Search: “concepts”

Methods, Data, and Conceptual Change: Reflections from Two Quantitative Diachronic Case Studies

arXiv:2605.02052v1 Announce Type: new Abstract: This discussion paper reflects on how quantitative approaches to historical linguistics interact with dataset properties. Drawing on two worked examples

researcharxiv-cs-cl

5 May 2026

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Meritocratic Fairness in Budgeted Combinatorial Multi-armed Bandits via Shapley Values

DGX agent

arXiv:2605.00762v1 Announce Type: new Abstract: We propose a new framework for meritocratic fairness in budgeted combinatorial multi-armed bandits with full-bandit feedback (BCMAB-FBF). Unlike semi-ba

safetyarxiv-cs-lg

4 May 2026

Trident: Improving Malware Detection with LLMs and Behavioral Features

arXiv:2605.00297v1 Announce Type: cross Abstract: Traditionally, machine learning methods for PE malware detection have relied on static features like byte histograms, string information, and PE heade

4 May 2026

Crosscoding Through Time: Tracking Emergence & Consolidation Of Linguistic Representations Throughout LLM Pretraining

arXiv:2509.05291v2 Announce Type: replace-cross Abstract: Large language models (LLMs) learn non-trivial abstractions during pretraining, such as detecting irregular plural noun subjects. However, bec

tutorialsarxiv-cs-ai

Fitting Horn DL Ontologies to ABox and Query Examples: A Tale of Simulation Quantifiers and Finite Models

arXiv:2604.26976v1 Announce Type: cross Abstract: We study the problem of fitting a description logic (DL) ontology to a given set of positive and negative examples that take the form of an ABox and a

researcharxiv-cs-ai

Agents

Imitation Game for Adversarial Disillusion with Chain-of-Thought Reasoning in Generative AI

arXiv:2501.19143v2 Announce Type: replace Abstract: As the cornerstone of artificial intelligence, machine perception confronts a fundamental threat posed by adversarial illusions. These adversarial a

agentsarxiv-cs-ai

Learning-to-Explain through 20Q Gaming: An Explainable Recommender for Cybersecurity Education

arXiv:2604.26964v1 Announce Type: cross Abstract: The growing sophistication of contemporary cyber threats necessitates a more effective and adaptive approach to cybersecurity training. Intuitive and

DCD: Decomposition-based Causal Discovery from Autocorrelated and Non-Stationary Temporal Data

arXiv:2602.01433v2 Announce Type: replace Abstract: Multivariate time series in domains such as finance, climate science, and healthcare often exhibit long-term trends, seasonal patterns, and short-te

Learning Illumination Control in Diffusion Models

arXiv:2604.24877v1 Announce Type: new Abstract: Controlling illumination in images is essential for photography and visual content creation. While closed-source models have demonstrated impressive ill

Model Releases

Personalization Toolkit: Training Free Personalization of Large Vision Language Models

model-releasesarxiv-cs-cv

arXiv:2502.02452v4 Announce Type: replace Abstract: Personalization of Large Vision-Language Models (LVLMs) involves customizing models to recognize specific users or object instances and to generate

The Rashomon Effect for Visualizing High-Dimensional Data

arXiv:2604.00485v2 Announce Type: replace Abstract: Dimension reduction (DR) is inherently non-unique: multiple embeddings can preserve the structure of high-dimensional data equally well while differ

safetyarxiv-cs-lg

Agents

Accelerating Reinforcement Learning for Wind Farm Control via Expert Demonstrations

arXiv:2604.22794v1 Announce Type: cross Abstract: Reinforcement learning (RL) offers a promising approach for adaptive wind farm flow control, yet its practical deployment is hindered by slow training

agentsarxiv-cs-lg

ANCHOR: LLM-driven Subject Conditioning for Text-to-Image Synthesis

arXiv:2404.10141v2 Announce Type: replace-cross Abstract: Text-to-image (T2I) models have achieved remarkable progress in high-quality image synthesis, yet most benchmarks rely on simple, self-contain

safetyarxiv-cs-cl

AV-Master: Dual-Path Comprehensive Perception Makes Better Audio-Visual Question Answering

arXiv:2510.18346v2 Announce Type: replace Abstract: Audio-Visual Question Answering (AVQA) requires models to effectively utilize both visual and auditory modalities to answer complex and diverse ques

