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aideep-diveDeep Research 前沿

Data Synthesis: WebShaper & S1-DeepResearch

Previous articles covered how to train agents. But training requires high-quality data—and deep research training data has been scarce. WebShaper solves this with mathematical formalization: define IS tasks in set theory, then use an agentic Expander to iteratively expand them. S1-DeepResearch goes further: moves training from 'search-centric' to 'real research.'

#deep-research#data-synthesis#webshaper#s1-deepresearch#formalization#iclr2026
aideep-diveDeep Research 前沿

WebWeaver & DeepPlanner: Dual-Agent Architecture and Planning Optimization

Previous articles covered training from scratch and long-horizon memory. This one goes deeper: how to make the agent's 'planning' itself better? WebWeaver tackles it architecturally (dual-agent iterative outline optimization). DeepPlanner tackles it through training (advantage shaping for planning tokens). Both point to the same conclusion: planning is the ceiling of deep research.

#deep-research#planning#dual-agent#advantage-shaping#webweaver#deepplanner#iclr2026#acl2026