Search: “models”

A New Semisupervised Technique for Polarity Analysis using Masked Language Models

arXiv:2604.26230v1 Announce Type: new Abstract: I developed a new version of Latent Semantic Scaling (LSS) employing word2vec as a masked language model. Unlike original spatial models, it assigns pol

researcharxiv-cs-cl

Affective Flow Language Model for Emotional Support Conversation

DGX agent

arXiv:2602.08826v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have been widely applied to emotional support conversation (ESC). However, complex multi-turn support remains cha

model-releasesarxiv-cs-ai

30 Apr 2026

Graph Property Inference in Small Language Models: Effects of Representation and Reasoning Strategy

arXiv:2603.06635v2 Announce Type: replace Abstract: Recent progress in language modeling has expanded the range of tasks that can be approached through natural language interfaces, including problems

L2RU: a Structured State Space Model with prescribed L2-bound

model-releasesarxiv-cs-lg

arXiv:2503.23818v3 Announce Type: replace-cross Abstract: Structured state-space models (SSMs) have recently emerged as a powerful architecture at the intersection of machine learning and control, fea

ReLoop: Structured Modeling and Behavioral Verification for Reliable LLM-Based Optimization

arXiv:2602.15983v2 Announce Type: replace-cross Abstract: Large language models (LLMs) can translate natural language into optimization code, but silent failures pose a critical risk: code that execut

Industry

The @huggingface CLI now includes a command to find the best open-source models for a given dataset! This is going to be very useful for age…

Hugging Face introduced a new CLI command that helps users identify the best open-source models suited for their specific datasets, streamlining the model selection process. This feature reduces the m

industryclem-delangue--x

Tutorials

Towards Redundancy Reduction in Diffusion Models for Efficient Video Super-Resolution

arXiv:2509.23980v2 Announce Type: replace Abstract: Diffusion models have recently shown promising results for video super-resolution (VSR). However, directly adapting generative diffusion models to V

tutorialsarxiv-cs-cv

Adaptive Meta-Learning Stochastic Gradient Hamiltonian Monte Carlo Simulation for Bayesian Updating of Structural Dynamic Models

arXiv:2604.25710v1 Announce Type: cross Abstract: In the last few decades, Markov chain Monte Carlo (MCMC) methods have been widely applied to Bayesian updating of structural dynamic models in the fie

Agents

BEVal: A Cross-dataset Evaluation Study of BEV Segmentation Models for Autonomous Driving

arXiv:2408.16322v4 Announce Type: replace Abstract: Current research in semantic bird's-eye view segmentation for autonomous driving focuses solely on optimizing neural network models using a single d

agentsarxiv-cs-cv

Beyond I'm Sorry, I Can't: Dissecting Large Language Model Refusal

arXiv:2509.09708v3 Announce Type: replace Abstract: Refusal on harmful prompts is a key safety behaviour in instruction-tuned large language models (LLMs), yet the internal causes of this behaviour re

Tutorials

Generative diffusion models for spatiotemporal influenza forecasting

arXiv:2604.24913v1 Announce Type: new Abstract: Forecasting infectious disease incidence can provide important information to guide public health planning, yet is difficult because epidemic dynamics a

tutorialsarxiv-cs-lg

Images Amplify Misinformation Sharing in Vision-Language Models

arXiv:2505.13302v2 Announce Type: replace Abstract: As language and vision-language models (VLMs) become central to information access and online interaction, concerns grow about their potential to am

Is This Just Fantasy? Language Model Representations Reflect Human Judgments of Event Plausibility

arXiv:2507.12553v3 Announce Type: replace Abstract: Language models (LMs) are used for a diverse range of tasks, from question answering to writing fantastical stories. In order to reliably accomplish

Large Language Models Explore by Latent Distilling

arXiv:2604.24927v1 Announce Type: new Abstract: Generating diverse responses is crucial for test-time scaling of large language models (LLMs), yet standard stochastic sampling mostly yields surface-le

Applications

LegalMidm: Use-Case-Driven Legal Domain Specialization for Korean Large Language Model

arXiv:2604.25297v1 Announce Type: new Abstract: In recent years, the rapid proliferation of open-source large language models (LLMs) has spurred efforts to turn general-purpose models into domain spec

applicationsarxiv-cs-cl

Nautile-370M: Spectral Memory Meets Attention in a Small Reasoning Model

model-releasesarxiv-cs-lg

arXiv:2604.24809v1 Announce Type: new Abstract: We present Nautile-370M, a 371-million-parameter small language model designed for efficient reasoning under strict parameter and inference budgets. Nau