Model Releases

EgoDyn-Bench: Evaluating Ego-Motion Understanding in Vision-Centric Foundation Models for Autonomous Driving

model-releasesarxiv-cs-cl

arXiv:2604.22851v1 Announce Type: cross Abstract: While Vision-Language Models (VLMs) have advanced highlevel reasoning in autonomous driving, their ability to ground this reasoning in the underlying

Learning Interpretable PDE Representations for Generative Reconstructions with Structured Sparsity

arXiv:2604.23867v1 Announce Type: new Abstract: Scientific measurements are often bottlenecked by suboptimal conditions, whether that be noise, incomplete spatial coverage, or limited resolution, rend

MindTrellis: Co-Creating Knowledge Structures with AI through Interactive Visual Exploration

arXiv:2604.23129v1 Announce Type: cross Abstract: Knowledge workers face increasing challenges in synthesizing information from multiple documents into structured conceptual understanding. This proces

researcharxiv-cs-ai

Agents

On the Convergence of Jacobian-Free Backpropagation for Optimal Control Problems with Implicit Hamiltonians

arXiv:2602.00921v2 Announce Type: replace-cross Abstract: Optimal feedback control with implicit Hamiltonians poses a fundamental challenge for learning-based value function methods due to the absence

agentsarxiv-cs-lg

SeqShield: A Behavioral Analysis Approach to Uncover Rootkits

arXiv:2604.23812v1 Announce Type: cross Abstract: Rootkits are among the most elusive types of malware, capable of bypassing traditional static analysis methods due to their metamorphic behavior. Sign

SMoES: Soft Modality-Guided Expert Specialization in MoE-VLMs

arXiv:2604.23996v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) has become a prevalent backbone for large vision-language models (VLMs), yet how modality-specific signals should guide expert

Translate or Simplify First: An Analysis of Cross-lingual Text Simplification in English and French

arXiv:2604.23844v1 Announce Type: new Abstract: Cross-Lingual Text Simplification (CLTS) aims to make content more accessible across languages by simultaneously addressing both linguistic complexity a

researcharxiv-cs-cl

Understanding Representation Gaps Across Scales in Tropical Tree Species Classification from Drone Imagery

arXiv:2604.23019v1 Announce Type: new Abstract: Accurate classification of tropical tree species from unoccupied aerial vehicle (UAV) imagery remains challenging due to high species diversity and stro

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VAMP-Net: An Interpretable Multi-Path Network of Genomic Permutation-Invariant Set Attention and Quality-Aware 1D-CNN for MTB Drug Resistance

arXiv:2512.21786v2 Announce Type: replace Abstract: Genomic prediction of drug resistance in Mycobacterium tuberculosis is often hindered by complex epistatic interactions and variable sequencing qual

A Nationwide Japanese Medical Claims Foundation Model: Balancing Model Scaling and Task-Specific Computational Efficiency

arXiv:2604.22348v1 Announce Type: new Abstract: Clinical risk prediction using longitudinal medical data supports individualized care. Self-supervised foundation models have emerged as a promising app

Adversarial Co-Evolution of Malware and Detection Models: A Bilevel Optimization Perspective

arXiv:2604.22569v1 Announce Type: cross Abstract: Machine learning-based malware detectors are increasingly vulnerable to adversarial examples. Traditional defenses, such as one-shot adversarial train

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,

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

Efficient Diffusion Distillation via Embedding Loss

arXiv:2604.22379v1 Announce Type: new Abstract: Recent advances in distilling expensive diffusion models into efficient few-step generators show significant promise. However, these methods typically d

Ethics Testing: Proactive Identification of Generative AI System Harms

arXiv:2604.22089v1 Announce Type: cross Abstract: Generative Artificial Intelligence (GAI) systems that can automatically generate content in the form of source code or other contents (e.g., images) h

Generating Synthetic Malware Samples Using Generative AI

arXiv:2604.22084v1 Announce Type: new Abstract: Malware attacks have a significant negative impact on organizations of varied scales in the field of cybersecurity. Recently, malware researchers have i

Addressing divergent representations from causal interventions on neural networks

arXiv:2511.04638v5 Announce Type: replace-cross Abstract: A common approach to mechanistic interpretability is to causally manipulate model representations via targeted interventions in order to under

researcharxiv-cs-ai

Compose and Fuse: Revisiting the Foundational Bottlenecks in Multimodal Reasoning

arXiv:2509.23744v3 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) promise enhanced reasoning by integrating diverse inputs such as text, vision, and audio. Yet cross-m

