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arXiv:2602.08129v2 Announce Type: replace-cross Abstract: Predicting interdependent load values in multiport scatterers is challenging due to high dimensionality and complex dependence between impedan

applicationsarxiv-cs-lg

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Before You Interpret the Profile: Validity Scaling for LLM Metacognitive Self-Report

DGX agent

arXiv:2604.17707v1 Announce Type: new Abstract: Clinical personality assessment screens response validity before interpreting substantive scales. LLM evaluation does not. We apply the validity scaling

researcharxiv-cs-cl

21 Apr 2026

Tutorials

Beyond Attack Success Rate: A Multi-Metric Evaluation of Adversarial Transferability in Medical Imaging Models

arXiv:2604.16532v1 Announce Type: new Abstract: While deep learning systems are becoming increasingly prevalent in medical image analysis, their vulnerabilities to adversarial perturbations raise seri

tutorialsarxiv-cs-cv

Budget-Aware Anytime Reasoning with LLM-Synthesized Preference Data

arXiv:2601.11038v2 Announce Type: replace Abstract: We study the reasoning behavior of large language models (LLMs) under limited computation budgets. In such settings, producing useful partial soluti

Do LLMs Use Cultural Knowledge Without Being Told? A Multilingual Evaluation of Implicit Pragmatic Adaptation

arXiv:2604.17718v1 Announce Type: new Abstract: Many benchmarks show that large language models can answer direct questions about culture. We study a different question: do they also change how they s

safetyarxiv-cs-cl

Forecasting Ionospheric Irregularities on GNSS Lines of Sight Using Dynamic Graphs with Ephemeris Conditioning

model-releasesarxiv-cs-lg

arXiv:2604.18379v1 Announce Type: new Abstract: Most data-driven ionospheric forecasting models operate on gridded products, which do not preserve the time-varying sampling structure of satellite-base

Functional Similarity Metric for Neural Networks: Overcoming Parametric Ambiguity via Activation Region Analysis

model-releasesarxiv-cs-lg

arXiv:2604.16426v1 Announce Type: new Abstract: As modern deep learning architectures grow in complexity, representational ambiguity emerges as a critical barrier to their interpretability and reliabl

Online Conformal Prediction with Adversarial Semi-bandit Feedback via Regret Minimization

arXiv:2604.17984v1 Announce Type: new Abstract: Uncertainty quantification is crucial in safety-critical systems, where decisions must be made under uncertainty. In particular, we consider the problem

Local Ai

Region-Grounded Report Generation for 3D Medical Imaging: A Fine-Grained Dataset and Graph-Enhanced Framework

arXiv:2604.18145v1 Announce Type: new Abstract: Automated medical report generation for 3D PET/CT imaging is fundamentally challenged by the high-dimensional nature of volumetric data and a critical s

local-aiarxiv-cs-cv

Spectral bandits for smooth graph functions

arXiv:2604.18420v1 Announce Type: cross Abstract: Smooth functions on graphs have wide applications in manifold and semi-supervised learning. In this paper, we study a bandit problem where the payoffs

SPOT: Single-Shot Positioning via Trainable Near-Field Rainbow Beamforming

arXiv:2511.11391v3 Announce Type: replace Abstract: Phase-time arrays, which integrate phase shifters (PSs) and true-time delays (TTDs), have emerged as a cost-effective architecture for generating fr

researcharxiv-cs-lg

TimeColor: Flexible Reference Colorization via Temporal Concatenation

model-releasesarxiv-cs-cv

arXiv:2601.00296v2 Announce Type: replace Abstract: Most colorization models condition only on a single reference, typically the first frame of the scene. However, this approach ignores other sources

Adaptive multi-fidelity optimization with fast learning rates

arXiv:2604.16239v1 Announce Type: cross Abstract: In multi-fidelity optimization, biased approximations of varying costs of the target function are available. This paper studies the problem of optimiz

Beyond a Single Frame: Multi-Frame Spatially Grounded Reasoning Across Volumetric MRI

model-releasesarxiv-cs-ai

arXiv:2604.15808v1 Announce Type: cross Abstract: Spatial reasoning and visual grounding are core capabilities for vision-language models (VLMs), yet most medical VLMs produce predictions without tran

CLIMB: Controllable Longitudinal Brain Image Generation using Mamba-based Latent Diffusion Model and Gaussian-aligned Autoencoder

arXiv:2604.15611v1 Announce Type: cross Abstract: Latent diffusion models have emerged as powerful generative models in medical imaging, enabling the synthesis of high quality brain magnetic resonance

researcharxiv-cs-ai

DALM: A Domain-Algebraic Language Model via Three-Phase Structured Generation

model-releasesarxiv-cs-ai

arXiv:2604.15593v1 Announce Type: cross Abstract: Large language models compress heterogeneous knowledge into a single parameter space, allowing facts from different domains to interfere during genera

