Search: “models”

Dario Amodei / @darioamodei: Dario Amodei says frontier models should face mandatory third-party testing for cyber, bio, and autonomy risks, in addition to overall transparency requirements — In addit

industrytechmeme

Exploration of Foundation Model-Based Robots in Patient and Elderly Care

arXiv:2606.10208v1 Announce Type: cross Abstract: Demand for older-adult and patient care is growing rapidly as populations age worldwide. Foundation models are increasingly being integrated into robo

GaussTrace: Provenance Analysis of 3D Gaussian Splatting Models with Evidence-based LLM Reasoning

arXiv:2606.10612v1 Announce Type: new Abstract: 3D Gaussian Splatting (3DGS) is a powerful technique for creating high-fidelity 3D assets. However, the widespread sharing and iterative modification of

researcharxiv-cs-cv

In Defense of Information Leakage in Concept-based Models

arXiv:2606.10669v1 Announce Type: cross Abstract: Concept-based models (CMs), deep neural networks that ground their predictions on representations aligned with human-understandable concepts (e.g., 'r

tutorialsarxiv-cs-ai

Interpretable deep convolutional model for nonlinear multivariate time series in complex systems

model-releasesarxiv-cs-lg

arXiv:2501.04339v2 Announce Type: replace-cross Abstract: We introduce the Deep Convolutional Interpreter for Time Series (DCIts), a deep-learning architecture for nonlinear multivariate time series t

Mix, Don't Pick: Why Synthetic Corpus Composition Matters for Time Series Foundation Model Pretraining

arXiv:2606.09912v1 Announce Type: cross Abstract: Choosing the wrong synthetic generator for time-series foundation model pretraining is costly: under identical training budgets, the best and worst ge

PhysMetrics.Weather: An Evaluation Framework for Physical Consistency in ML Weather Models

arXiv:2606.10642v1 Announce Type: new Abstract: Machine learning weather prediction (MLWP) models have achieved impressive forecasting performance at a small fraction of the computational costs requir

QDepth-VLA: Quantized Depth Prediction as Auxiliary Supervision for Vision-Language-Action Models

arXiv:2510.14836v3 Announce Type: replace Abstract: Spatial perception and reasoning are crucial for Vision-Language-Action (VLA) models to accomplish fine-grained manipulation tasks. However, existin

tutorialsarxiv-cs-cv

Representation-Aware Advantage Estimation: Your Reward Model Provides More Than A Scalar Output

arXiv:2606.10528v1 Announce Type: cross Abstract: Current reinforcement learning from human feedback (RLHF) methods primarily rely on scalar rewards from a trained reward model (RM). While effective,

researcharxiv-cs-cl

Self-Supervised Relevance Modelling in Autonomous Driving via Counterfactual Analysis

arXiv:2606.10688v1 Announce Type: new Abstract: Autonomous driving relies on computationally intensive perception pipelines to continuously detect and track objects in the surrounding environment. Whi

safetyarxiv-cs-ro

Industry

Should we try to train an open source AI building model? We obviously have interesting datasets with HF, MLintern, transformers, trl…

Clem Delangue discusses the potential value of training an open source AI model specifically for software development and AI engineering tasks, leveraging Hugging Face's internal datasets including th

industryclem-delangue--x

STEDiff: Strengthening Text Embedding for Text-to-Image Alignment in Diffusion Model

arXiv:2606.10653v1 Announce Type: new Abstract: Although pretrained text-to-image (T2I) generation models can produce high-quality images, they often fail to faithfully reflect the semantic intent of

safetyarxiv-cs-cv

UPLOTS: A Unified Pretrained Language Model for Constrained Time-series Generation

arXiv:2606.10466v1 Announce Type: cross Abstract: In time-series generation, existing approaches typically handcraft ortrain a separate model for each dataset, which hinders their scalability and fail

applicationsarxiv-cs-ai

Who Brought Easter Eggs to Eid? Auditing Cultural Translation of Math Word Problems Across Diverse Languages and Regions

model-releasesarxiv-cs-cl

arXiv:2606.11009v1 Announce Type: new Abstract: Large language models are increasingly used to adapt math word problems for personalized learning at scale, but it remains an open question whether thos

A Topological Characterization of Graph Neural Networks via Stochastic Block Model Embeddings on the n-Sphere

arXiv:2606.07598v1 Announce Type: cross Abstract: We propose a topological framework for comparing trained Graph Neural Networks (GNNs) by mapping the Stochastic Block Models (SBMs) induced on the gra

AMN: An Adaptive Multi-Scale Fusion Network with Boundary and Uncertainty Modeling for Nuclei Segmentation

arXiv:2606.07633v1 Announce Type: cross Abstract: Accurate classification of nuclei subtypes in histopathology images is critical for downstream tasks including tumor grading, immune infiltrate quanti

