Web-CogReasoner: Towards Knowledge-Induced Cognitive Reasoning for Web
  Agents
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
Procedural. In this framework, knowledge content learning corresponds to the
agent's processes of Memorizing and Understanding, which rely on the first two
knowledge types, representing the "what" of learning. Conversely, cognitive
processes correspond to Exploring, grounded in Procedural knowledge, defining
the "how" of reasoning and action. To facilitate knowledge acquisition, we
construct the Web-CogDataset, a structured resource curated from 14 real-world
websites, designed to systematically instill core knowledge necessary for web
agent. This dataset serves as the agent's conceptual grounding-the "nouns" upon
which comprehension is built-as well as the basis for learning how to reason
and act. Building on this foundation, we operationalize these processes through
a novel knowledge-driven Chain-of-Thought (CoT) reasoning framework, developing
and training our proposed agent, the Web-CogReasoner. Extensive experimentation
reveals its significant superiority over existing models, especially in
generalizing to unseen tasks where structured knowledge is decisive. To enable
rigorous evaluation, we introduce the Web-CogBench, a comprehensive evaluation
suite designed to assess and compare agent performance across the delineated
knowledge domains and cognitive capabilities. Our code and data is open sourced
at https://github.com/Gnonymous/Web-CogReasoner
689326568da45ffb0a2b2598
Yuhan Guo
claimed_verified
2025-08-06T19:14:35.040Z
67189ab6034e7dc93591fcfa
https://cdn-avatars.huggingface.co/v1/production/uploads/67189ab6034e7dc93591fcfa/jeLp-_pdEDzGDsSyKuAks.png
Eohan G
user
Gnonymous
689326568da45ffb0a2b2599
Cong Guo
689326568da45ffb0a2b259a
Aiwen Sun
689326568da45ffb0a2b259b
Hongliang He
689326568da45ffb0a2b259c
Xinyu Yang
689326568da45ffb0a2b259d
Yue Lu
689326568da45ffb0a2b259e
Yingji Zhang
689326568da45ffb0a2b259f
Xuntao Guo
689326568da45ffb0a2b25a0
Dong Zhang
689326568da45ffb0a2b25a1
Jianzhuang Liu
689326568da45ffb0a2b25a2
Jiang Duan
689326568da45ffb0a2b25a3
Yijia Xiao
claimed_verified
2025-08-18T06:59:49.673Z
619b6a24030b707ff1f05dee
https://cdn-avatars.huggingface.co/v1/production/uploads/619b6a24030b707ff1f05dee/8FZjePfOuTtN3uvTmaEg5.jpeg
Yijia Xiao
user
Yijia-Xiao
689326568da45ffb0a2b25a4
Liangjian Wen
689326568da45ffb0a2b25a5
Hai-Ming Xu
689326568da45ffb0a2b25a6
Yong Dai
Web-CogReasoner: Towards Knowledge-Induced Cognitive Reasoning for Web
  Agents
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
Procedural. In this framework, knowledge content learning corresponds to the
agent's processes of Memorizing and Understanding, which rely on the first two
knowledge types, representing the "what" of learning. Conversely, cognitive
processes correspond to Exploring, grounded in Procedural knowledge, defining
the "how" of reasoning and action. To facilitate knowledge acquisition, we
construct the Web-CogDataset, a structured resource curated from 14 real-world
websites, designed to systematically instill core knowledge necessary for web
agent. This dataset serves as the agent's conceptual grounding-the "nouns" upon
which comprehension is built-as well as the basis for learning how to reason
and act. Building on this foundation, we operationalize these processes through
a novel knowledge-driven Chain-of-Thought (CoT) reasoning framework, developing
and training our proposed agent, the Web-CogReasoner. Extensive experimentation
reveals its significant superiority over existing models, especially in
generalizing to unseen tasks where structured knowledge is decisive. To enable
rigorous evaluation, we introduce the Web-CogBench, a comprehensive evaluation
suite designed to assess and compare agent performance across the delineated
knowledge domains and cognitive capabilities. Our code and data is open sourced
at https://github.com/Gnonymous/Web-CogReasoner
A framework for web agents decomposes their capabilities into knowledge content learning and cognitive processes, using a structured dataset and a novel reasoning framework to enhance generalization and performance.
Web-CogKnowledge Framework Factual knowledge Conceptual knowledge Procedural knowledge Memorizing Understanding Exploring Web-CogDataset Chain-of-Thought (CoT) reasoning Web-CogReasoner Web-CogBench