Holo1: New family of GUI automation VLMs powering GUI agent Surfer-H
H Company has released Holo1, a new family of vision-language models specifically designed for GUI automation tasks. These models power Surfer-H, a GUI agent capable of interacting with graphical interfaces. The release represents a specialized VLM family targeting the agent-tool ecosystem for desktop/web automation. Details on architecture, training data, and benchmarks are expected in the accompanying blog post.
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H Company releases Holo3.1: fast local computer use agent model
H Company published a Hugging Face blog post announcing Holo3.1, a model designed for computer use agents that runs locally. The release targets fast, on-device computer control tasks, positioning it in the growing space of open/local agentic models. The body content is minimal, but the announcement signals a new entrant in the local computer-use agent category.
H Company's Holo2 235B-A22B Model Leads in UI Localization
H Company has released Holo2, a 235B parameter mixture-of-experts model with 22B active parameters, announced via the Hugging Face blog. The model is positioned as a leader in UI localization tasks, suggesting a focus on agent-oriented or multimodal UI understanding capabilities. The post appears to be a product/model introduction from H Company, a relatively newer AI lab.
smolagents Now Supports Vision-Language Models
Hugging Face has added vision-language model (VLM) support to its smolagents framework, enabling agents to process and reason over visual inputs alongside text. This update extends the agentic tooling ecosystem to multimodal workflows. The announcement comes from the Hugging Face blog, which serves as the primary communication channel for the smolagents project.
Smol2Operator: Post-Training GUI Agents for Computer Use
Hugging Face published a blog post introducing Smol2Operator, a post-training approach for building GUI agents capable of computer use tasks. The work focuses on training small language models to operate graphical user interfaces, extending the SmolLM2 model family into the agent/computer-use domain. The post likely covers training methodology, datasets, and evaluation of the resulting GUI agent capabilities.
Holotron-12B - High Throughput Computer Use Agent
Hcompany has released Holotron-12B, a 12-billion parameter model designed for computer use agent tasks with a focus on high throughput. The model is announced via the Hugging Face blog, suggesting it is available or soon available on the platform. Details on architecture, benchmarks, and capabilities are not present in the provided body text.
HiViG: History-aware visually grounded critic improves computer use agents across GUI benchmarks
Researchers introduce HiViG, a test-time framework for Computer Use Agents that addresses two weaknesses in existing critic models: short-sighted decision loops and lack of visual grounding. The system trains a multimodal critic on real GUI trajectories to maintain a compact macro-action history and verify execution coordinates against live screenshots before action execution. Evaluated on web, mobile, and desktop benchmarks, HiViG improves average success rates by 5.8% over the strongest baseline with Qwen3-VL-32B and 9.0% with Gemini-3-Flash, with both history and grounding components shown to be independently necessary.
Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance
TII UAE has released Falcon-H1, a new family of hybrid-head language models combining attention and state-space mechanisms to improve efficiency and performance. The models are published on Hugging Face and represent TII's latest iteration in the Falcon series. The hybrid architecture targets better inference economics and competitive benchmark results relative to model size.
ScreenSuite: Comprehensive Evaluation Suite for GUI Agents
Hugging Face has released ScreenSuite, described as the most comprehensive evaluation suite for GUI (Graphical User Interface) agents. The suite aims to standardize and broaden benchmarking for agents that interact with visual interfaces. This addresses a gap in the evaluation ecosystem for screen-based AI agents, which are increasingly relevant as agentic systems expand into desktop and web automation tasks.