Search: “models”

A Sparse Bayesian Learning Algorithm for Estimation of Interaction Kernels in Motsch-Tadmor Model

arXiv:2505.07068v2 Announce Type: replace-cross Abstract: In this paper, we investigate the data-driven identification of asymmetric interaction kernels in the Motsch-Tadmor model based on observed tr

researcharxiv-cs-lg

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Research

An Improved Method for Personalizing Diffusion Models

DGX agent

arXiv:2407.05312v2 Announce Type: replace Abstract: Diffusion models have demonstrated impressive image generation capabilities. Personalized approaches, such as textual inversion and Dreambooth, enha

researcharxiv-cs-cv

3 Jun 2026

Attention, May I Have Your Decision? Localizing Generative Choices in Diffusion Models

arXiv:2604.06052v2 Announce Type: replace Abstract: Text-to-image diffusion models exhibit remarkable generative capabilities, yet their internal operations remain opaque, particularly when handling p

local-aiarxiv-cs-cv

Beyond Semantics: Modeling Factual and Affective Perceptual Experiences from Vision-Language Data

arXiv:2606.03345v1 Announce Type: cross Abstract: We present P-Topics (Perception Topics) modeling, a novel problem for understanding how images are perceived affectively and across cultures. The goal

BYORn: Bootstrap Your Own Responses to Defend Large Vision-Language Models Against Backdoor Attacks

arXiv:2606.02947v1 Announce Type: cross Abstract: Supervised fine-tuning is the predominant approach for adapting autoregressive vision-language models to downstream tasks. Recent work has shown that

researcharxiv-cs-cv

CoralBay: A Self-Supervised CT Foundation Model

model-releasesarxiv-cs-cv

arXiv:2606.03888v1 Announce Type: new Abstract: Self-supervised learning has enabled large-scale pre-training on 2D natural images, producing general-purpose visual representations that transfer effec

DMT-CBT: Longitudinal Therapeutic State Modeling for CBT Counseling

arXiv:2606.03132v1 Announce Type: new Abstract: Large language models (LLMs) have shown growing potential for Cognitive Behavioral Therapy (CBT) counseling. However, most existing approaches still for

local-aiarxiv-cs-cl

Do Explanations Increase the Risk of Decision Logic Leakage? Explanation-Guided Stealing of Graph Models

arXiv:2506.03087v2 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) have become essential tools for analyzing graph-structured data in domains such as drug discovery and financial a

safetyarxiv-cs-ai

FlowGuard: Flow Matching for Identity-Independent Detection of Data-Free Model Stealing Attacks on Energy System Intrusion Detection Systems

arXiv:2606.03430v1 Announce Type: cross Abstract: Artificial Intelligence (AI)-based Intrusion Detection Systems (IDS) deployed in energy infrastructure are vulnerable to model theft attacks, which al

Generalizing Graph Foundation Models via Hyperbolic Retrieval-Augmented Generation

arXiv:2606.03307v1 Announce Type: cross Abstract: Graph foundation models (GFMs) emerged as a dominant paradigm in graph representation learning by leveraging large-scale pre-training for cross-domain

local-aiarxiv-cs-ai

GeoSem-WAM: Geometry- and Semantic-Aware World Action Models

arXiv:2606.03188v1 Announce Type: new Abstract: Recent World Action Models (WAMs) have demonstrated impressive capabilities in embodied decision-making. However, whether their effectiveness stems from

researcharxiv-cs-ro

GuidedBridge: Training-freely Improving Bridge Models with Prior Guidance

arXiv:2606.03119v1 Announce Type: cross Abstract: Guidance methods, such as classifier-free guidance (CFG) and auto-guidance (AG), have advanced noise-to-data generation in diffusion models. Recently,

KBQA-R1: Reinforcing Large Language Models for Knowledge Base Question Answering

arXiv:2512.10999v3 Announce Type: replace Abstract: Knowledge Base Question Answering (KBQA) challenges models to bridge the gap between natural language and strict knowledge graph schemas by generati

safetyarxiv-cs-cl

Language Models Compare Quantities Using Number-specific and Unit-specific Heuristics

arXiv:2606.03982v1 Announce Type: new Abstract: Quantities with measurement units, such as 110 cm and 1.2 m, require language models (LMs) to combine a numeral with a symbolic unit scale. Here, we stu

Language Models Need Sleep: Learning to Self-Modify and Consolidate Memories

arXiv:2606.03979v1 Announce Type: cross Abstract: The past few decades have witnessed significant advances in the design of machine learning algorithms, from early studies on task-specific shallow mod

safetyarxiv-cs-ai

Neural Attention Search Linear: Towards Adaptive Token-Level Hybrid Attention Models

arXiv:2602.03681v2 Announce Type: replace Abstract: The quadratic computational complexity of softmax transformers has become a bottleneck in long-context scenarios. In contrast, linear attention mode

Applications

NewtPhys: Do Foundation Models Understand Newtonian Physics?

