arXiv:2508.01858v3 Announce Type: replace-cross Abstract: Multimodal large-scale models have significantly advanced the development of web agents, enabling perception and interaction with digital environments akin to human cognition. In this paper, we argue that web agents must first acquire sufficient knowledge to effectively engage in cognitive reasoning. Therefore, we decompose a web agent's capabilities into two essential stages: knowledge content learning and cognitive processes. To formalize this, we propose Web-CogKnowledge Framework, categorizing knowledge as Factual, Conceptual, and P
This paper represents a focused effort to integrate knowledge acquisition with cognitive reasoning in web agents, a critical next step as general multimodal models reach performance plateaus.
Sophisticated web agents with enhanced cognitive reasoning, formalized by frameworks like Web-CogKnowledge, are essential for automating complex digital tasks and reducing human intervention.
The development pathway for AI agents will increasingly prioritize explicit knowledge modeling and cognitive architectures, moving beyond pure statistical pattern matching.
- · AI software developers
- · Enterprise automation platforms
- · Data annotation services
- · Cloud providers
- · Legacy RPA providers
- · Human-in-the-loop content moderation
- · Simple rule-based automation systems
More capable and autonomous AI agents will emerge, reducing the need for human oversight in mundane digital tasks.
The economic value shifts from human data entry and task execution to AI system design, training, and oversight.
This could lead to a restructuring of white-collar labor, as many knowledge-based tasks become fully automatable by advanced web agents.
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Read at arXiv cs.AI