Search: “models”

Multimodal Empirical Bayes Variational Autoencoders for Joint Longitudinal and Time-to-Event Modeling

arXiv:2607.13984v1 Announce Type: cross Abstract: Longitudinal tumor measurements, dropout information, and genetic covariates provide complementary information about treatment response, but integrati

16 Jul 2026

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Agents

Social Simulations: from Agent-Based Modeling to Digital Twins

DGX agent

arXiv:2607.13693v1 Announce Type: cross Abstract: This book chapter covers the evolution of social simulation from classical agent-based models, in which agents interact according to explicitly define

agentsarxiv-cs-ai

16 Jul 2026

The SIGReg Objective as Variational Free Energy: A Theoretical Active-Inference Account of JEPA World Models

arXiv:2607.13612v1 Announce Type: cross Abstract: Joint-Embedding Predictive Architectures (JEPAs) are the dominant design for latent world models, yet they are usually justified by empirical performa

16 Jul 2026

Towards Enhancing 3D Spatial Reasoning in Medical Multimodal Large Language Models

arXiv:2607.13860v1 Announce Type: new Abstract: While Multimodal Large Language Models (MLLMs) have demonstrated remarkable success in 2D medical image understanding, their extension to 3D volumetric

researcharxiv-cs-cv

16 Jul 2026

Entropy-Preserving Supervised Fine-Tuning via Adaptive Self-Distillation for Large Reasoning Models

arXiv:2602.02244v3 Announce Type: replace-cross Abstract: The standard post-training recipe for large reasoning models, supervised fine-tuning followed by reinforcement learning (SFT-then-RL), may lim

researcharxiv-cs-cl

Graph Feedback Controls Consensus and Clique Formation in Open-Weight Language-Model Populations

arXiv:2607.12077v1 Announce Type: new Abstract: Multi-agent language-model systems increasingly route local interactions, yet the runtime interaction graph is often treated as an implementation detail

local-aiarxiv-cs-ai

Hy-Embodied-VLM-1.0: Efficient Physical-World Agents

arXiv:2607.12894v1 Announce Type: new Abstract: Building capable embodied agents requires not only multimodal perception and understanding, but also agentic capabilities for reasoning about actions, a

LP Mining with LP2Graph: A Use Case for Railway Rescheduling

arXiv:2607.11980v1 Announce Type: new Abstract: Like many optimization-driven domains, railway rescheduling relies on Mixed-Integer Linear Programming (MILP), yet the field's modeling knowledge is sca

PolarBM: Complex-valued Boltzmann Machine for Modeling Audio Signals in Polar and Log-polar Coordinates

arXiv:2607.12417v1 Announce Type: new Abstract: Although vast amounts of data, such as audio signal spectra, are naturally represented using complex numbers, conventional machine learning methods ofte

SlimPer: Make Personalization Model Slim and Smart

arXiv:2607.12281v1 Announce Type: cross Abstract: Transformer-style architectures are increasingly adopted for industrial recommendation systems, yet they inherit a design premise misaligned with the

SymbOmni: Evolving Agentic Omni Models via Symbolic Concept Learning

arXiv:2607.12042v1 Announce Type: new Abstract: Visual generation is increasingly ubiquitous in diverse domains, from text-to-image/video synthesis to multimodal interactive creation. Yet prevailing m

agentsarxiv-cs-cv

Watermark Forensics for Generative Models: An Information-Theoretic Perspective

arXiv:2607.13003v1 Announce Type: cross Abstract: A watermark in a generative model's output is usually asked only whether a text is machine-made. The same mark can do more: attribute it to the user w

safetyarxiv-cs-lg

COALA: Robust Contextualized Speech-augmented Language Modeling for ASR via Contrastive Regularizer and Biasing Score Estimation

model-releasesarxiv-cs-cl

arXiv:2607.08117v1 Announce Type: new Abstract: Contextual biasing seeks to integrate external knowledge into automatic speech recognition (ASR) systems to accurately recognize domain-specific entitie

CommuniWave:A Machine Learning Model for Quantifying the Degree of Temporary Informal Behavior in Urban Communities

arXiv:2607.08554v1 Announce Type: new Abstract: For urban managers and designers, improving the functional attributes of urban communities to enhance territorial resilience in the face of complexity a

Concretized Proposition Prompting Resolves Composition-Knowledge Dichotomy in Large Language Models

arXiv:2607.08018v1 Announce Type: new Abstract: LLMs often struggle to balance compositionality with knowledgeability, a challenge we define as Composition-Knowledge Dichotomy. To address this, we pro

