Search: “models”

Lotus-2: Advancing Geometric Dense Prediction with Powerful Image Generative Model

arXiv:2512.01030v3 Announce Type: replace Abstract: Recovering pixel-wise geometric properties from a single image is fundamentally ill-posed due to appearance ambiguity and non-injective mappings bet

researcharxiv-cs-cv

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Research

Mitigating 3D Prostate Biparametric MRI Data Scarcity through Domain Adaptation using Locally-Trained Latent Diffusion Models for Prostate Cancer Detection

DGX agent

arXiv:2507.06384v2 Announce Type: replace-cross Abstract: Objective: Latent diffusion models (LDMs) could mitigate data scarcity challenges affecting machine learning development for medical image int

researcharxiv-cs-cv

19 May 2026

MoleCode unlocks structural intelligence in large language models

arXiv:2605.16480v1 Announce Type: cross Abstract: Molecules are graphs, but large language models~(LLMs) are usually asked to reason about them through linear strings. The most popular molecular repre

On the Adversarial Robustness of Large Vision-Language Models under Visual Token Compression

arXiv:2601.21531v2 Announce Type: replace-cross Abstract: Visual token compression is widely used to accelerate large vision-language models (LVLMs) by pruning or merging visual tokens, yet its advers

One Model to Translate Them All: Universal Any-to-Any Translation for Heterogeneous Collaborative Perception

arXiv:2605.17907v1 Announce Type: cross Abstract: By sharing intermediate features, collaborative perception extends each agent's sensing beyond standalone limits, but real-world feature modality hete

Peak-Detector: Explainable Peak Detection via Instruction-Tuned Large Language Models in Physiological Sign

arXiv:2605.16452v1 Announce Type: cross Abstract: Accurate peak detection across diverse cardiac physiological signals, including the Electrocardiogram (ECG), Photoplethysmogram (PPG), Ballistocardiog

Applications

Perovskite-R1: a domain-specialized large language model for intelligent discovery of precursor additives and experimental design

arXiv:2507.16307v2 Announce Type: replace-cross Abstract: Perovskite solar cells (PSCs) have rapidly emerged as a leading contender in next-generation photovoltaic technologies, owing to their excepti

applicationsarxiv-cs-ai

PersonaArena: Dynamic Simulation for Evaluating and Enhancing Persona-Level Role-Playing in Large Language Models

arXiv:2605.17044v1 Announce Type: new Abstract: Large language models (LLMs) increasingly serve as interactive social agents, yet their ability to maintain coherent and authentic persona-level role-pl

Hardware

Pocket Foundation Models: Distilling TFMs into CPU-Ready Gradient-Boosted Trees

arXiv:2605.18654v1 Announce Type: cross Abstract: A fraud scorer needs to answer in under 2 ms. The best tabular foundation models (TFMs) take 151-1,275 ms on GPU. We close this gap by distilling the

hardwarearxiv-cs-ai

SG-CADVLM: A Context-Aware Decoding Powered Vision Language Model for Safety-Critical Scenario Generation

arXiv:2601.18442v3 Announce Type: replace Abstract: Autonomous Vehicle (AV) requires rigorous testing in safety-critical scenarios for safety validation, yet its validation is hindered by the high cos

safetyarxiv-cs-ro

Sketch Then Paint: Hierarchical Reinforcement Learning for Diffusion Multi-Modal Large Language Models

arXiv:2605.16842v1 Announce Type: new Abstract: Diffusion Multi-Modal Large Language Models (dMLLMs) are powerful for image generation, but optimizing them through reinforcement learning (RL) remains

local-aiarxiv-cs-ai

SparseSAM: Structured Sparsification of Activations in Segment Anything Models

arXiv:2605.17633v1 Announce Type: cross Abstract: The Segment Anything Model (SAM) achieves strong open-vocabulary segmentation, but its ViT-based image encoders dominate inference latency and memory.

StructLens: A Structural Lens for Language Models via Maximum Spanning Trees

arXiv:2603.03328v2 Announce Type: replace-cross Abstract: Language exhibits inherent structures, a property that explains both language acquisition and language change. Given this characteristic, we e

The Lattice Representation Hypothesis of Large Language Models

arXiv:2603.01227v2 Announce Type: replace Abstract: We propose the Lattice Representation Hypothesis of large language models: a symbolic backbone that grounds conceptual hierarchies and logical opera

Towards Generalized Image Manipulation Localization via Score-based Model

arXiv:2605.16879v1 Announce Type: new Abstract: With the rapid evolution of synthetic media, Image Manipulation Localization (IML) has emerged as a critical component in multimedia forensics for ensur

local-aiarxiv-cs-cv

Trajectory-Aware Adaptive Inference in Object Detection Models

arXiv:2605.16397v1 Announce Type: cross Abstract: The increasing integration of sensors in autonomous maritime navigation has led to large-scale multimodal datasets, raising challenges in achieving ef

