Search: “models”

Variational Matrix-Learning Fourier Networks for Parametric Multiphysics Surrogates

arXiv:2605.02280v1 Announce Type: new Abstract: Multiphysics simulation is critical for system-technology co-optimization (STCO) in chiplet-based design, but repeated finite-element solutions of PDE-g

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Applications

VAUQ: Vision-Aware Uncertainty Quantification for LVLM Self-Evaluation

DGX agent

arXiv:2602.21054v2 Announce Type: replace-cross Abstract: Large Vision-Language Models (LVLMs) frequently hallucinate, limiting their safe deployment in real-world applications. Existing LLM self-eval

applicationsarxiv-cs-cl

5 May 2026

VERGE: Formal Refinement and Guidance Engine for Verifiable LLM Reasoning

arXiv:2601.20055v2 Announce Type: replace Abstract: Despite the syntactic fluency of Large Language Models (LLMs), ensuring their logical correctness in high-stakes domains remains a fundamental chall

local-aiarxiv-cs-cl

ViewSAM: Learning View-aware Cross-modal Semantics for Weakly Supervised Cross-view Referring Multi-Object Tracking

arXiv:2605.02638v1 Announce Type: new Abstract: Cross-view Referring Multi-Object Tracking (CRMOT) aims to track multiple objects specified by natural language across multiple camera views, with globa

researcharxiv-cs-cv

Watermarking LLM Agent Trajectories

arXiv:2602.18700v2 Announce Type: replace-cross Abstract: LLM agents rely heavily on high-quality trajectory data to guide their problem-solving behaviors, yet producing such data requires substantial

When Good OCR Is Not Enough: Benchmarking OCR Robustness for Retrieval-Augmented Generation

arXiv:2605.00911v1 Announce Type: new Abstract: Industrial Retrieval-Augmented Generation (RAG) systems depend on optical character recognition (OCR) to transform visual documents into text. Existing

When Less Is More: Simplicity Beats Complexity for Physics-Constrained InSAR Phase Unwrapping

arXiv:2605.00896v1 Announce Type: new Abstract: Operational phase unwrapping is the primary computational bottleneck in InSAR-based volcanic and seismic monitoring. We challenge the industry trend of

Hardware

WindowQuant: Mixed-Precision KV Cache Quantization based on Window-Level Similarity for VLMs Inference Optimization

arXiv:2605.02262v1 Announce Type: cross Abstract: Recently, video language models (VLMs) have been applied in various fields. However, the visual token sequence of the VLM is too long, which may cause

hardwarearxiv-cs-cl

Tutorials

XekRung Technical Report

arXiv:2605.00072v1 Announce Type: cross Abstract: We present XekRung, a frontier large language model for cybersecurity, designed to provide comprehensive security capabilities. To achieve this, we de

tutorialsarxiv-cs-ai

A Comparative Study of QSPR Methods on a Unique Multitask PAMPA dataset

arXiv:2605.00508v1 Announce Type: new Abstract: We present a unique, multitask dataset comprising 143 drug and drug candidate molecules, each evaluated on in vitro, parallel artificial-membrane permea

researcharxiv-cs-lg

A Novel Patch-Based TDA Approach for Computed Tomography Imaging

arXiv:2512.12108v5 Announce Type: replace Abstract: The development of machine learning models based on computed tomography (CT) imaging has been a major focus due to the promise that imaging holds fo

researcharxiv-cs-cv

Comparing Exploration-Exploitation Strategies of LLMs and Humans: Insights from Standard Multi-armed Bandit Experiments

arXiv:2505.09901v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used to simulate or automate human behavior in complex sequential decision-making settings. A na

researcharxiv-cs-cl

CompleteRXN: Toward Completing Open Chemical Reaction Databases

arXiv:2605.00222v1 Announce Type: new Abstract: Chemical reaction datasets such as USPTO suffer from substantial incompleteness, frequently missing byproducts, co-reactants, and stoichiometric coeffic

CURE-OOD: Benchmarking Out-of-Distribution Detection for Survival Prediction

arXiv:2605.00350v1 Announce Type: new Abstract: ``How long can I live and remain free of cancer?'' is often the first question a patient asks after receiving a cancer diagnosis and treatment. Accurate

Dynamics-Encoded Deep Learning for Robust System Identification and Parameter Estimation

arXiv:2410.04299v2 Announce Type: replace Abstract: Incorporating a priori physics knowledge into machine learning leads to more robust and interpretable algorithms. In this work, we combine deep lear