Dropping Just a Handful of Preferences Can Change Top Large Language Model Rankings

arXiv:2508.11847v4 Announce Type: replace-cross Abstract: We propose a method for evaluating the robustness of widely used LLM ranking systems -- variants of a Bradley--Terry model -- to dropping a wo

Game Theory Driven Multi-Agent Framework Mitigates Language Model Hallucination

arXiv:2607.08403v1 Announce Type: new Abstract: The application of lightweight Large Language Models in rule-based scientific domains remains severely limited by their tendency to mimic linguistic pat

agentsarxiv-cs-ai

Large-Language-Models-as-a-Judge in Theory-Agnostic Adaptive Metric-Alignment for Prototypical Networks in Personality Recognition

arXiv:2607.08374v1 Announce Type: cross Abstract: Personality recognition has traditionally been constrained by theory-dependent formulations, where models are trained to fit predefined psychological

Omni-Sleep: A Sleep Foundation Model via Hierarchical Contrastive Learning of CNS--ANS Dynamic

arXiv:2607.07720v1 Announce Type: cross Abstract: Sleep physiology arises from the coordinated dynamics of the central nervous system (CNS) and autonomic nervous system (ANS), as reflected by multimod

Structural Bottlenecks on Frequency Representation in End-to-End Audio Models

arXiv:2607.08545v1 Announce Type: cross Abstract: End-to-end neural audio models achieve high-fidelity compression and generation. We might read that performance as evidence they directly represent in

Towards Isolated Interventions via Almost Orthogonal Features in Language Models

arXiv:2602.04718v2 Announce Type: replace-cross Abstract: A central premise in mechanistic interpretability is that meaningful concepts in language models are represented by linear features in activat

local-aiarxiv-cs-ai

TypeProbe: Recovering Type Representations from Hidden States of Pre-trained Code Models

arXiv:2607.08339v1 Announce Type: cross Abstract: State-of-the-art code models achieve impressive performance, yet the extent to which they internally encode type information remains poorly understood

XOV-Action: Towards Generalizable Open-Vocabulary Action Recognition

arXiv:2403.01560v3 Announce Type: replace Abstract: Inspired by the impressive success of image-text foundation models, recent works have proposed to adapt these foundation models to video data, leadi

EMO-R3: Reflective Reinforcement Learning for Emotional Reasoning in Multimodal Large Language Models

arXiv:2602.23802v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) have shown remarkable progress in visual reasoning and understanding tasks but still struggle to capture th

Institutional Red-Teaming: Deployment Rules, Not Just Models, Causally Shape Multi-Agent AI Safety

arXiv:2607.07695v1 Announce Type: new Abstract: We introduce institutional red-teaming, an evaluation methodology for testing deployment rules in multi-agent AI: hold the agents, objectives, and task

Strategies for Span Labeling with Large Language Models

arXiv:2601.16946v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used for text analysis tasks, such as named entity recognition or error detection. Unlike encoder-base

researcharxiv-cs-cl

Vision Foundation Models in Radiology: A Scoping Review of Data, Methodology, Evaluation and Clinical Translation

arXiv:2607.07219v1 Announce Type: cross Abstract: Vision foundation models (VFMs) are increasingly being developed for radiological imaging, yet their definition, development and evaluation remain het

WAM-TTT: Steering World-Action Models by Watching Human Play at Test Time

arXiv:2607.06988v1 Announce Type: cross Abstract: Steering robot foundation models (RFMs) toward new task variants or user-preferred behaviors remains challenging, often requiring additional robot dem

A Coin Flip Per Token: Bernoulli Sparse Steering of Large Language Models

arXiv:2607.05615v1 Announce Type: new Abstract: Activation steering via sparse autoencoders (SAEs) enables behavioral control of large language models without task-specific fine-tuning, but standard m

safetyarxiv-cs-lg

AVA-VLM: Adaptive Visual Attention-Vision Language Model for In-the-Wild Construction Site Monitoring

arXiv:2607.05859v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are promising for construction-site monitoring, and recent construction-tailored VLMs have primarily adapted pretrained VL

local-aiarxiv-cs-cv

Diffusion Language Model Parallel Decoding via Product-of-Experts Bridge

arXiv:2606.08048v1 Announce Type: cross Abstract: Diffusion language models (DLMs) offer substantial speed advantages through parallel decoding, but the lack of token dependencies limits generation qu

From Global to Granular: Revealing IQA Model Performance via Correlation Surface

arXiv:2601.21738v2 Announce Type: replace-cross Abstract: Evaluation of Image Quality Assessment (IQA) models has long been dominated by global correlation metrics, such as Pearson Linear Correlation

