Search: “models”

Compression as Adaptation: Implicit Visual Representation with Diffusion Foundation Models

arXiv:2603.07615v2 Announce Type: replace-cross Abstract: Modern visual generative models acquire rich visual knowledge through large-scale training, yet existing visual representations (such as pixel

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Do Large Language Models Plan Answer Positions? Position Bias in Multiple-Choice Question Generation

DGX agent

arXiv:2605.01846v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to generate multiple-choice questions (MCQs), where correct answers should ideally be uniformly distr

safetyarxiv-cs-cl

5 May 2026

Edge-Efficient Image Restoration: Transformer Distillation into State-Space Models

arXiv:2605.02794v1 Announce Type: new Abstract: We propose a modular framework for hybrid image restoration that integrates transformer and state-space model (SSM) blocks with a focus on improving run

Fuzzy Fingerprinting Encoder Pre-trained Language Models for Emotion Recognition in Conversations: Human Assessment and Validity Study

arXiv:2605.02665v1 Announce Type: new Abstract: In Emotion Recognition in Conversations (ERC), model decisions should align with nuanced human perception and ideally provide insights on the classifica

GEASS: Training-Free Caption Steering for Hallucination Mitigation in Vision-Language Models

arXiv:2605.01733v1 Announce Type: new Abstract: Vision-Language Models (VLMs) excel at grounded reasoning but remain prone to object hallucination. Recent work treats self-generated captions as a unif

GeoSAE: Geometric Prior-Guided Layer-Wise Sparse Autoencoder Annotation of Brain MRI Foundation Models

arXiv:2605.01829v1 Announce Type: new Abstract: Brain MRI foundation models learn rich representations of anatomy, but interpreting what clinical information they encode remains an open problem. Stand

tutorialsarxiv-cs-cv

H-Probes: Extracting Hierarchical Structures From Latent Representations of Language Models

arXiv:2605.00847v1 Announce Type: new Abstract: Representing and navigating hierarchy is a fundamental primitive of reasoning. Large language models have demonstrated proficiency in a wide variety of

applicationsarxiv-cs-cl

InfoLaw: Information Scaling Laws for Large Language Models with Quality-Weighted Mixture Data and Repetition

arXiv:2605.02364v1 Announce Type: new Abstract: Upweighting high-quality data in LLM pretraining often improves performance, but in datalimited regimes, especially under overtraining, stronger upweigh

LLM-Foraging: Large Language Models for Decentralized Swarm Robot Foraging

model-releasesarxiv-cs-ro

arXiv:2605.01461v1 Announce Type: new Abstract: Swarm foraging algorithms, such as the central-place foraging algorithm (CPFA), typically rely on offline parameter optimization using genetic algorithm

Noise is All You Need: Solving Linear Inverse Problems by Noise Combination Sampling with Diffusion Models

arXiv:2510.23633v2 Announce Type: replace-cross Abstract: Pretrained diffusion models have demonstrated strong capabilities in zero-shot inverse problem solving by incorporating observation informatio

PubMed-Ophtha: An open resource for training ophthalmology vision-language models on scientific literature

arXiv:2605.02720v1 Announce Type: cross Abstract: Vision-language models hold considerable promise for ophthalmology, but their development depends on large-scale, high-quality image-text datasets tha

Spatiotemporal Hidden-State Dynamics as a Signature of Internal Reasoning in Large Language Models

arXiv:2605.01853v1 Announce Type: new Abstract: Large reasoning models (LRMs) generate extended solutions, yet it remains unclear whether these traces reflect substantive internal computation or merel

SpectraDINO: Bridging the Spectral Gap in Vision Foundation Models via Lightweight Adapters

arXiv:2605.02258v1 Announce Type: new Abstract: Vision Foundation Models (VFMs) pretrained on large-scale RGB data have demonstrated remarkable representation quality, yet their applicability to multi

safetyarxiv-cs-cv

Spoken Language Identification with Pre-trained Models and Margin Loss

model-releasesarxiv-cs-cl

arXiv:2605.01905v1 Announce Type: cross Abstract: For the speaker-controlled spoken language identification task proposed in the TidyLang Challenge 2026, this paper proposes a language identification

StressEval: Failure-Driven Dynamic Benchmarking for Knowledge-Intensive Reasoning in Large Language Models

model-releasesarxiv-cs-cl

arXiv:2605.01939v1 Announce Type: new Abstract: Static benchmarks for LLMs are increasingly compromised by contamination and overfitting especially on knowledge intensive reasoning tasks While recent

Stylistic Attribute Control in Latent Diffusion Models

arXiv:2605.02583v1 Announce Type: new Abstract: Text-to-image diffusion models have revolutionized image synthesis and editing, but precise control over stylistic attributes remains a challenge, often

