GUIGuard-Bench is a new benchmark with annotated GUI screenshots that measures privacy recognition, planning fidelity under protection, and utility impact for trajectory-based GUI agents.
Fara-7b: An efficient agentic model for computer use
8 Pith papers cite this work. Polarity classification is still indexing.
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2026 8representative citing papers
ReVision reduces visual token usage by 46% on average in agent trajectories via a learned patch selector and improves success rates by 3% on three benchmarks, showing that history saturation stems from inefficient representations rather than lack of utility.
Weblica scales RL training for visual web agents by building thousands of reproducible environments through HTTP caching for stable replays and LLM synthesis from real sites, yielding an 8B model that beats similar open baselines on navigation benchmarks.
Open 4B and 8B visual web agents achieve state-of-the-art results on browser benchmarks by predicting actions from screenshots and instructions, outperforming similar open models and some closed larger-model agents, with full release of data and code planned.
SimGym is a browser-based VLM agent framework that simulates A/B test outcomes on e-commerce storefronts with 77% directional agreement on add-to-cart shifts from real buyer traffic.
Diverse teacher-generated rationales improve MLLM visual persuasiveness prediction via supervised fine-tuning, while a new three-dimensional faithfulness framework shows that prediction accuracy alone does not ensure faithful reasoning and that decision sensitivity best matches human preferences.
The authors built 1,000 synthetic computers and ran long-horizon agent simulations on them, generating experiential data that improves AI performance on both similar and new productivity tasks.
Generalizable agents require environment scaling via diverse executable rule-sets, distinguished from trajectory and task scaling in a new taxonomy.
citing papers explorer
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GUIGuard-Bench: Toward a General Evaluation for Privacy-Preserving GUI Agents
GUIGuard-Bench is a new benchmark with annotated GUI screenshots that measures privacy recognition, planning fidelity under protection, and utility impact for trajectory-based GUI agents.
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ReVision: Scaling Computer-Use Agents via Temporal Visual Redundancy Reduction
ReVision reduces visual token usage by 46% on average in agent trajectories via a learned patch selector and improves success rates by 3% on three benchmarks, showing that history saturation stems from inefficient representations rather than lack of utility.
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Weblica: Scalable and Reproducible Training Environments for Visual Web Agents
Weblica scales RL training for visual web agents by building thousands of reproducible environments through HTTP caching for stable replays and LLM synthesis from real sites, yielding an 8B model that beats similar open baselines on navigation benchmarks.
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MolmoWeb: Open Visual Web Agent and Open Data for the Open Web
Open 4B and 8B visual web agents achieve state-of-the-art results on browser benchmarks by predicting actions from screenshots and instructions, outperforming similar open models and some closed larger-model agents, with full release of data and code planned.
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SimGym: A Framework for A/B Test Simulation in E-Commerce with Traffic-Grounded VLM Agents
SimGym is a browser-based VLM agent framework that simulates A/B test outcomes on e-commerce storefronts with 77% directional agreement on add-to-cart shifts from real buyer traffic.
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Can MLLMs Reason About Visual Persuasion? Evaluating the Efficacy and Faithfulness of Reasoning
Diverse teacher-generated rationales improve MLLM visual persuasiveness prediction via supervised fine-tuning, while a new three-dimensional faithfulness framework shows that prediction accuracy alone does not ensure faithful reasoning and that decision sensitivity best matches human preferences.
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Synthetic Computers at Scale for Long-Horizon Productivity Simulation
The authors built 1,000 synthetic computers and ran long-horizon agent simulations on them, generating experiential data that improves AI performance on both similar and new productivity tasks.
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Scalable Environments Drive Generalizable Agents
Generalizable agents require environment scaling via diverse executable rule-sets, distinguished from trajectory and task scaling in a new taxonomy.