Applications

Full-range Binary Classifier Calibration for Stable Model Updates in Production

arXiv:2607.05481v1 Announce Type: cross Abstract: Detection models running in adversarial environments face a malicious distribution that drifts rapidly while the benign distribution stays comparative

applicationsarxiv-cs-ai

Hypothesis-driven Model Expansion under Uncertainty for Open-World Robot Planning

arXiv:2607.06501v1 Announce Type: new Abstract: We consider an open-world planning setting in which service robots must operate in unknown environments with incomplete knowledge of objects and actions

agentsarxiv-cs-ro

Hardware

Life Cycle Assessment of Pre-training the Lucie 7B Open-Source Large Language Model on the Jean Zay Supercomputer

arXiv:2607.05408v1 Announce Type: cross Abstract: The environmental impact of training large language models (LLMs) is increasingly scrutinised, yet most published estimates focus on operational energ

hardwarearxiv-cs-lg

MLLM-LLaVA-FL: Multimodal Large Language Model Assisted Federated Learning

arXiv:2409.06067v3 Announce Type: replace Abstract: Previous studies on federated learning (FL) often encounter performance degradation due to data heterogeneity among different clients. In light of t

local-aiarxiv-cs-ai

Narrative World Model: Narratology-Grounded Writer Memory for Long-Form Fiction

arXiv:2607.05577v1 Announce Type: new Abstract: Long-form fiction writers need memory that answers multi-hop questions about evolving story state: who knows a secret and when they learned it, whether

Reward as An Agent for Embodied World Models

arXiv:2606.19990v2 Announce Type: replace Abstract: While RL has become a promising tool for refining world models, existing methods largely rely on conservative rollouts near the training distributio

agentsarxiv-cs-ai

Social 3D Scene Graphs: Modeling Human Actions and Relations for Interactive Service Robots

arXiv:2509.24966v2 Announce Type: replace Abstract: Understanding how people interact with their surroundings and each other is essential for enabling robots to act in socially compliant and context-a

Structured Data Extraction from Real Estate Documents using Clustering, Classification, and Large Language Models

arXiv:2607.06012v1 Announce Type: new Abstract: Real estate property listings expose structured metadata through the API. Still, the richest property-level information (i.e., legal status, structural

Applications

Training-Free Acceleration for Vision-Language-Action Models with Action Caching and Refinement

arXiv:2607.06370v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have emerged as a promising approach for generalizable robotic manipulations. In particular, flow matching-based V

applicationsarxiv-cs-cv

Tutorials

A harmonised dataset for Earth system foundation models

arXiv:2607.03298v1 Announce Type: cross Abstract: Foundation models for Earth systems have so far been trained primarily on physical climate and weather data, with limited representation of the human

tutorialsarxiv-cs-ai

AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments

arXiv:2607.05174v1 Announce Type: new Abstract: Language agents, i.e., LLM agents, progress rapidly and are increasingly deployed in production environments. This trend underscores the urgent need for

AMT-APC: Automatic Piano Cover by Fine-Tuning an Automatic Music Transcription Model

arXiv:2409.14086v2 Announce Type: replace-cross Abstract: There have been several studies on automatically generating piano covers, and recent advancements in deep learning have enabled the creation o

APeB: Benchmarking Personalization Ability of Large Language Model Agents

arXiv:2607.03162v1 Announce Type: new Abstract: LLM-powered agents struggle with personalization when users issue raw, underspecified queries. In this setting, agents must infer latent intent, extract

Hardware

AquaGen: Scaling generative models to molecular dynamics precision on thousands of atoms

arXiv:2607.03513v1 Announce Type: cross Abstract: We present AquaGen, the first all-atom, explicit solvent, periodic-boundary-condition-aware generative model that produces molecular configurations fr

hardwarearxiv-cs-lg

CanniUplift: A Holistic Framework for Mitigating Seller and Incentive Cannibalization in E-commerce Uplift Modeling

arXiv:2607.05242v1 Announce Type: cross Abstract: Personalized incentive allocation is vital for e-commerce, where uplift modeling is the standard for estimating Individual Treatment Effects (ITE). Ho

Tutorials

EyeMulator: Improving Code Language Models by Mimicking Human Visual Attention

arXiv:2508.16771v3 Announce Type: replace-cross Abstract: Code Language Models (CodeLLMs) learn token importance from data correlations, whereas human developers attend selectively to semantically sal

tutorialsarxiv-cs-ai