DCMorph: Face Morphing via Dual-Stream Cross-Attention Diffusion

arXiv:2604.21627v1 Announce Type: new Abstract: Advancing face morphing attack techniques is crucial to anticipate evolving threats and develop robust defensive mechanisms for identity verification sy

Explainable Disentangled Representation Learning for Generalizable Authorship Attribution in the Era of Generative AI

arXiv:2604.21300v1 Announce Type: new Abstract: Learning robust representations of authorial style is crucial for authorship attribution and AI-generated text detection. However, existing methods ofte

tutorialsarxiv-cs-cl

ID-Eraser: Proactive Defense Against Face Swapping via Identity Perturbation

arXiv:2604.21465v1 Announce Type: new Abstract: Deepfake technologies have rapidly advanced with modern generative AI, and face swapping in particular poses serious threats to privacy and digital secu

Information Bottleneck-Guided Heterogeneous Graph Learning for Interpretable Neurodevelopmental Disorder Diagnosis

arXiv:2502.20769v3 Announce Type: replace Abstract: Developing interpretable models for neurodevelopmental disorders (NDDs) diagnosis presents significant challenges in effectively encoding, decoding,

LatRef-Diff: Latent and Reference-Guided Diffusion for Facial Attribute Editing and Style Manipulation

arXiv:2604.21279v1 Announce Type: new Abstract: Facial attribute editing and style manipulation are crucial for applications like virtual avatars and photo editing. However, achieving precise control

Quotient-Space Diffusion Models

arXiv:2604.21809v1 Announce Type: cross Abstract: Diffusion-based generative models have reformed generative AI, and have enabled new capabilities in the science domain, for example, generating 3D str

S1-VL: Scientific Multimodal Reasoning Model with Thinking-with-Images

arXiv:2604.21409v1 Announce Type: new Abstract: We present S1-VL, a multimodal reasoning model for scientific domains that natively supports two complementary reasoning paradigms: Scientific Reasoning

Model Releases

Symbolic Grounding Reveals Representational Bottlenecks in Abstract Visual Reasoning

model-releasesarxiv-cs-ai

arXiv:2604.21346v1 Announce Type: new Abstract: Vision--language models (VLMs) often fail on abstract visual reasoning benchmarks such as Bongard problems, raising the question of whether the main bot

The Effect of Idea Elaboration on the Automatic Assessment of Idea Originality

arXiv:2604.20569v1 Announce Type: cross Abstract: Automatic systems are increasingly used to assess the originality of responses in creative tasks. They offer a potential solution to key limitations o

Transfer Learning for Loan Recovery Prediction under Distribution Shifts with Heterogeneous Feature Spaces

arXiv:2604.02832v2 Announce Type: replace-cross Abstract: Accurate forecasting of recovery rates (RR) is central to credit risk management and regulatory capital determination. In many loan portfolios

UniGenDet: A Unified Generative-Discriminative Framework for Co-Evolutionary Image Generation and Generated Image Detection

arXiv:2604.21904v1 Announce Type: new Abstract: In recent years, significant progress has been made in both image generation and generated image detection. Despite their rapid, yet largely independent

safetyarxiv-cs-cv

VFM-VAE: Vision Foundation Models Can Be Good Tokenizers for Latent Diffusion Models

arXiv:2510.18457v3 Announce Type: replace Abstract: The performance of Latent Diffusion Models (LDMs) is critically dependent on the quality of their visual tokenizers. While recent works have explore

safetyarxiv-cs-cv

CXR-LanIC: Language-Grounded Interpretable Classifier for Chest X-Ray Diagnosis

arXiv:2510.21464v2 Announce Type: replace Abstract: Deep learning models have achieved remarkable accuracy in chest X-ray diagnosis, yet their widespread clinical adoption remains limited by the black

Image Generators are Generalist Vision Learners

arXiv:2604.20329v1 Announce Type: cross Abstract: Recent works show that image and video generators exhibit zero-shot visual understanding behaviors, in a way reminiscent of how LLMs develop emergent

tutorialsarxiv-cs-ai

MD-Face: MoE-Enhanced Label-Free Disentangled Representation for Interactive Facial Attribute Editing

arXiv:2604.20317v1 Announce Type: new Abstract: GAN-based facial attribute editing is widely used in virtual avatars and social media but often suffers from attribute entanglement, where modifying one

Membership Inference for Contrastive Pre-training Models with Text-only PII Queries

arXiv:2603.14222v2 Announce Type: replace-cross Abstract: Contrastive pretraining models such as CLIP and CLAP, serve as the ubiquitous perceptual backbones for modern multimodal large models, yet the