arXiv:2606.17645v1 Announce Type: cross Abstract: Large language model (LLM) web agents are usually deployed as tool callers: each turn, the model reads a fresh page observation and emits one structured tool action. When every action is a low-level primitive, horizons grow quickly and so do policy-facing LLM completions, dominating latency and cost on benchmarks such as Mind2Web and WebArena. Recent systems therefore wrap repeated interaction fragments as web skills: callable tools built from successful trajectories or induced programs, so one call can replace several primitives. However, prio
The accelerating development of large language models and their application as web agents necessitates more efficient interaction paradigms to overcome limitations of latency and cost.
This research directly addresses the core challenges in scaling LLM-driven web agents, impacting their viability for broad enterprise and consumer applications.
The shift from low-level primitive actions to reusable 'web skills' fundamentally changes how LLMs interact with digital environments, making agentic systems more efficient and robust.
- · AI agent developers
- · Cloud computing providers (reduced inference cost)
- · Enterprises adopting AI agents
- · Open-source AI communities
- · Inefficient LLM agent architectures
- · Companies reliant on primitive API interactions
More capable and cost-effective AI agents become widespread, automating complex online tasks.
Reduced operational costs and increased efficiency lead to faster development and deployment cycles for AI agent solutions across various industries.
The enhanced performance of web agents could accelerate the collapse of certain white-collar workflows and the SaaS layer, leading to significant economic restructuring.
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Read at arXiv cs.CL