Search: “models”

How Many Human Survey Respondents is a Large Language Model Worth? An Uncertainty Quantification Perspective

arXiv:2502.17773v5 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly used to simulate survey responses, but synthetic data can be misaligned with the human populatio

researcharxiv-cs-lg

21 May 2026

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Musical Attention Transformer: Music Generation Using a Music-Specific Attention Model

DGX agent

arXiv:2605.21081v1 Announce Type: cross Abstract: This study aims to enhance the quality of music generation using Transformers by incorporating meta-information. While Transformer-based approaches ar

researcharxiv-cs-lg

21 May 2026

SDM: A Powerful Tool for Evaluating Model Robustness

arXiv:2605.20308v1 Announce Type: new Abstract: Gradient-based attacks are important methods for evaluating model robustness. However, since the proposal of APGD, it has been difficult for such method

researcharxiv-cs-cv

21 May 2026

You Don't Need Attention: Gated Convolutional Modeling for Watch-Based Fall Detection

arXiv:2605.20275v1 Announce Type: new Abstract: Existing deep learning approaches for wearable fall detection systems rely on self-attention mechanisms that impose quadratic computational overhead, di

21 May 2026

3D Modeling and Automated Measurement of Concrete Cracks via Segment Anything Refinement and Visual Inertial LiDAR Fusion

arXiv:2501.09203v2 Announce Type: replace Abstract: Visual-Spatial Systems has become increasingly essential in concrete crack inspection. However, existing methods often lacks adaptability to diverse

FieldFormer: Locality-Aware Transformers for Spatio-Temporal Modeling on Sparse Sensor Networks

arXiv:2510.03589v2 Announce Type: replace Abstract: Spatio-temporal sensor data in real-world systems is often sparse, noisy, and irregular, making latent field reconstruction fundamentally underconst

local-aiarxiv-cs-lg

FlyMirage: A Fully Automated Generation Pipeline for Diverse and Scalable UAV Flight Data via Generative World Model

arXiv:2605.19600v1 Announce Type: new Abstract: In the field of Vision-Language Navigation (VLN), aerial datasets remain limited in their ability to combine scale, diversity, and realism, often relyin

applicationsarxiv-cs-ro

Position: Graph Condensation Needs a Reset -- Move Beyond Full-dataset Training and Model-Dependence

arXiv:2605.18893v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) are powerful tools for learning from graph-structured data, but their scalability is increasingly strained by the size of r

applicationsarxiv-cs-lg

Rotation-Aligned Key Channel Pruning for Efficient Vision-Language Model Inference

arXiv:2605.19218v1 Announce Type: cross Abstract: Vision-Language Models suffer severe KV cache pressure at inference, as a single image often encodes into thousands of tokens. Most existing methods e

Axial-Relation Guided Fusion State Space Model for Optical-Elevation Sensing Image Segmentation

arXiv:2605.16768v1 Announce Type: new Abstract: Semantic segmentation of multi-source remote sensing images is a fundamental task for Earth observation applications. Existing methods often struggle wi

Confidence Geometry Reveals Trace-Level Correctness in Large Language Model Reasoning

arXiv:2605.16824v1 Announce Type: cross Abstract: Large language models (LLMs) generate not only reasoning text, but also token-level confidence trajectories that record how uncertainty evolves during

researcharxiv-cs-cl

Counterparty Modeling is Not Strategy: The Limits of LLM Negotiators

arXiv:2605.16575v1 Announce Type: new Abstract: Negotiation requires more than inferring what the other side wants: it requires using that information to make advantageous offers and counteroffers ove

Does Your Reasoning Model Implicitly Know When to Stop Thinking?

arXiv:2602.08354v5 Announce Type: replace Abstract: Recent advancements in large reasoning models (LRMs) have greatly improved their capabilities on complex reasoning tasks through Long Chains of Thou

DriveMoE: Mixture-of-Experts for Vision-Language-Action Model in End-to-End Autonomous Driving

arXiv:2505.16278v2 Announce Type: replace-cross Abstract: End-to-end autonomous driving (E2E-AD) demands effective processing of multi-view sensory data and robust handling of diverse and complex driv

From Demographics to Survey Anchors: Evaluating LLM Agents for Modeling Retirement Attitudes

arXiv:2605.16303v1 Announce Type: cross Abstract: Large language models (LLM) agents may offer tools to predict human responses to surveys. A common technique for defining these agents uses only demog

safetyarxiv-cs-ai

Tutorials

Learning What Evaluators Value: A Reliable Approach to Modeling Evaluator Preferences

arXiv:2605.16615v1 Announce Type: new Abstract: In many applications, human and LLM evaluators use assessments of relevant criteria to create an overall evaluation for an item or individual. For examp

tutorialsarxiv-cs-lg

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Generative Modeling by Minimizing the Wasserstein-2 Loss

arXiv:2406.13619v4 Announce Type: replace-cross Abstract: This paper develops a generative model by minimizing the second-order Wasserstein loss (the W_2 loss) through a distribution-dependent ordinar

researcharxiv-cs-lg

Multi-Modal World Model for Physical Robot Interactions: Simultaneous Visual and Tactile Predictions for Enhanced Accuracy

arXiv:2304.11193v2 Announce Type: replace-cross Abstract: Predicting the outcomes of robotic actions, often referred to as learning a world model, in complex environments remains a fundamental challen