Escaping Mode Collapse in LLM Generation via Geometric Regulation

arXiv:2605.00435v1 Announce Type: new Abstract: Mode collapse is a persistent challenge in generative modeling and appears in autoregressive text generation as behaviors ranging from explicit looping

researcharxiv-cs-cl

Safety

Estimating LLM Grading Ability and Response Difficulty in Automatic Short Answer Grading via Item Response Theory

arXiv:2605.00238v1 Announce Type: new Abstract: Automated short answer grading (ASAG) with large language models (LLMs) is commonly evaluated with aggregate metrics such as macro-F1 and Cohen's kappa.

safetyarxiv-cs-cl

From Birdsong to Rumbles: Classifying Elephant Calls with Out-of-Species Embeddings

arXiv:2605.00225v1 Announce Type: cross Abstract: We show that pretrained acoustic embeddings classify elephant vocalisations at a level approaching that of end-to-end supervised neural networks, with

local-aiarxiv-cs-lg

High-Probability Convergence in Decentralized Stochastic Optimization with Gradient Tracking

arXiv:2605.00281v1 Announce Type: new Abstract: We study high-probability (HP) convergence guarantees in decentralized stochastic optimization, where multiple agents collaborate to jointly train a mod

Applications

LambdaRankIC: Directly Optimizing Rank IC for Financial Prediction

arXiv:2605.00501v1 Announce Type: new Abstract: In financial predictions, the performance of machine learning models is often assessed by Rank IC, which is the Spearman rank correlation between the mo

applicationsarxiv-cs-lg

Learning Locally, Revising Globally: Global Reviser for Federated Learning with Noisy Labels

arXiv:2412.00452v2 Announce Type: replace-cross Abstract: Conventioanl federated learning (FL) heavily depends on high-quality labels, which are often impractical in the real world, leading to the fed

M-CaStLe: Uncovering Local Causal Structures in Multivariate Space-Time Gridded Data

arXiv:2605.00398v1 Announce Type: new Abstract: Causal graph discovery for space-time systems is challenging in high-dimensional gridded data, which often has many more grid cells than temporal observ

MoDAl: Self-Supervised Neural Modality Discovery via Decorrelation for Speech Neuroprosthesis

arXiv:2605.00025v1 Announce Type: cross Abstract: Speech neuroprosthesis systems decode intended speech from neural activity in the absence of audible output, offering a path to restoring communicatio

Odysseus: Scaling VLMs to 100+ Turn Decision-Making in Games via Reinforcement Learning

arXiv:2605.00347v1 Announce Type: cross Abstract: Given the rapidly growing capabilities of vision-language models (VLMs), extending them to interactive decision-making tasks such as video games has e

researcharxiv-cs-cl

On the Expressive Power of Contextual Relations in Transformers

arXiv:2603.25860v2 Announce Type: replace-cross Abstract: Transformer architectures have achieved remarkable empirical success in modeling contextual relations, yet a clear understanding of their expr

researcharxiv-cs-lg

OTSS: Output-Targeted Soft Segmentation for Contextual Decision-Weight Learning

arXiv:2605.00193v1 Announce Type: new Abstract: Many machine learning systems make constrained decisions by optimizing factorized objectives, but the context-specific objective is often treated as fix

Paired-CSLiDAR: Height-Stratified Registration for Cross-Source Aerial-Ground LiDAR Pose Refinement

arXiv:2605.00634v1 Announce Type: cross Abstract: We introduce Paired-CSLiDAR (CSLiDAR), a cross-source aerial-ground LiDAR benchmark for single-scan pose refinement: refining a ground-scan pose withi

PILIR: Physics-Informed Local Implicit Representation

arXiv:2605.00385v1 Announce Type: new Abstract: Physics-Informed Neural Networks have become a powerful mesh-free method for solving partial differential equations, but their performance is often limi

Applications

Prediction of Alzheimer's Disease Risk Factors from Retinal Images via Deep Learning: Development and Validation of Biologically Relevant Morphological Associations in the UK Biobank

arXiv:2605.00665v1 Announce Type: new Abstract: The systemic, metabolic, lifestyle factors have established associations with Alzheimer's Disease (AD) through epidemiologic and AD-specific biomarker s

applicationsarxiv-cs-cv

Prefer-DAS: Learning from Local Preferences and Sparse Prompts for Domain Adaptive Segmentation of Electron Microscopy

arXiv:2602.19423v3 Announce Type: replace Abstract: Domain adaptive segmentation (DAS) is a promising paradigm for delineating intracellular structures from various large-scale electron microscopy (EM

local-aiarxiv-cs-cv

ScreenParse: Moving Beyond Sparse Grounding with Complete Screen Parsing Supervision

arXiv:2602.14276v2 Announce Type: replace Abstract: Modern computer-use agents (CUA) must perceive a screen as a structured state, what elements are visible, where they are, and what text they contain

