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AI startup Hark launches Handoff, a computer use agent that autonomously navigates the web, claiming top benchmark scores and significantly lower token prices than frontier models.
StepGuard proposes a framework combining Dynamic Dual-Policy Optimization (DDPO) and Confidence-Guided Adaptive Navigation Reflection (CANR) to address reward misalignment and error propagation in web navigation agents, achieving state-of-the-art performance.
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.
This paper proposes a multi-agent computer use (MACU) system that uses a manager model to decompose tasks into directed acyclic graphs for parallel execution by subagents. It demonstrates consistent improvements over single-agent baselines on multiple benchmarks and better test-time scaling.
This paper introduces GTA, a scalable framework for automatically generating long-horizon, multi-hop web agent tasks with executable trajectories, addressing the lack of process-level supervision in web agent benchmarks. The framework integrates crawling, retrieval-based seeding, and automated quality control to produce realistic tasks across multiple websites.
StableBrowse is a new browser layer for AI agents that reduces token usage by 70% and speeds up execution by 3-4x by converting websites into reusable execution graphs.
The author describes observing an AI agent autonomously complete a competitive intelligence workflow that normally takes 4 hours in just 18 minutes, highlighting its ability to navigate websites, process PDFs, and iterate search, suggesting a move beyond simple chatbots to autonomous execution.
MemGym is a benchmark for evaluating memory formation in LLM agents over long-horizon tasks, unifying existing agent gyms and synthetic pipelines with memory-isolated scores. It spans tool-use dialogue, multi-turn search, coding, and computer use, and includes a lightweight reward model (MemRM) for efficient evaluation.
The article explains how Manus's Browser Operator works by operating inside the user's authorized local browser session, allowing it to access subscription-based and authenticated content beyond typical AI search capabilities, and provides a step-by-step guide for enabling and using it.
Purdue researchers introduce Mango, a multi-agent web navigation system that uses global site structure and Thompson Sampling to pick optimal starting URLs, boosting success rates on WebVoyager and WebWalkerQA benchmarks.
This paper proposes a method to train LLM agents with intrinsic meta-evolution capabilities, enabling spontaneous self-improvement without external rewards at inference time. Applied to Qwen3-30B and Seed-OSS-36B, the approach yields a 20% performance boost on web navigation benchmarks, with a 14B model outperforming Gemini-2.5-Flash.
OpenAI has partnered with Be My Eyes to use GPT-4 for visual accessibility, enabling blind and low-vision users to navigate websites, e-commerce platforms, and physical spaces through intelligent summarization and real-time guidance. The system leverages GPT-4's vision capabilities to identify important content and provide contextual assistance that mimics how sighted users naturally scan information.