UB-SMoE: Universally Balanced Sparse Mixture-of-Experts for Resource-adaptive Federated Fine-tuning of Foundation Models

arXiv:2605.16690v1 Announce Type: new Abstract: Heterogeneous LoRA-rank methods address system heterogeneity in federated fine-tuning of foundation models by assigning client-specific ranks based on c

VLM-AutoDrive: Post-Training Vision-Language Models for Safety-Critical Autonomous Driving Events

arXiv:2603.18178v2 Announce Type: replace-cross Abstract: The rapid growth of ego-centric dashcam footage presents a major challenge for detecting safety-critical events such as collisions and near-co

Wasserstein bounds for denoising diffusion probabilistic models via the Follmer process

arXiv:2605.18069v1 Announce Type: cross Abstract: This paper studies sampling error bounds for denoising diffusion probabilistic models (DDPMs) in the 2-Wasserstein distance. Our contributions are thr

What Matters for Grocery Product Retrieval with Open Source Vision Language Models

arXiv:2605.18029v1 Announce Type: new Abstract: Multimodal product retrieval (MPR) underpins checkout-free retail and automated inventory systems, yet it demands fine-grained SKU discrimination that s

researcharxiv-cs-cv

When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited

arXiv:2605.17017v1 Announce Type: cross Abstract: Behavior Foundation Models (BFMs) enable scalable imitation learning (IL) by pretraining task-agnostic representations that can be rapidly adapted to

Attribute-Grounded Selective Reasoning for Artwork Emotion Understanding with Multimodal Large Language Models

arXiv:2605.15755v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) can produce fluent artwork emotion explanations, but they often suffer from attribute flooding: they enumerate

agentsarxiv-cs-cv

BPDQ: Bit-Plane Decomposition Quantization on a Variable Grid for Large Language Models

arXiv:2602.04163v2 Announce Type: replace Abstract: Large language model inference is often bounded by memory footprint and bandwidth in resource-constrained deployments, making quantization fundament

Tutorials

Constrained latent state modeling: A unifying perspective on representation learning under competing constraints

arXiv:2605.15995v1 Announce Type: cross Abstract: Learning latent representations from complex data is central to modern machine learning, spanning temporal, multimodal, and partially observed systems

tutorialsarxiv-cs-ai

DebiasRAG: A Tuning-Free Path to Fair Generation in Large Language Models through Retrieval-Augmented Generation

arXiv:2605.16113v1 Announce Type: cross Abstract: Large language models (LLMs) have achieved unprecedented success due to their exceptional generative capabilities. However, because they depend on kno

Differentiable Mixture-of-Agents Incentivizes Swarm Intelligence of Large Language Models

arXiv:2605.15706v1 Announce Type: new Abstract: Recent advances in Large Language Models (LLMs) have catalyzed the development of multi-agent systems (MAS) for complex reasoning tasks. However, existi

agentsarxiv-cs-lg

Do Less, Achieve More: Do We Need Every-Step Optimization for RL Fine-tuning of Diffusion Models?

arXiv:2605.15855v1 Announce Type: new Abstract: Despite strong image-generation performance, diffusion models' reconstruction objectives limit alignment with human preferences. RL enables such alignme

safetyarxiv-cs-cv

Embedding-perturbed Exploration Preference Optimization for Flow Models

arXiv:2605.15803v1 Announce Type: new Abstract: Recent advancements have established Reinforcement Learning (RL) as a pivotal paradigm for aligning generative models with human intent. However, group-

safetyarxiv-cs-cv

From Model Design to Organizational Design: Complexity Redistribution and Trade-Offs in Generative AI

arXiv:2506.22440v2 Announce Type: replace-cross Abstract: This paper introduces the Generality-Accuracy-Simplicity (GAS) framework to analyze how large language models (LLMs) are reshaping organizatio

safetyarxiv-cs-lg

Intrinsic Wasserstein Rates for Score-Based Generative Models on Smooth Manifolds

arXiv:2605.15822v1 Announce Type: new Abstract: Score-based generative models are trained in high-dimensional ambient spaces, yet many data distributions are supported on low-dimensional nonlinear str

Modeling Music as a Time-Frequency Image: A 2D Tokenizer for Music Generation

arXiv:2605.15831v1 Announce Type: cross Abstract: Autoregressive music generation depends strongly on the audio tokenizer. Existing high-fidelity codecs often use residual multi-codebook quantization,

