Search: “models”

arXiv:2607.04457v1 Announce Type: new Abstract: Reconstructing the pulmonary vascular tree from computed tomography (CT) images is essential for quantitative lung analysis, vascular morphology assessm

arXiv:2607.04235v1 Announce Type: new Abstract: Large language model agents operate in partially observable, long-horizon settings where obtaining supervision remains a major bottleneck. We address th

arXiv:2607.04963v1 Announce Type: new Abstract: Reinforcement Learning (RL) is the dominant paradigm for training Large Language Model (LLM) agents on long-horizon tasks. However, sparse and delayed r

arXiv:2510.08048v4 Announce Type: replace-cross Abstract: Query-product relevance prediction is fundamental to e-commerce search and has become even more critical in the era of AI-powered shopping, wh

arXiv:2607.04812v1 Announce Type: new Abstract: Bird's-Eye View (BEV) end-to-end instance prediction has emerged as a robust paradigm for autonomous driving perception, effectively mitigating the erro

arXiv:2607.02615v1 Announce Type: cross Abstract: Generating structured artifacts with Large Language Models - e.g. database queries, threat framework mappings, entity schemas - is relatively straight

This blog is a great review of the current environment on self-improvement We leveraged a number of things here for our harness of harnesses, Zenith, which takes normal models above Fable level on the

arXiv:2607.04515v1 Announce Type: new Abstract: Efik, a tonal language spoken by about 3 million second language speakers and 1.5 million native speakers in Southeastern Nigeria, remains underrepresen

arXiv:2607.02864v1 Announce Type: new Abstract: Reinforcement Learning (RL) has emerged as a powerful approach in financial trading, enabling agents to learn optimal strategies through direct market i

arXiv:2607.04745v1 Announce Type: cross Abstract: Modern thermal visual place recognition (TIR-VPR) frontends based on foundation models achieve remarkable closed-set retrieval but suffer from an over

arXiv:2607.04425v1 Announce Type: cross Abstract: Recent advances in multimodal foundation models and agent systems have driven GUI agents from single-platform task execution toward cross-platform int

arXiv:2510.16923v3 Announce Type: replace-cross Abstract: Deep learning models deployed in safety critical applications like autonomous driving use simulations to test their robustness against adversa

arXiv:2310.00505v3 Announce Type: replace-cross Abstract: Fetal health classification is a critical task in obstetrics, enabling early identification and management of potential health problems. Howev

arXiv:2607.03900v1 Announce Type: new Abstract: Test-time adaptation (TTA) has emerged as a popular paradigm for improving the performance of vision-language models (e.g., CLIP) on downstream tasks. A

arXiv:2607.05230v1 Announce Type: new Abstract: Pre-term infants are susceptible to potentially harmful apnoea-related cessations of breathing due to immature respiratory control. However, reliable re

arXiv:2607.05247v1 Announce Type: new Abstract: Dense spatial perception is essential for physical intelligence, where visual systems are expected to recover structured, metric, and actionable represe

arXiv:2410.00296v2 Announce Type: replace Abstract: Vision-language Models (VLMs) are essential for contextual understanding of both visual and textual information. However, their vulnerability to adv

arXiv:2607.04738v1 Announce Type: cross Abstract: Reconstructing population dynamics is a central problem in the physical and data sciences. Often, the dynamics are modeled as a Wasserstein gradient f

Enterprise artificial intelligence infrastructure has become as critical to AI success as the models themselves. As organizations move AI into production, attention is increasingly shifting toward the

arXiv:2607.02708v1 Announce Type: new Abstract: Adapting frozen vision foundation models to fine-grained segmentation now largely depends on backbone selection. Whether the backbone applies global att

b9879 is a release build identifier in the llama.cpp project, a C/C++ implementation of Meta's LLaMA language models. This release likely contains bug fixes, performance improvements, and feature upda