QCalEval: Benchmarking Vision-Language Models for Quantum Calibration Plot Understanding

model-releasesarxiv-cs-cv

arXiv:2604.25884v1 Announce Type: cross Abstract: Quantum computing calibration depends on interpreting experimental data, and calibration plots provide the most universal human-readable representatio

Walking Through Uncertainty: An Empirical Study of Uncertainty Estimation for Audio-Aware Large Language Models

arXiv:2604.25591v1 Announce Type: cross Abstract: Recent audio-aware large language models (ALLMs) have demonstrated strong capabilities across diverse audio understanding and reasoning tasks, but the

Local Ai

300,000 AI builders have already added their hardware to HF to instantly see what model they can run locally. To do so, go to https://huggin…

300,000 AI builders have already added their hardware to HF to instantly see what model they can run locally. To do so, go to https://huggingface.co/settings/local-apps and add your hardware specs. Yo

local-aiclem-delangue--x

A satellite foundation model for improved wealth monitoring

model-releasesarxiv-cs-cv

arXiv:2604.23166v1 Announce Type: cross Abstract: Poverty statistics guide social policy, but in many low- and middle-income countries, censuses and household surveys that collect these data are costl

Agents

A Systematic Approach for Large Language Models Debugging

arXiv:2604.23027v1 Announce Type: new Abstract: Large language models (LLMs) have become central to modern AI workflows, powering applications from open-ended text generation to complex agent-based re

agentsarxiv-cs-ai

An empirical evaluation of the risks of AI model updates using clinical data: stability, arbitrariness, and fairness

arXiv:2604.23954v1 Announce Type: new Abstract: Artificial Intelligence and Machine Learning (AI/ML) models used in clinical settings are increasingly deployed to support clinical decision-making. How

AP-BMM: Approximating Capability-Efficiency Pareto Sets of LLMs via Asynchronous Prior-guided Bayesian Model Merging

arXiv:2512.09972v5 Announce Type: replace-cross Abstract: Navigating the capability--efficiency trade-off in Large Language Models (LLMs) requires approximating a high-quality Pareto set. Existing mod

researcharxiv-cs-cl

Automating Categorization of Scientific Texts with In-Context Learning and Prompt-Chaining in Large Language Models

arXiv:2604.23430v1 Announce Type: cross Abstract: The relentless expansion of scientific literature presents significant challenges for navigation and knowledge discovery. Within Research Information

AutoRISE: Agent-Driven Strategy Evolution for Red-Teaming Large Language Models

arXiv:2604.22871v1 Announce Type: cross Abstract: Automated red-teaming methods for large language models typically optimize attack prompts within a fixed, human-designed strategy, leaving the attack

Beyond Cross-Modal Alignment: Measuring and Leveraging Modality Gap in Vision-Language Models

arXiv:2502.14888v4 Announce Type: replace-cross Abstract: The success of vision-language models is primarily attributed to effective alignment across modalities such as vision and language. However, m

CoreGuard: Safeguarding Foundational Capabilities of LLMs Against Model Stealing in Edge Deployment

arXiv:2410.13903v3 Announce Type: replace-cross Abstract: Proprietary large language models (LLMs) exhibit strong generalization capabilities across diverse tasks and are increasingly deployed on edge

Credal Concept Bottleneck Models for Epistemic-Aleatoric Uncertainty Decomposition

arXiv:2604.24170v1 Announce Type: new Abstract: Concept Bottleneck Models (CBMs) predict through human-interpretable concepts, but they typically output point concept probabilities that conflate epist

Detecting and Evaluating Medical Hallucinations in Large Vision Language Models

model-releasesarxiv-cs-cv

arXiv:2406.10185v2 Announce Type: replace Abstract: Large Vision Language Models (LVLMs) are increasingly integral to healthcare applications, including medical visual question answering and imaging r

DPRM: A Plug-in Doob h transform-induced Token-Ordering Module for Diffusion Language Models

arXiv:2604.24357v1 Announce Type: cross Abstract: Diffusion language models generate without a fixed left-to-right order, making token ordering a central algorithmic choice: which tokens should be rev

DRP: Distilled Reasoning Pruning with Skill-aware Step Decomposition for Efficient Large Reasoning Models

arXiv:2505.13975v4 Announce Type: replace Abstract: While Large Reasoning Models (LRMs) have demonstrated success in complex reasoning tasks through long chain-of-thought (CoT) reasoning, their infere

researcharxiv-cs-cl

Evaluating Language Models' Evaluations of Games

arXiv:2510.10930v2 Announce Type: replace-cross Abstract: Reasoning is not just about solving problems -- it is also about evaluating which problems are worth solving at all. Evaluations of artificial