It's Never Too Late: Noise Optimization for Collapse Recovery in Trained Diffusion Models

arXiv:2601.00090v2 Announce Type: replace Abstract: Contemporary text-to-image models exhibit a surprising degree of mode collapse, as can be seen when sampling several images given the same text prom

4 May 2026

When Do Diffusion Models learn to Generate Multiple Objects?

arXiv:2605.00273v1 Announce Type: new Abstract: Text-to-image diffusion models achieve impressive visual fidelity, yet they remain unreliable in multi-object generation. Despite extensive empirical ev

tutorialsarxiv-cs-cv

4 May 2026

Cross-Lingual Sentiment Misalignment: Auditing Multilingual Language Models for Inversion Risk, Dialectal Representation, and Affective Stability

arXiv:2602.17469v2 Announce Type: replace Abstract: Recent advances in multilingual representation learning aim to bridge the performance gap between high- and low-resource languages, yet their abilit

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Agents

From Context to Skills: Can Language Models Learn from Context Skillfully?

arXiv:2604.27660v1 Announce Type: new Abstract: Many real-world tasks require language models (LMs) to reason over complex contexts that exceed their parametric knowledge. This calls for context learn

agentsarxiv-cs-ai

Green Physics-Informed Machine Learning Models For Structural Health Monitoring

arXiv:2604.27638v1 Announce Type: new Abstract: Machine learning continues to emerge as an important tool to be utilised within structural engineering and structural health monitoring, due to its abil

applicationsarxiv-cs-lg

Improving Calibration in Test-Time Prompt Tuning for Vision-Language Models via Data-Free Flatness-Aware Prompt Pretraining

arXiv:2604.27715v1 Announce Type: new Abstract: Test-time prompt tuning (TPT) has emerged as a promising technique for enhancing the adaptability of vision-language models by optimizing textual prompt

applicationsarxiv-cs-cv

Mitigating Selection Bias in Large Language Models via Permutation-Aware GRPO

arXiv:2603.21016v2 Announce Type: replace-cross Abstract: Large language models (LLMs) used for multiple-choice and pairwise evaluation tasks often exhibit selection bias due to non-semantic factors l

safetyarxiv-cs-ai

Static Program Slicing Using Language Models With Dataflow-Aware Pretraining and Constrained Decoding

arXiv:2604.26961v1 Announce Type: cross Abstract: Static program slicing is a fundamental software engineering technique for isolating code relevant to specific variables. While recent learning-based

researcharxiv-cs-ai

The Effects of Visual Priming on Cooperative Behavior in Vision-Language Models

arXiv:2604.27953v1 Announce Type: new Abstract: As Vision-Language Models (VLMs) become increasingly integrated into decision-making systems, it is essential to understand how visual inputs influence

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Agents

Understanding Adversarial Transferability in Vision-Language Models for Autonomous Driving: A Cross-Architecture Analysis

arXiv:2604.27414v1 Announce Type: new Abstract: Vision-language models (VLMs) are increasingly used in autonomous driving because they combine visual perception with language-based reasoning, supporti

agentsarxiv-cs-cv

VIPaint: Image Inpainting with Pre-Trained Diffusion Models via Variational Inference

arXiv:2411.18929v2 Announce Type: replace-cross Abstract: Diffusion probabilistic models learn to remove noise added during training, generating novel data (e.g., images) from Gaussian noise through s

tutorialsarxiv-cs-ai

Analysing Lightweight Large Language Models for Biomedical Named Entity Recognition on Diverse Ouput Formats

arXiv:2604.25920v1 Announce Type: cross Abstract: Despite their strong linguistic capabilities, Large Language Models (LLMs) are computationally demanding and require substantial resources for fine-tu

applicationsarxiv-cs-ai

Beyond Fixed Formulas: Data-Driven Linear Predictor for Efficient Diffusion Models

model-releasesarxiv-cs-cv

arXiv:2604.26365v1 Announce Type: new Abstract: To address the high sampling cost of Diffusion Transformers (DiTs), feature caching offers a training-free acceleration method. However, existing method

Agents

RetroMotion: Retrocausal Motion Forecasting Models are Instructable

arXiv:2505.20414v2 Announce Type: replace-cross Abstract: Motion forecasts of road users (i.e., agents) vary in complexity depending on the number of agents, scene constraints, and interactions. In pa

agentsarxiv-cs-ai

Local Ai

TLPO: Token-Level Policy Optimization for Mitigating Language Confusion in Large Language Models

arXiv:2604.26553v1 Announce Type: cross Abstract: Large language models (LLMs) demonstrate strong multilingual capabilities, yet often fail to consistently generate responses in the intended language,

local-aiarxiv-cs-ai

WebAggregator: Enhancing Compositional Reasoning Capabilities of Deep Research Agent Foundation Models

model-releasesarxiv-cs-cl

arXiv:2510.14438v2 Announce Type: replace Abstract: The hallmark of Deep Research agents lies in compositional reasoning, the capacity to aggregate distributed, heterogeneous information into coherent