Multi-Level Contextual Token Relation Modeling for Machine-Generated Text Detection

arXiv:2605.16107v1 Announce Type: new Abstract: Machine-generated texts (MGTs) pose risks such as disinformation and phishing, underscoring the need for reliable detection. Metric-based methods, which

local-aiarxiv-cs-cl

Solvita: Enhancing Large Language Models for Competitive Programming via Agentic Evolution

arXiv:2605.15301v1 Announce Type: new Abstract: Large language models (LLMs) still struggle with the rigorous reasoning demands of hard competitive programming. While recent multi-agent frameworks att

Beyond What to Select: A Plug-and-play Oscillatory Data-Volume Scheduling for Efficient Model Training

arXiv:2605.14773v1 Announce Type: cross Abstract: Data selection accelerates training by identifying representative training data while preserving model performance. However, existing methods mainly f

Concurrency without Model Changes: Future-based Asynchronous Function Calling for LLMs

arXiv:2605.15077v1 Announce Type: cross Abstract: Function calling, also known as tool use, is a core capability of modern LLM agents but is typically constrained by synchronous execution semantics. U

Applications

DT-Transformer: A Foundation Model for Disease Trajectory Prediction on a Real-world Health System

arXiv:2605.14227v1 Announce Type: cross Abstract: Accurate disease trajectory prediction is critical for early intervention, resource allocation, and improving long-term outcomes. While electronic hea

applicationsarxiv-cs-cl

Exploring Geographic Relative Space in Large Language Models through Activation Patching

arXiv:2605.14535v1 Announce Type: new Abstract: The increased use of Large Language Models (LLMs) in geography raises substantial questions about the safety of integrating these tools across a wide ra

safetyarxiv-cs-lg

Graph of States: Solving Abductive Tasks with Large Language Models

arXiv:2603.21250v2 Announce Type: replace Abstract: Logical reasoning encompasses deduction, induction, and abduction. However, while Large Language Models (LLMs) have effectively mastered the former

Image Restoration via Diffusion Models with Dynamic Resolution

arXiv:2605.14267v1 Announce Type: cross Abstract: Diffusion models (DMs) have exhibited remarkable efficacy in various image restoration tasks. However, existing approaches typically operate within th

Multi-scale Coarse-to-fine Modeling for Test-time Human Motion Control

arXiv:2605.14935v1 Announce Type: new Abstract: We present MSCoT, a multi-scale, coarse-to-fine model for test-time human motion synthesis and control. Unlike recent approaches that rely on multiple i

safetyarxiv-cs-cv

Applications

MultiMat: Multimodal Program Synthesis for Procedural Materials using Large Multimodal Models

arXiv:2509.22151v3 Announce Type: replace Abstract: Material node graphs are programs that generate the 2D channels of procedural materials, including geometry such as roughness and displacement maps,

applicationsarxiv-cs-cv

SeaVis: Modeling and Control of a Remotely Operated Towed Vehicle for Seabed Visualization and Mapping

arXiv:2605.14683v1 Announce Type: new Abstract: High-resolution seafloor mapping necessitates stable and precise positioning for underwater robots. This paper introduces a novel mathematical model for

researcharxiv-cs-ro

TRIO: Token Reduction via Inference-Objective Guidance for Efficient Vision-Language Models

arXiv:2602.04657v3 Announce Type: replace Abstract: Recently, reducing redundant visual tokens in vision-language models (VLMs) to accelerate VLM inference has emerged as a hot topic. However, most ex

local-aiarxiv-cs-cv

A Hierarchical Language Model with Predictable Scaling Laws and Provable Benefits of Reasoning

arXiv:2605.13687v1 Announce Type: cross Abstract: We introduce a family of synthetic languages with hierarchical structure -- generated by a broadcast process on trees -- for which the role of context

Assessing the Creativity of Large Language Models: Testing, Limits, and New Frontiers

arXiv:2605.13450v1 Announce Type: new Abstract: Measuring the creativity of large language models (LLMs) is essential for designing methods that can improve creativity and for enhancing our scientific

CLIP Tricks You: Training-free Token Pruning for Efficient Pixel Grounding in Large VIsion-Language Models

arXiv:2605.13178v1 Announce Type: cross Abstract: In large vision-language models, visual tokens typically constitute the majority of input tokens, leading to substantial computational overhead. To ad

Constraints-of-Thought: A Framework for Constrained Reasoning in Language-Model-Guided Search

arXiv:2510.08992v3 Announce Type: replace Abstract: While researchers have made significant progress in enabling large language models (LLMs) to perform multi-step planning, LLMs struggle to ensure th

safetyarxiv-cs-lg

DisaBench: A Participatory Evaluation Framework for Disability Harms in Language Models

arXiv:2605.12702v1 Announce Type: new Abstract: General-purpose safety benchmarks for large language models do not adequately evaluate disability-related harms. We introduce DisaBench: a taxonomy of t