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The article discusses challenges in training browser-agent models for sequential decision-making, such as error recovery and memory, and seeks input on optimizing training objectives. The author mentions working on the 'mako' model at tinyfish and invites community feedback.
AgentJourney is a free tool that enables testing of how AI agents interact with products by running intents on websites, providing real-time insights into their journey, token usage, and pricing.
Browser Use CLI is a browser harness that enables AI agents to run complete web browsing workflows via scripts, improving efficiency and reducing costs for web tasks.
Browser Use Cloud v4 introduces a web agent platform that claims to solve accuracy and cost constraints, making reliable web agents at scale feasible, with $15 free credits offered to try.
Microsoft AI Frontiers introduces Web Skill Factory, a pipeline that converts solved web tasks into reusable, verified code programs. Reusing the library on a WebArena subset with gpt-5.4 boosts held-out accuracy from 55% to 70% and reduces average steps from 17.1 to 14.7.
PersonaTrail is a benchmark for personalized web agents that uses realistic browsing trajectories to evaluate agents' ability to infer user preferences and recall past information. The paper also proposes PACMem, a memory framework that outperforms existing baselines on both tasks.
This article breaks down the five infrastructure layers required to run production web agents beyond just a browser, covering warm pools, isolation, identity, observability, and model gateways, and discusses when it makes sense to build vs. buy.
DeepSearch-Evolve introduces a self-distillation framework for web agents using a verifiable environment (DeepSearch-World) with 420K multi-hop QA tasks, achieving competitive performance without distillation from stronger models.
Introduces Ko-WideSearch, a Korean breadth-search benchmark for web agents that evaluates exhaustive set enumeration across 228 tables. Findings show agents have high item recall but struggle with row completion, especially for open-ended cells.
This paper introduces SkillMigrator, an LLM web agent that learns reusable skills and transfers them across websites by matching layout structure rather than domain-specific metadata, reducing LLM action count by 8-10% on WebArena and Mind2Web benchmarks.
This paper introduces SkillMigrator, an agent that learns reusable web skills as transferable interaction patterns (TIPs) and transfers them across websites by matching layout structure, reducing LLM action counts by 8-10% on benchmarks.
The browser_use team achieved the #1 spot on the Odysseys benchmark, a challenging evaluation for long-horizon web agents, outperforming models like Opus 4.6 and GPT-5.4.
This paper investigates whether online skill and memory modules for web agents are worth their token cost under a fixed inference budget, finding that a budget-matched vanilla baseline often matches or outperforms augmented methods across three domains and models.
This paper introduces WebDecept, a framework for injecting deceptive interface patterns into web environments to evaluate the safety of autonomous web agents. Experiments show current agents are highly susceptible to such manipulations, highlighting safety challenges for real-world deployment.
The paper proposes Signal-Driven Observation (SDO), a method for web agents to avoid context degradation by only reading task-relevant parts of the DOM and re-invoking observation only when triggered by specific signals, rather than reading the full page state at every action step.
AsyncWebRL introduces an asynchronous multi-step reinforcement learning system for vision-language web agents, achieving up to 2.9x training speedup and setting a new state-of-the-art on WebGym by replacing per-trajectory normalization with a constant to reduce trajectory length inefficiency.
SlimSearcher is a framework that improves efficiency in deep research agents by combining Pareto-efficient trajectory filtering and adaptive reward shaping, reducing tool-call rounds by 17-58% while maintaining accuracy on benchmarks like GAIA, BrowseComp, and XBenchDeepSearch.
This paper proposes SGDR (State-Grounded Dynamic Retrieval), an online skill learning method for web agents that enables stepwise, state-aware skill reuse rather than static task-level retrieval. Experiments on WebArena show SGDR achieves 37.5% success rate with GPT-4.1, a ~10.6% relative gain over strong baselines.
Proposes SCALE, a framework for self-improving web agents using cognitive-aware exploration with three adversarial roles and a graph exploration strategy. Also introduces a large-scale dataset SCALE-20k from real websites, showing significant improvements in MLLM-based web agents.
OpenWebRL presents an open framework for training visual web agents using online multi-turn reinforcement learning on real websites, achieving state-of-the-art performance with minimal initial supervision. Their 4B-parameter model outperforms prior open agents and competes with proprietary systems like OpenAI CUA and Gemini CUA.