B9885 is a build-tagged release of llama.cpp, an open-source C/C++ inference engine for running large language models locally. The project does not use traditional semantic versions; instead it ships

b9891 is a build release of llama.cpp, the open-source C/C++ inference engine for running large language models locally. This release enables LLM inference with minimal setup and state-of-the-art perf

Release b9892 is a version identifier for llama.cpp, an open-source C/C++ framework for running large language model inference on consumer hardware. This specific build (b9892) represents a snapshot o

Today, we’re excited to announce a deep-link integration between Hugging Face and Amazon SageMaker AI. Developers can now go from model discovery to hands-on experimentation in SageMaker Studio with a

This article from Modal explains the pricing models and cost considerations for serverless GPU services, likely covering how providers like Modal calculate charges based on GPU usage, execution time,

This post showcases experimental video transition techniques created using ComfyUI and Adobe After Effects, utilizing AI models including GPT Image 2.0, Wan 2.2 (for image-to-video, text-to-video, and

Gary Marcus reflects on his 2023 prediction that large language models would become commoditized, noting that this outcome is now materializing. The post suggests that LLMs are transitioning from nove

OpenAI and Databricks collaborated at the DAIS 2026 conference to demonstrate practical applications of enterprise AI solutions. The partnership likely focused on integrating OpenAI's language models

As models become cheaper and more interchangeable, the durable advantage shifts to the agent harness: the loop, tools, memory, permissions, and workflow you can own and refine. The post Own the loop:

Clem Delangue likely argues that open-source development models will be central to AI's future, emphasizing the benefits of transparency, community collaboration, and democratized access to AI technol

Safety policies define what constitutes safe and unsafe AI outputs, guiding data annotation and model development. However, annotation disagreement is pervasive and can stem from multiple sources such

llama.cpp b9876 is a build-tagged release from the llama.cpp project , a pure C/C++ implementation of large language model inference . The project enables LLM inference with minimal setup and state-of

Automated lint: 51 errors, 15 warnings, 3 info

LLMs training is opaque to end users and responses can be personalized for industry, locality, individual. The Trump regime openly controls who has access to the latest models. Government control of L

Come and chat with us in ICML 🥳 Excited to present Temporal Straightening for Latent Planning in the Tuesday morning session #1509. Let’s talk about world models, JEPA and representation learning. Age

Here's a cool piece of LLM lore: the original scaling laws were wrong due to a bug, which probably led to a lot of wasted compute on oversized undertrained models 🫣 (and that was before we even starte

arXiv:2512.13836v2 Announce Type: replace-cross Abstract: Autonomous driving requires reliable collision avoidance in dynamic environments. Nonlinear Model Predictive Controllers (NMPCs) are suitable

arXiv:2601.04509v2 Announce Type: replace Abstract: Mixed-integer linear programming (MILP) is a foundational framework for combinatorial optimization across science and engineering, but remains hard

arXiv:2607.01974v1 Announce Type: cross Abstract: This technical report describes our system for Task 1 of the DCASE 2026 Challenge, which aims to classify heterogeneous audio recordings according to

arXiv:2607.01840v1 Announce Type: new Abstract: Fault trees are a widely used as effective risk models for complex systems, answering the question 'what can go wrong?', especially through minimal cut

arXiv:2607.02205v1 Announce Type: new Abstract: Sim-to-real transfer in robot learning is often limited by discrepancies between the ideal actuator dynamics assumed during policy training and the nonl

Vercel has announced the availability of Agent Runs in both the Vercel MCP (Model Context Protocol) and CLI (Command Line Interface), expanding developer capabilities for AI-powered automation and dep

arXiv:2607.02247v1 Announce Type: cross Abstract: The aggregation with exponential weights (AEW) estimator is not fully understood in the basic setting of model selection aggregation with squared loss

arXiv:2607.01366v1 Announce Type: new Abstract: Federated learning (FL) research often depends on many small but consequential algorithmic choices: optimizer variants, server aggregation rules, local

arXiv:2607.02121v1 Announce Type: cross Abstract: As Large Language Models (LLMs) and agentic systems become integrated into real-world applications, ensuring their safety and security is critical. Gu