arXiv:2606.03986v1 Announce Type: new Abstract: Previous work has evaluated physics reasoning in foundation models using synthetic or semi-synthetic scenes and visual question-answering tasks. However

applicationsarxiv-cs-cv

Optimal Design and Analytical Modeling of a Soft Fin-Ray Effect Gripper Finger Using the Finite Rigid Elements Method

arXiv:2606.03798v1 Announce Type: new Abstract: Fin Ray-inspired soft grippers offer a promising solution for gently handling delicate, irregular objects, especially in agriculture. The objective of t

researcharxiv-cs-ro

Partially Observable Adversarial Patch Attacks on Vision-Language-Action Models in Robotics

arXiv:2606.03556v1 Announce Type: new Abstract: Vision-language-action (VLA) models are gaining attention in robotics, yet their robustness to adversarial attacks remains largely unexplored. Existing

local-aiarxiv-cs-ro

q0: Primitives for Hyper-Epoch Pretraining

model-releasesarxiv-cs-ai

arXiv:2606.03938v1 Announce Type: cross Abstract: Multi-epoch training is becoming the standard now that compute is growing faster than the supply of high-quality text. But pretraining a single model

SAMatcher: Co-Visibility Modeling with Segment Anything for Robust Feature Matching

arXiv:2606.03406v1 Announce Type: new Abstract: Reliable correspondence estimation is a fundamental problem in image processing, underpinning applications such as Structure from Motion, visual localiz

local-aiarxiv-cs-cv

See, Infer, Intervene: Proactive World Modeling for Goal-Oriented Social Intelligence

model-releasesarxiv-cs-cl

arXiv:2606.03371v1 Announce Type: new Abstract: Multimodal retail agents should not only recognize what a customer is doing, but also decide whether and how to assist before an explicit request is mad

Spatial Transcriptomics-Guided Alignment Enhances Molecular Profiling in Pathology Foundation Model

model-releasesarxiv-cs-lg

arXiv:2606.03644v1 Announce Type: new Abstract: Comprehensive molecular profiling is essential for modern precision oncology but remains hindered by prohibitive costs, specimen exhaustion, and protrac

TGV-KV: Text-Grounded KV Eviction for Vision-Language Models

arXiv:2606.03075v1 Announce Type: new Abstract: Vision-Language Models (VLMs) inherit the auto-regressive generation paradigm and cache the keys and values (KV) of all previous tokens to accelerate in

safetyarxiv-cs-cv

Validation-Gated Multi-Agent Governance for Online Adaptation of Thermal-Hydraulic Surrogate Models under Operating-Regime Shift

arXiv:2606.03321v1 Announce Type: new Abstract: Artificial-intelligence surrogates can support second-by-second thermal-hydraulic forecasting, but models selected and frozen offline may become conditi

safetyarxiv-cs-lg

A Theoretical Framework for Statistical Evaluability of Generative Models

arXiv:2604.05324v2 Announce Type: replace Abstract: Statistical evaluation aims to estimate the generalization performance of a model using held-out i.i.d. test data sampled from the ground-truth dist

researcharxiv-cs-lg

Annotation-Informed Block-Sparse Bayesian Modeling for cis-Expression Prediction

arXiv:2606.00483v1 Announce Type: cross Abstract: Genotype-based cis-expression prediction depends on accurately modeling local regulatory architecture. We present block-sparse Bayesian sparse linear

local-aiarxiv-cs-lg

BlockGen: Flexible Blockwise Sequence Modeling with Hybrid Samplers

arXiv:2606.02241v1 Announce Type: new Abstract: Is the uniform-state diffusion framework a more powerful paradigm for discrete diffusion? Recent studies indicate that this may be the case. In combinat

researcharxiv-cs-lg

Characterization of Multi-Model Agentic AI Systems on General Tasks via Trace-Driven Simulation

model-releasesarxiv-cs-ai

arXiv:2606.01725v1 Announce Type: new Abstract: Agentic AI completes tasks through iterative planning, tool use, and reasoning based on observed outcomes. Despite its popularity, its system-level beha

Applications

Coupling Language Models with Physics-based Simulation for Synthesis of Inorganic Materials

arXiv:2606.00315v1 Announce Type: new Abstract: Modern generative machine learning (ML) models can propose novel inorganic crystalline materials with targeted properties; however, synthesis planning o

applicationsarxiv-cs-ai

Diffusion Models for Hyperspectral Image Analysis: A Comprehensive Review

arXiv:2505.11158v4 Announce Type: replace-cross Abstract: Hyperspectral image (HSI) analysis plays a critical role in remote sensing, agriculture, and environmental monitoring. However, traditional me

researcharxiv-cs-cv

Early Prediction of Liver Cirrhosis Up to Two Years in Advance: A Machine Learning Study Benchmarking Against the FIB-4 and APRI Scores

model-releasesarxiv-cs-lg

arXiv:2601.00175v2 Announce Type: replace Abstract: Objective: Develop and evaluate machine learning (ML) models for predicting incident liver cirrhosis (LC) one and two years prior to diagnosis using

