arXiv:2605.31365v1 Announce Type: new Abstract: Recent advances in Multimodal Large Language Models (MLLMs) have led to promising progress in web agents. However, existing web agents often rely on handcrafted execution pipelines or expensive expert trajectories, limiting their adaptability to complex, dynamic environments. To address these challenges, we propose SCALE (Self-Cognitive-Aware Learning and Exploration), which leverages three adversarial roles, Selector, Predictor, and Judger to autonomously discover the agent's limitations and expand its cognitive boundaries through environmental
The proliferation of advanced MLLMs is pushing the boundaries of autonomous agent capabilities, necessitating improvements in adaptability for real-world application.
Self-improving web agents represent a significant step towards more autonomous and versatile AI, reducing reliance on human intervention and specialized training data.
AI agents will become more robust and adaptable in dynamic online environments, diminishing the need for constant retraining and handcrafted pipelines.
- · AI development companies
- · Businesses adopting automation
- · Software developers
- · Tasks requiring repetitive web interaction
- · Legacy automation solutions
Web agents can perform complex online tasks with greater independence and less human oversight.
This development could accelerate the automation of white-collar workflows and reduce operational costs across various industries.
Increased agent autonomy might lead to new ethical and regulatory challenges regarding online behavior and decision-making by AI.
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Read at arXiv cs.AI