DP-{lambda}CGD: Efficient Noise Correlation for Differentially Private Model Training

arXiv:2601.22334v2 Announce Type: replace Abstract: Differentially private stochastic gradient descent (DP-SGD) is the gold standard for training machine learning models with formal differential priva

researcharxiv-cs-lg

Efficient LLM-based Advertising via Model Compression and Parallel Verification

arXiv:2605.11582v1 Announce Type: new Abstract: Large language models (LLMs) have shown remarkable potential in advertising scenarios such as ad creative generation and targeted advertising. However,

applicationsarxiv-cs-cl

Enhancing Target-Guided Proactive Dialogue Systems via Conversational Scenario Modeling and Intent-Keyword Bridging

arXiv:2605.11964v1 Announce Type: new Abstract: A target-guided proactive dialogue system aims to steer conversations proactively toward pre-defined targets, such as designated keywords or specific to

safetyarxiv-cs-cl

Joint Learning of Hierarchical Neural Options and Abstract World Model

arXiv:2602.02799v2 Announce Type: replace Abstract: Building agents that can perform new skills by composing existing skills is a long-standing goal of AI agent research. Towards this end, we investig

agentsarxiv-cs-lg

Model-based Bootstrap of Controlled Markov Chains

arXiv:2605.12410v1 Announce Type: cross Abstract: We propose and analyze a model-based bootstrap for transition kernels in finite controlled Markov chains (CMCs) with possibly nonstationary or history

safetyarxiv-cs-lg

Model-Level GNN Explanations via Rule-to-Graph Readout for Logit Reconstruction

arXiv:2503.09051v2 Announce Type: replace Abstract: We propose a novel model-level GNN explanation framework that shifts the explanation target from class-wise rule extraction to rule-based logit reco

applicationsarxiv-cs-lg

Tutorials

APCD: Adaptive Path-Contrastive Decoding for Reliable Large Language Model Generation

arXiv:2605.09492v1 Announce Type: cross Abstract: Large language models (LLMs) often suffer from hallucinations due to error accumulation in autoregressive decoding, where suboptimal early token choic

tutorialsarxiv-cs-ai

Biosignal Fingerprinting: A Cross-Modal PPG-ECG Foundation Model

arXiv:2605.09579v1 Announce Type: cross Abstract: Cardiovascular disease remains the leading cause of global mortality, yet scalable cardiac monitoring is hindered by the gap between diagnostic-rich E

applicationsarxiv-cs-ai

Composing Policy Gradients and Prompt Optimization for Language Model Programs

arXiv:2508.04660v2 Announce Type: replace Abstract: Group Relative Policy Optimization (GRPO) has proven to be an effective tool for post-training language models (LMs). However, AI systems are increa

safetyarxiv-cs-cl

Detector-Empowered Video Large Language Model for Efficient Spatio-Temporal Grounding

arXiv:2512.06673v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) are rapidly expanding from general video understanding to finer-grained understanding such as spatio-tempor

EvoStreaming: Your Offline Video Model Is a Natively Streaming Assistant

arXiv:2605.10343v1 Announce Type: cross Abstract: Streaming video understanding demands more than watching longer videos: assistants must decide when to speak in real time, balancing responsiveness ag

safetyarxiv-cs-ai

Fully Decentralized Cooperative Multi-Agent Reinforcement Learning is A Context Modeling Problem

arXiv:2509.15519v2 Announce Type: replace Abstract: This paper studies fully decentralized cooperative multi-agent reinforcement learning, where each agent solely observes the states, its local action

local-aiarxiv-cs-lg

GenCellAgent: Generalizable, Training-Free Cellular Image Segmentation via Large Language Model Agents

arXiv:2510.13896v2 Announce Type: replace-cross Abstract: Cellular image segmentation is essential for quantitative biology yet remains difficult due to heterogeneous modalities, morphological variabi

Hierarchical Causal Abduction: A Foundation Framework for Explainable Model Predictive Control

arXiv:2605.10624v1 Announce Type: new Abstract: Model Predictive Control (MPC) is widely used to operate safety-critical infrastructure by predicting future trajectories and optimizing control actions

safetyarxiv-cs-ai