Exploitation Over Exploration: Unmasking the Bias in Linear Bandit Recommender Offline Evaluation

arXiv:2507.18756v2 Announce Type: replace Abstract: Multi-Armed Bandit (MAB) algorithms are widely used in recommender systems that require continuous, incremental learning. A core aspect of MABs is t

Sample Complexity Bounds for Stochastic Shortest Path with a Generative Model

arXiv:2604.16111v1 Announce Type: new Abstract: We study the sample complexity of learning an epsilon-optimal policy in the Stochastic Shortest Path (SSP) problem. We first derive sample complexity bo

Local Ai

TableSeq: Unified Generation of Structure, Content, and Layout

arXiv:2604.16070v1 Announce Type: new Abstract: We present TableSeq, an image-only, end-to-end framework for joint table structure recognition, content recognition, and cell localization. The model fo

local-aiarxiv-cs-cv

The Harder Path: Last Iterate Convergence for Uncoupled Learning in Zero-Sum Games with Bandit Feedback

arXiv:2604.16087v1 Announce Type: new Abstract: We study the problem of learning in zero-sum matrix games with repeated play and bandit feedback. Specifically, we focus on developing uncoupled algorit

A Nonasymptotic Theory of Gain-Dependent Error Dynamics in Behavior Cloning

arXiv:2604.14484v1 Announce Type: new Abstract: Behavior cloning (BC) policies on position-controlled robots inherit the closed-loop response of the underlying PD controller, yet the effect of control

researcharxiv-cs-ro

Acceptance Dynamics Across Cognitive Domains in Speculative Decoding

arXiv:2604.14682v1 Announce Type: cross Abstract: Speculative decoding accelerates large language model (LLM) inference. It uses a small draft model to propose a tree of future tokens. A larger target

Benchmarking Classical Coverage Path Planning Heuristics on Irregular Hexagonal Grids for Maritime Coverage Scenarios

model-releasesarxiv-cs-ro

arXiv:2604.15202v1 Announce Type: new Abstract: Coverage path planning on irregular hexagonal grids is relevant to maritime surveillance, search and rescue and environmental monitoring, yet classical

Bias in Surface Electromyography Features across a Demographically Diverse Cohort

arXiv:2604.14460v1 Announce Type: cross Abstract: Neuromotor decoding from upper-limb electromyography (sEMG) can enhance human-machine interfaces and offer a more natural means of controlling prosthe

Calibration-Gated LLM Pseudo-Observations for Online Contextual Bandits

arXiv:2604.14961v1 Announce Type: new Abstract: Contextual bandit algorithms suffer from high regret during cold-start, when the learner has insufficient data to distinguish good arms from bad. We pro

Controlling Authority Retrieval: A Missing Retrieval Objective for Authority-Governed Knowledge

arXiv:2604.14488v1 Announce Type: cross Abstract: In any domain where knowledge accumulates under formal authority -- law, drug regulation, software security -- a later document can formally void an e

From Risk to Rescue: An Agentic Survival Analysis Framework for Liquidation Prevention

arXiv:2604.14583v1 Announce Type: new Abstract: Decentralized Finance (DeFi) lending protocols like Aave v3 rely on over-collateralization to secure loans, yet users frequently face liquidation due to

Tutorials

Learning temporal embeddings from electronic health records of chronic kidney disease patients

arXiv:2601.18675v2 Announce Type: replace Abstract: We investigate whether temporal embedding models trained on longitudinal electronic health records can learn clinically meaningful representations w

tutorialsarxiv-cs-lg

Multi-User mmWave Beam and Rate Adaptation via Combinatorial Satisficing Bandits

arXiv:2604.14908v1 Announce Type: new Abstract: We study downlink beam and rate adaptation in a multi-user mmWave MISO system where multiple base stations (BSs), each using analog beamforming from fin

PROXIMA: A Reliability Scoring Framework for Proxy Metrics in Online Controlled Experiments

arXiv:2604.14352v1 Announce Type: cross Abstract: Online A/B testing at scale relies on proxy metrics -- short-term, easily-measured signals used in place of slow-moving long-term outcomes. When the p

SatBLIP: Context Understanding and Feature Identification from Satellite Imagery with Vision-Language Learning

arXiv:2604.14373v1 Announce Type: new Abstract: Rural environmental risks are shaped by place-based conditions (e.g., housing quality, road access, land-surface patterns), yet standard vulnerability i

Switch-KD: Visual-Switch Knowledge Distillation for Vision-Language Models

arXiv:2604.14629v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have shown remarkable capabilities in joint vision-language understanding, but their large scale poses significant challen

