parametric

researcharxiv-cs-ro

ShapeY: A Principled Framework for Measuring Shape Recognition Capacity via Nearest-Neighbor Matching

arXiv:2604.25065v1 Announce Type: new Abstract: Object recognition (OR) in humans relies heavily on shape cues and the ability to recognize objects across varying 3D viewpoints. Unlike humans, deep ne

Sharp Capacity Scaling of Spectral Optimizers in Learning Associative Memory

arXiv:2603.26554v2 Announce Type: replace Abstract: Spectral optimizers such as Muon have recently shown strong empirical performance in large-scale language model training, but the source and extent

Sharp Risk Bounds for Early-Stopping in Gaussian Linear Regression

arXiv:2503.03426v2 Announce Type: replace Abstract: We study early-stopped mirror descent (ESMD) for high-dimensional Gaussian linear regression over arbitrary convex bodies and design matrices, where

Shearlet Neural Operators for Anisotropic-Shock-Dominated and Multi-scale parametric partial differential equations

arXiv:2604.25181v1 Announce Type: new Abstract: Neural operators have emerged as powerful data-driven surrogates for learning solution operators of parametric partial differential equations (PDEs). Ho

SIEVES: Selective Prediction Generalizes through Visual Evidence Scoring

arXiv:2604.25855v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) achieve ever-stronger performance on visual-language tasks. Even as traditional visual question answering bench

Sketch2Arti: Sketch-based Articulation Modeling of CAD Objects

arXiv:2604.25781v1 Announce Type: new Abstract: Articulation modeling aims to infer movable parts and their motion parameters for a 3D object, enabling interactive animation, simulation, and shape edi