Entropy Minimization without Model Collapse: Mitigating Prediction Bias in Medical Imaging

arXiv:2606.02339v1 Announce Type: cross Abstract: Entropy minimization (EM) is the dominant objective for test-time adaptation, yet its failure mode, model collapse, remains poorly understood. In this

safetyarxiv-cs-cv

Extreme Low-Bit Inference in Reasoning Models: Failure Modes and Targeted Recovery

arXiv:2606.02011v1 Announce Type: new Abstract: Large Reasoning Models (LRMs) rely on long reasoning traces, making inference expensive. While low-bit quantization reduces per-token decoding cost, we

FIRM: Federated In-client Regularized Multi-objective Alignment for Large Language Models

arXiv:2511.16992v3 Announce Type: replace Abstract: Aligning Large Language Models (LLMs) with human values often involves balancing multiple, conflicting objectives such as helpfulness and harmlessne

safetyarxiv-cs-lg

ForesightKV: Optimizing KV Cache Eviction for Reasoning Models by Learning Long-Term Contribution

arXiv:2602.03203v2 Announce Type: replace Abstract: Recently, large language models (LLMs) have shown remarkable reasoning abilities by producing long reasoning traces. However, as the sequence length

Hardware

FreqLite: A Lightweight Frequency-Decomposed Linear Model with Adaptive Reversible Normalization for Robust Long-Term Time-Series Forecasting

arXiv:2606.01339v1 Announce Type: cross Abstract: Long-term time-series forecasting needs models that are accurate yet efficient enough for commodity hardware. Lightweight linear forecasters are remar

hardwarearxiv-cs-ai

From Unfamiliar to Familiar: Detecting Pre-training Data via Gradient Deviations in Large Language Models

model-releasesarxiv-cs-cl

arXiv:2603.04828v2 Announce Type: replace Abstract: Pre-training data detection for LLMs is essential for addressing copyright concerns and mitigating benchmark contamination. Existing methods mainly

Heterogeneous Decentralized Diffusion Models

arXiv:2603.06741v2 Announce Type: replace-cross Abstract: Training frontier-scale diffusion models often requires substantial computational resources concentrated in tightly-coupled clusters, limiting

local-aiarxiv-cs-ai

Hyperbolic and Evidence-Prioritized Experts for Large Vision-Language Models

arXiv:2606.00275v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have demonstrated impressive performance on multimodal tasks through scaled architectures and extensive training.

Improving Visual Grounding in Remote Sensing via Cluster-Guided Refinement and Model Ensemble Voting

arXiv:2606.00556v1 Announce Type: new Abstract: Visual grounding aims to locate image regions that correspond to natural language descriptions and is a key component of interpretable vision systems. I

researcharxiv-cs-cv

KnowledgeBerg: Evaluating Systematic Knowledge Coverage and Compositional Reasoning in Large Language Models

model-releasesarxiv-cs-ai

arXiv:2604.17621v2 Announce Type: replace Abstract: Many real-world questions appear deceptively simple yet implicitly demand two capabilities: (i) systematic coverage of a bounded knowledge universe

Large-scale Uncertainty Quantification for Latent Variable Models Using Subsampling Markov Chain Monte Carlo

model-releasesarxiv-cs-lg

arXiv:2606.00309v1 Announce Type: new Abstract: Stochastic gradient Langevin dynamics combined with Gibbs updates (SGLD--Gibbs) provides a highly scalable approach to approximate Bayesian inference in

Applications

LLMSynthor: Macro-Aligned Micro-Records Synthesis with Large Language Models

arXiv:2505.14752v3 Announce Type: replace Abstract: Macro-aligned micro-records are crucial for credible simulations in social science and urban studies. For example, epidemic models are only reliable

applicationsarxiv-cs-lg

lmfaoooo at SemEval-2026 Task 1: Humor Is an Audience. Preference Modeling for Constrained Humor Generation

arXiv:2606.00022v1 Announce Type: cross Abstract: Humor generation remains difficult not only because producing fluent, novel jokes is hard, but because 'funny' is audience-dependent and supervision i

M^3 Scaling Law: Optimizing Multi-Epoch, Multi-Lingual, and Multi-Stage Training for Low-Resource Language Models

arXiv:2410.12325v2 Announce Type: replace Abstract: In this paper, we study a fundamental design problem in pretraining Large Language Models (LLMs) for low-resource language regimes. Existing works a

Mechanistic Diagnostics of Spatial Lexical Bias in Multimodal Large Language Model Spatial Reasoning

arXiv:2606.01914v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) remain unreliable on spatial multiple-choice questions, and their failures are often attributed to poorly atten

safetyarxiv-cs-cl

Meta-Black-Box Optimization with Ensemble Surrogate Modeling for Robustness-Accuracy Trade-off within SAEA

arXiv:2606.00862v1 Announce Type: cross Abstract: Surrogate-assisted evolutionary algorithms (SAEAs) have been widely used for expensive black-box optimization problems. However, their reliance on rig

safetyarxiv-cs-lg