Tag
WebRider is a hierarchical framework that formalizes delegated web tasks as intent contracts, preserving persona-conditioned policies through every browsing step. It includes RiderBench, a benchmark of 4,096 live-web contracts, and an 8B action-policy model trained through its guarded interface.
Microsoft released Fara1.5-27B, a 27B parameter multimodal computer use agent for web browsers, fine-tuned from Qwen3.5-27B, capable of end-to-end web tasks via screenshots.
This paper investigates whether GRPO post-training improves a small (4B-8B) language and vision-language model web agent. It finds a controlled null result: no configuration yields credible gains on mastered tasks, and moderate-to-high learning rates cause degradation or collapse, revealing a double dissociation between degrade and collapse regimes.
MAG introduces a benchmark and harness for multimodal web agents that both execute tasks and generate step-by-step guide text, using screenshots and grounding schemes. The work includes a GRPO training method that nearly doubles the success rate of a 9B agent.
An article sharing hard-won lessons from building a web agent that can interact with real web applications, offering insights into challenges and best practices.
BrowserCode achieves #1 spot on Odysseys benchmark for long-horizon web agents, demonstrating strong performance in multi-hour web workflows.
Browser Use Beta achieved state-of-the-art results on a difficult internal web agent benchmark, using Fable for optimization and analysis.
WebChallenger is a new web agent framework that achieves strong performance across multiple benchmarks using open-weight models without fine-tuning, by replicating human cognitive advantages through architecture design rather than model scale.
Aitor built new browser infrastructure from scratch using custom chromium fork, firecracker VMs, and custom Linux kernel, achieving 3x cheaper cost, unlimited scaling, and fastest performance for browser automation. Now available at $0.02/hour.
Microsoft has released a terminal-native Web Agent framework called Webwright, which automates web operations by having LLMs write Playwright scripts. It features a minimal architecture and state-of-the-art performance, with support for multiple model backends and product integrations.
PANDO is a web agent framework that improves efficiency through online skill distillation, reducing token usage by 58-61% while outperforming baselines on VisualWebArena tasks.
WebWatcher is a multimodal agent for deep research that uses synthetic trajectories and reinforcement learning to achieve superior performance in complex visual and textual information retrieval tasks. The paper also introduces BrowseComp-VL, a new benchmark for evaluating multimodal agents.