Liquid Neural Network Models for Natural Gas Spot Price Time-Series Forecasting

model-releasesarxiv-cs-lg

arXiv:2604.24788v1 Announce Type: new Abstract: Natural gas is undoubtedly an essential component of the global energy system. Accurate short-term forecasting of natural gas price is challenging due t

29 Apr 2026

VibeToken: Scaling 1D Image Tokenizers and Autoregressive Models for Dynamic Resolution Generations

arXiv:2604.24885v1 Announce Type: new Abstract: We introduce an efficient, resolution-agnostic autoregressive (AR) image synthesis approach that generalizes to arbitrary resolutions and aspect ratios,

applicationsarxiv-cs-cv

29 Apr 2026

BEAR: Towards Beam-Search-Aware Optimization for Recommendation with Large Language Models

arXiv:2601.22925v2 Announce Type: replace-cross Abstract: Recent years have seen a rapid surge in research leveraging Large Language Models (LLMs) for recommendation. These methods typically employ su

applicationsarxiv-cs-ai

CUB: Benchmarking Context Utilisation Techniques for Language Models

arXiv:2505.16518v3 Announce Type: replace-cross Abstract: Incorporating external knowledge is crucial for knowledge-intensive tasks, such as question answering and fact checking. However, language mod

Forecasting Commencing Enrolments Under Data Sparsity: A Zero-Shot Time Series Foundation Models Framework for Higher Education Planning

arXiv:2602.12120v3 Announce Type: replace Abstract: Effective resource allocation in higher education depends on reliable enrolment forecasts, yet institutional planners frequently face data series di

HardFlow: Hard-Constrained Sampling for Flow-Matching Models via Trajectory Optimization

arXiv:2511.08425v3 Announce Type: replace Abstract: Diffusion and flow-matching have emerged as powerful methodologies for generative modeling, with remarkable success in capturing complex data distri

researcharxiv-cs-lg

High-accuracy sampling for diffusion models and log-concave distributions

arXiv:2602.01338v2 Announce Type: replace Abstract: We present algorithms for diffusion model sampling which obtain elta-error in polylog(1/elta) steps, given access to widetilde O(elta)-accurate scor

researcharxiv-cs-lg

Investigating the Representation of Backchannels and Fillers in Fine-tuned Language Models

arXiv:2509.20237v2 Announce Type: replace Abstract: Backchannels and fillers are important linguistic expressions in dialogue, but often treated as 'noise' to be bypassed in modern transformer-based l

tutorialsarxiv-cs-cl

MirrorMark: A Distortion-Free Multi-Bit Watermark for Large Language Models

arXiv:2601.22246v2 Announce Type: replace-cross Abstract: As large language models (LLMs) become integral to applications such as question answering and content creation, reliable content attribution

researcharxiv-cs-ai

Planning Under Observation Mismatch for Traffic Signal Control via Adaptive Modular World Models

arXiv:2501.02548v2 Announce Type: replace-cross Abstract: Deploying learned decision-making systems often requires transferring to new sites where the sensing pipeline differs. In such cases, observat

researcharxiv-cs-ai

Probing Visual Planning in Image Editing Models

arXiv:2604.22868v1 Announce Type: cross Abstract: Visual planning represents a crucial facet of human intelligence, especially in tasks that require complex spatial reasoning and navigation. Yet, in m

tutorialsarxiv-cs-ai

Quantifying and Improving the Robustness of Retrieval-Augmented Language Models Against Spurious Features in Grounding Data

arXiv:2503.05587v3 Announce Type: replace-cross Abstract: Robustness has become a critical attribute for the deployment of RAG systems in real-world applications. Existing research focuses on robustne

applicationsarxiv-cs-ai

Strategic Bidding in 6G Spectrum Auctions with Large Language Models

arXiv:2604.24156v1 Announce Type: cross Abstract: Efficient and fair spectrum allocation is a central challenge in 6G networks, where massive connectivity and heterogeneous services continuously compe

Universal approximation property of Banach space-valued random feature models including random neural networks

arXiv:2312.08410v5 Announce Type: replace Abstract: We introduce a Banach space-valued extension of random feature learning, a data-driven supervised machine learning technique for large-scale kernel

tutorialsarxiv-cs-lg

V-GRPO: Online Reinforcement Learning for Denoising Generative Models Is Easier than You Think

arXiv:2604.23380v1 Announce Type: cross Abstract: Aligning denoising generative models with human preferences or verifiable rewards remains a key challenge. While policy-gradient online reinforcement

safetyarxiv-cs-cv

xOffense: An Autonomous Multi-Agent Framework for Penetration Testing with Domain-Adapted Large Language Models

arXiv:2509.13021v2 Announce Type: replace-cross Abstract: This work introduces xOffense, an AI-driven, multi-agent penetration testing framework that shifts the process from labor-intensive, expert-dr