WebDART: Dynamic Decomposition and Re-planning for Complex Web Tasks

Large-language-model (LLM) agents are becoming competent at straightforward web tasks, such as opening an item page or submitting a form, but still struggle with objectives that require long-horizon navigation, large-scale information extraction, and reasoning under constraints. DART, a general framework that enables a single LLM to handle such complex chores. DART (i) dynamically decomposes each objective into three focused sub-tasks--navigation, information extraction, and execution--so the model concentrates on one skill at a time, and (ii) continuously re-plans the decomposition as new webpages are revealed, taking advantage of newly discovered filters or shortcuts and avoiding redundant exploration. LLM-powered web agents have recently shown promising abilities in web navigation tasks (Drouin et al., 2024; He et al., 2024; Wei et al., 2025; Y ang et al., 2024a; Pan et al., 2024; Song et al., 2024). Benchmarks such as WebArena (Zhou et al., 2023) demonstrate that these agents achieve reasonable accuracy on simple objectives, highlighting their potential as general-purpose automation tools. However, when the objectives require more complex reasoning and multi-step exploration, the performance of these agents often collapses. As shown in Figure 1, on WebChoreArena (Miyai et al., 2025), a benchmark designed to test higher-complexity web tasks, agents powered by GPT -4o achieve only 8.0% accuracy on tasks across different web domains, far below the 46.6% accuracy on WebArena. This gap highlights a critical weakness of current worflows: while sufficient for simple goals, they are not well equipped for tasks demand multi-step reasoning, long-horizon navigation, and structured information processing. A closer examination reveals that the difficulty arises from cognitive overload. Complex tasks require agents to simultaneously navigate across multiple web pages, extract and track large amounts of information, and reason under constraints. Consider the following task from WebChore-Arena (Miyai et al., 2025): "T ell me the top 3 products with the highest number of reviews in Home Audio of Electronics within the price range of $1,000 to $9,999". As illustrated in Figure 1, product information is distributed across multiple nested web pages. Each page may contain tens of products with attributes such as price and number of reviews.