Tug-of-War within A Decade: Conflict Resolution in Vulnerability Analysis via Teacher-Guided Retrieval-Augmented Generations

arXiv:2604.14172v1 Announce Type: new Abstract: Large Language Models (LLMs) are essential for analyzing and addressing vulnerabilities in cybersecurity. However, among over 200,000 vulnerabilities we

researcharxiv-cs-cl

When Fairness Metrics Disagree: Evaluating the Reliability of Demographic Fairness Assessment in Machine Learning

arXiv:2604.15038v1 Announce Type: cross Abstract: The evaluation of fairness in machine learning systems has become a central concern in high-stakes applications, including biometric recognition, heal

Counterfactual Peptide Editing for Causal TCR--pMHC Binding Inference

model-releasesarxiv-cs-lg

arXiv:2604.13256v1 Announce Type: new Abstract: Neural models for TCR-pMHC binding prediction are susceptible to shortcut learning: they exploit spurious correlations in training data -- such as pepti

Language steering in latent space to mitigate unintended code-switching

arXiv:2510.13849v3 Announce Type: replace Abstract: Multilingual Large Language Models (LLMs) often exhibit hallucinations such as unintended code-switching, reducing reliability in downstream tasks.

Remember Me, Refine Me: A Dynamic Procedural Memory Framework for Experience-Driven Agent Evolution

arXiv:2512.10696v2 Announce Type: replace-cross Abstract: Procedural memory enables large language model (LLM) agents to internalize 'how-to' knowledge, theoretically reducing redundant trial-and-erro

agentsarxiv-cs-cl

Sandpile Economics: Theory, Identification, and Evidence

arXiv:2604.13890v1 Announce Type: cross Abstract: Why do capitalist economies recurrently generate crises whose severity is disproportionate to the size of the triggering shock? This paper proposes a

agentsarxiv-cs-lg

WebXSkill: Skill Learning for Autonomous Web Agents

arXiv:2604.13318v1 Announce Type: cross Abstract: Autonomous web agents powered by large language models (LLMs) have shown promise in completing complex browser tasks, yet they still struggle with lon

agentsarxiv-cs-cl

A document is worth a structured record: Principled inductive bias design for document recognition

arXiv:2507.08458v2 Announce Type: replace-cross Abstract: Many document types use intrinsic, convention-driven structures that serve to encode precise and structured information, such as the conventio

A Theoretical Comparison of No-U-Turn Sampler Variants: Necessary and Sufficient Convergence Conditions and Mixing Time Analysis under Gaussian Targets

arXiv:2603.18640v3 Announce Type: replace-cross Abstract: The No-U-Turn Sampler (NUTS) is the computational workhorse of modern Bayesian software libraries, yet its qualitative and quantitative conver

researcharxiv-cs-lg

Beyond Factual Grounding: The Case for Opinion-Aware Retrieval-Augmented Generation

arXiv:2604.12138v1 Announce Type: new Abstract: RAG systems have transformed how LLMs access external knowledge, but we find that current implementations exhibit a bias toward factual, objective conte

CamReasoner: Reinforcing Camera Movement Understanding via Structured Spatial Reasoning

arXiv:2602.00181v3 Announce Type: replace-cross Abstract: Understanding camera dynamics is a fundamental pillar of video spatial intelligence. However, existing multimodal models predominantly treat t

Challenging Vision-Language Models with Physically Deployable Multimodal Semantic Lighting Attacks

arXiv:2604.12833v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have shown remarkable performance, yet their security remains insufficiently understood. Existing adversarial studies focu

DocSeeker: Structured Visual Reasoning with Evidence Grounding for Long Document Understanding

arXiv:2604.12812v1 Announce Type: new Abstract: Existing Multimodal Large Language Models (MLLMs) suffer from significant performance degradation on the long document understanding task as document le

GF-Score: Certified Class-Conditional Robustness Evaluation with Fairness Guarantees

model-releasesarxiv-cs-ai

arXiv:2604.12757v1 Announce Type: cross Abstract: Adversarial robustness is essential for deploying neural networks in safety-critical applications, yet standard evaluation methods either require expe

Applications

GGD-SLAM: Monocular 3DGS SLAM Powered by Generalizable Motion Model for Dynamic Environments

arXiv:2604.12837v1 Announce Type: new Abstract: Visual SLAM algorithms achieve significant improvements through the exploration of 3D Gaussian Splatting (3DGS) representations, particularly in generat

applicationsarxiv-cs-ro

Hear Both Sides: Efficient Multi-Agent Debate via Diversity-Aware Message Retention

arXiv:2603.20640v2 Announce Type: replace Abstract: Multi-Agent Debate has emerged as a promising framework for improving the reasoning quality of large language models through iterative inter-agent c

agentsarxiv-cs-cl