Exploring the Secondary Risks of Large Language Models

arXiv:2506.12382v5 Announce Type: replace-cross Abstract: Ensuring the safety and alignment of Large Language Models is a significant challenge with their growing integration into critical application

GA2-CLIP: Generic Attribute Anchor for Efficient Prompt Tuningin Video-Language Models

arXiv:2511.22125v2 Announce Type: replace Abstract: Visual and textual soft prompt tuning can effectively improve the adaptability of Vision-Language Models (VLMs) in downstream tasks. However, fine-t

researcharxiv-cs-cv

Game-Time: Evaluating Temporal Dynamics in Spoken Language Models

arXiv:2509.26388v3 Announce Type: replace-cross Abstract: Conversational Spoken Language Models (SLMs) are emerging as a promising paradigm for real-time speech interaction. However, their capacity of

GeoEdit: Local Frames for Fast, Training-Free On-Manifold Editing in Diffusion Models

arXiv:2604.24238v1 Announce Type: new Abstract: Diffusion models are a leading paradigm for data generation, but training-free editing typically re-runs the full denoising trajectory for every edit st

Geometry Preserving Loss Functions Promote Improved Adaptation of Blackbox Generative Model

arXiv:2604.23888v1 Announce Type: cross Abstract: Adaptation of blackbox generative models has been widely studied recently through the exploration of several methods including generator fine-tuning,

GWT: Scalable Optimizer State Compression for Large Language Model Training

arXiv:2501.07237v5 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated exceptional capabilities across diverse natural language processing benchmarks. However, the es

Integrative neurocybernetic modeling in the era of large-scale neuroscience

arXiv:2604.23903v1 Announce Type: cross Abstract: Large-scale neuroscience is generating rich datasets across animals, brain areas and behavioral contexts, yet our modeling efforts remains fragmented

LongFlow: Efficient KV Cache Compression for Reasoning Models

arXiv:2603.11504v2 Announce Type: replace-cross Abstract: Recent reasoning models such as OpenAI-o1 and DeepSeek-R1 have shown strong performance on complex tasks including mathematical reasoning and

NVIDIA Nemotron 3 Nano Omni Powers Multimodal Agent Reasoning in a Single Efficient Open Model

model-releasesnvidia-developer

NVIDIA Nemotron 3 Nano Omni is a 30B hybrid mixture-of-experts model that brings multimodal perception and reasoning into a single system, natively supporting text, image, video, and audio inputs whil

Predicting one-year clinical instability and mortality in heart failure patients using sequence modeling

arXiv:2511.16839v3 Announce Type: replace-cross Abstract: Heart failure (HF) discharge planning depends on identifying patients at risk of deterioration or death, yet accurate prediction from routinel

Predicting Wind Loads on Container Ships in Harbor Environments through Multi-Fidelity Modeling

model-releasesarxiv-cs-lg

arXiv:2604.22882v1 Announce Type: new Abstract: Modern container ships face higher wind loads due to increased windage areas, making accurate predictions of wind loads essential for mooring design. Ex

RegMean++: Enhancing Effectiveness and Generalization of Regression Mean for Model Merging

arXiv:2508.03121v3 Announce Type: replace Abstract: Regression Mean (RegMean), an approach that formulates model merging as a linear regression problem, aims to find the optimal weights for each linea

SketchVLM: Vision language models can annotate images to explain thoughts and guide users

arXiv:2604.22875v1 Announce Type: cross Abstract: When answering questions about images, humans naturally point, label, and draw to explain their reasoning. In contrast, modern vision-language models

Structural Pruning of Large Vision Language Models: A Comprehensive Study on Pruning Dynamics, Recovery, and Data Efficiency

arXiv:2604.24380v1 Announce Type: new Abstract: While Large Vision Language Models (LVLMs) demonstrate impressive capabilities, their substantial computational and memory requirements pose deployment

The Kerimov-Alekberli Model: An Information-Geometric Framework for Real-Time System Stability

arXiv:2604.24083v1 Announce Type: new Abstract: This study introduces the Kerimov-Alekberli model, a novel information-geometric framework that redefines AI safety by formally linking non-equilibrium

What Drives Compositional Generalization? The Importance of Continuous Training Objectives in Visual Generative Models

arXiv:2510.03075v3 Announce Type: replace-cross Abstract: Compositional generalization, the ability to generate novel combinations of known concepts, is a key ingredient for visual generative models.