Automatic Extraction of Structured Information from Brain MRI Reports Using an Open-Weight Large Language Model

arXiv:2606.07721v1 Announce Type: new Abstract: Objectives: Automatic data extraction from free-text radiology reports enables large-scale research, but few studies assessed the performance of large l

BCG-FM: A Foundation Model for Ambient Cardiac Health Sensing

arXiv:2606.07692v1 Announce Type: cross Abstract: Foundation models for wearable biosignals have matched or exceeded supervised specialists across a range of clinical tasks, yet all rely on modalities

Beyond Accuracy: Interpreting Topic Representation in Suicide Ideation Detection Models

arXiv:2606.07714v1 Announce Type: cross Abstract: Suicide ideation detection models are typically evaluated using aggregate performance metrics, yet little is known about how they internally represent

safetyarxiv-cs-ai

Beyond Spherical Harmonics: Rethinking Appearance Models for Radiance Reconstruction

arXiv:2606.09794v1 Announce Type: new Abstract: View-dependent appearance modeling remains a challenging problem in novel-view synthesis and reconstruction. Accurately representing complex angular eff

researcharxiv-cs-cv

BrainSurgery: Reproducible and Reliable Declarative Weight Manipulations for Model Editing and Upcycling

arXiv:2606.09707v1 Announce Type: new Abstract: As deep learning models scale, managing, inspecting, and modifying large checkpoints has become increasingly challenging. Researchers often need to alte

Community-Specific Slang and Entity Detection via Semantic Shift in Fine-Tuned Language Models

arXiv:2606.07522v1 Announce Type: cross Abstract: We propose an unsupervised method of resolving slang, unique entities, and folklore from online communities by isolating words in the lexicon that hav

Conan-embedding-v3: Fusing Modality-Specific Models for Omni-Modal Embedding

arXiv:2606.09331v1 Announce Type: cross Abstract: Omni-modal retrieval promises a single embedding space for text, image, video, document, and audio inputs, but building such a unified retriever is di

Discovering heuristics in a complex SAT solver with large language models

arXiv:2507.22876v2 Announce Type: replace Abstract: The Satisfiability problem (SAT) is fundamental in computational complexity theory and has a wide range of industrial applications. Optimizing moder

Enabling KV Caching of Shared Prefix for Diffusion Language Models

arXiv:2606.07571v1 Announce Type: cross Abstract: Key-value (KV) caching for shared prefixes is essential for high-throughput large language model (LLM) serving, but it faces critical challenges in em

Local Ai

Federated Large Language Models: Current Progress and Future Directions

arXiv:2409.15723v3 Announce Type: replace Abstract: Large Language Models have achieved impressive performance across diverse applications, yet their training typically depends on centralized data col

local-aiarxiv-cs-lg

Latent Spatial Memory for Video World Models

arXiv:2606.09828v1 Announce Type: new Abstract: Video world models that maintain 3D spatial consistency across generated frames typically rely on explicit point cloud memory constructed in RGB space.

researcharxiv-cs-cv

Learning from flowsheets: A generative transformer model for autocompletion of flowsheets

arXiv:2208.00859v2 Announce Type: replace Abstract: We propose a novel method enabling autocompletion of chemical flowsheets. This idea is inspired by the autocompletion of text. We represent flowshee

tutorialsarxiv-cs-lg

LUNA-AD: Lightweight Uncertainty-Aware Language Model with Lifelong Learning for Autonomous Driving

arXiv:2606.08470v1 Announce Type: new Abstract: While large language models (LLMs) offer promising reasoning capabilities, their integration into safety-critical driving systems is hindered by limited

safetyarxiv-cs-ro

MatMind: A Structure-Activity Knowledge-Driven Generative Foundation Model for Materials Science

arXiv:2606.07712v1 Announce Type: cross Abstract: Progress in AI-driven crystal materials science has so far been carried by narrow architectures purpose-built for individual tasks -- graph neural net

Modeling Stochastic Conditional Dynamics from Sparse Observations via Kernel-Stabilized Flow Matching

arXiv:2411.08314v5 Announce Type: replace Abstract: Learning to transform conditional probability densities over time is a fundamental challenge spanning probabilistic modeling and the natural science

applicationsarxiv-cs-lg

MotionWAM: Towards Foundation World Action Models for Real-Time Humanoid Loco-Manipulation

arXiv:2606.09215v1 Announce Type: new Abstract: World Action Models (WAMs) couple a video dynamics prior to the policy and have shown encouraging results on tabletop manipulation, but iterative denois

safetyarxiv-cs-ro

Local Ai

Need We Teach Foundation Models What is a Generative Image? Gradient-Free Generative Artifact Detection via Analytic Spectral Adaptation