SHIFT: Robust Double Machine Learning for Average Dose-Response Functions under Heavy-Tailed Contamination

arXiv:2605.00176v1 Announce Type: cross Abstract: Double-machine-learning pipelines for the Average Dose-Response Function rely on kernel-weighted local-linear smoothers, which inherit unbounded funct

SketchGuard: Scaling Byzantine-Robust Decentralized Federated Learning via Sketch-Based Screening

arXiv:2510.07922v4 Announce Type: replace Abstract: Decentralized Federated Learning (DFL) enables privacy-preserving collaborative training without centralized servers but remains vulnerable to Byzan

researcharxiv-cs-lg

Stepper: Stepwise Immersive Scene Generation with Multiview Panoramas

arXiv:2603.28980v2 Announce Type: replace Abstract: The synthesis of immersive 3D scenes from text is rapidly maturing, driven by novel video generative models and feed-forward 3D reconstruction, with

researcharxiv-cs-cv

Value Explicit Pretraining for Learning Transferable Representations

arXiv:2312.12339v3 Announce Type: replace Abstract: Understanding visual inputs for a given task amidst varied changes is a key challenge posed by visual reinforcement learning agents. We propose exti

Vesselpose: Vessel Graph Reconstruction from Learned Voxel-wise Direction Vectors in 3D Vascular Images

arXiv:2605.00538v1 Announce Type: new Abstract: Blood vessel segmentation and -tracing are essential tasks in many medical imaging applications. Although numerous methods exist, the prevailing segment

When RAG Chatbots Expose Their Backend: An Anonymized Case Study of Privacy and Security Risks in Patient-Facing Medical AI

arXiv:2605.00796v1 Announce Type: cross Abstract: Background: Patient-facing medical chatbots based on retrieval-augmented generation (RAG) are increasingly promoted to deliver accessible, grounded he

A Gated Hybrid Contrastive Collaborative Filtering Recommendation

arXiv:2604.27117v1 Announce Type: cross Abstract: Recommender systems increasingly incorporate textual reviews to enrich user and item representations. However, most review-aware models remain optimiz

researcharxiv-cs-ai

Safety

A Woman with a Knife or A Knife with a Woman? Measuring Directional Bias Amplification in Image Captions

arXiv:2503.07878v5 Announce Type: replace-cross Abstract: When we train models on biased datasets, they not only reproduce data biases, but can worsen them at test time - a phenomenon called bias ampl

safetyarxiv-cs-ai

Agents

Agentic Compilation: Mitigating the LLM Rerun Crisis for Minimized-Inference-Cost Web Automation

arXiv:2604.09718v2 Announce Type: cross Abstract: LLM-driven web agents operating through continuous inference loops -- repeatedly querying a model to evaluate browser state and select actions -- exhi

agentsarxiv-cs-ai

Agentic Education: Using Claude Code to Teach Claude Code

arXiv:2604.17460v2 Announce Type: replace-cross Abstract: AI coding assistants have proliferated rapidly, yet structured pedagogical frameworks for learning these tools remain scarce. Developers face

APPSI-139: A Parallel Corpus of English Application Privacy Policy Summarization and Interpretation

arXiv:2604.27550v1 Announce Type: cross Abstract: Privacy policies are essential for users to understand how service providers handle their personal data. However, these documents are often long and c

BoostLoRA: Growing Effective Rank by Boosting Adapters

arXiv:2604.27308v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning (PEFT) methods face a tradeoff between adapter size and expressivity: ultra-low-parameter adapters are confined to fix

CausalCompass: Evaluating the Robustness of Time-Series Causal Discovery in Misspecified Scenarios

arXiv:2602.07915v2 Announce Type: replace-cross Abstract: Causal discovery from time series is a fundamental task in machine learning. However, its widespread adoption is hindered by a reliance on unt

Do Papers Tell the Whole Story? A Benchmark and Framework for Uncovering Hidden Implementation Gaps in Bioinformatics

arXiv:2603.22018v2 Announce Type: replace Abstract: Ensuring consistency between research papers and their corresponding software code implementations is a fundamental prerequisite for guaranteeing th

Effective Prompt Pool Learning for Continual Category Discovery

arXiv:2407.19001v3 Announce Type: replace Abstract: This paper studies effective prompt pool learning for Continual Category Discovery (CCD), a challenging open-world setting where a model must discov

local-aiarxiv-cs-cv

Entropy of Ukrainian

arXiv:2604.27534v1 Announce Type: new Abstract: In natural language processing, the entropy of a language is a measure of its unpredictability and complexity. The first study on this subject was condu

FinChain: A Symbolic Benchmark for Verifiable Chain-of-Thought Financial Reasoning

arXiv:2506.02515v4 Announce Type: replace-cross Abstract: Multi-step symbolic reasoning is essential for robust financial analysis; yet, current benchmarks largely overlook this capability. Existing d