arXiv:2606.07660v1 Announce Type: new Abstract: Adapting foundation models to detect generative artifacts via gradient-based updates compromises their intrinsic representations. Under optimization on

local-aiarxiv-cs-cv

Local Ai

Perceptive Behavior Foundation Model: Adapting Human Motion Priors to Robot-Centric Terrain

arXiv:2606.08059v1 Announce Type: new Abstract: Humanoid behavior foundation models aim to acquire reusable whole-body control policies from broad human motion priors, enabling a single controller to

local-aiarxiv-cs-ro

Physics-Aware Sparse Learning and Selective Online Adaptation for Euler-Lagrange Robot Dynamics

arXiv:2606.09640v1 Announce Type: new Abstract: Accurate dynamics models are essential for model-based robotic control, yet nominal Euler--Lagrange models often become inaccurate in the presence of pa

applicationsarxiv-cs-ro

Scaffold Effects on GAIA: A Controlled Comparison

arXiv:2606.08529v1 Announce Type: new Abstract: Published agent capability scores conflate what a model can do with what its scaffold lets it do, and the magnitude of this elicitation gap is not well

State Backdoor: Towards Stealthy Real-world Poisoning Attack on Vision-Language-Action Model in State Space

arXiv:2601.04266v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models are widely deployed in safety-critical embodied AI applications such as robotics. However, their complex m

safetyarxiv-cs-lg

TimpaTeks: Automatic In-place Text Sequence Modification via Diffusion Language Model Steering

arXiv:2606.08408v1 Announce Type: cross Abstract: We extend activation steering to diffusion language models (DLMs) and study a novel problem that arose due to the inference mechanism of DLMs: Modifyi

Hardware

Toward Compiler World Models: Learning Latent Dynamics for Efficient Tensor Program Search

arXiv:2606.09312v1 Announce Type: new Abstract: Tensor program optimization is essential for modern machine learning systems, but its search space is enormous. Existing auto-schedulers reduce measurem

hardwarearxiv-cs-lg

Towards Graph Foundation Models for Dynamics in Complex Networked Systems: Lessons from Super-Spreader Identification in Multilayer Networks

arXiv:2606.08306v1 Announce Type: new Abstract: Network dynamics - including spreading, influence maximisation, and epidemic modelling - remain largely confined to the transductive paradigm, where mod

applicationsarxiv-cs-lg

vla.cpp: A Unified Inference Runtime for Vision-Language-Action Models

arXiv:2606.08094v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) policies are typically shipped as Python/PyTorch stacks that assume a workstation-class GPU, a mismatch for the hardware

A robust PPG foundation model using multimodal physiological supervision

arXiv:2606.07365v1 Announce Type: cross Abstract: Photoplethysmography (PPG), a non-invasive measure of changes in blood volume, is widely used in both wearable devices and clinical settings. Recent P

tutorialsarxiv-cs-ai

AI Sovereignty: A Qualitative Model of Strategic Competition as AI Becomes an Instrument of National Power

arXiv:2606.07245v1 Announce Type: cross Abstract: AI sovereignty is the extent to which a nation independently controls its artificial intelligence (AI) technologies. The race toward ever-more-sophist

An Analysis Focused on Womens Safety: Can VAD Models Be Enhanced by a Multi-modal Dataset?

model-releasesarxiv-cs-cv

arXiv:2605.25806v2 Announce Type: replace Abstract: Women's safety and security are paramount for a modern society. Crimes against women occur in daylight as well as in low-light conditions. Often, su

Architecturally Significant MLOps Guidelines for ML Model Integration and Deployment: a Gray Literature Review

arXiv:2606.06535v1 Announce Type: cross Abstract: Context. Despite the growing adoption of Machine Learning Operations (MLOps), teams often approach MLOps projects in an ad hoc manner due to the lack

As many of you were interested in the technical details of the model, here is a followup thread to go more into technical details about VLA-…

As many of you were interested in the technical details of the model, here is a followup thread to go more into technical details about VLA-JEPA. 1. Architecture 2. Training 3. Recipe for the demo 4.

tutorialsyann-lecun--x

Bootstrap Theory of Representational Emergence: Explanatory Insufficiency as a Driver of Representation Learning and World Models

arXiv:2606.07303v1 Announce Type: new Abstract: Representation learning is central to modern machine learning, enabling transitions from handcrafted features to learned embeddings, latent spaces, foun

DyCon: Dynamic Reasoning Control via Evolving Difficulty Modeling

arXiv:2606.07108v1 Announce Type: new Abstract: Recent advances in Large Reasoning Models (LRMs) demonstrate remarkable performance improvements by iteratively reflecting, exploring, and executing com