EvoSyn organizes agentic data synthesis around a synthesis space: a structured, revisable set of controllable interaction factors that serves as both a diagnostic coordinate system and a generation control surface.
Agentic data synthesis improves by evolving the generation space based on learner feedback
EvoSyn closes the feedback-to-decision gap by reallocating synthesis effort within existing factors and expanding the space when weaknesses fall outside it.
Big Tech
Wei Yang · Qing Ping · Youzhi Luo · Yawei Wang · Abhishek Kumar · Panpan Xu · +1 more
University of Southern California · AWS AI
Research Digest··3 min read
The authors introduce EvoSyn, a multi-agent framework in which the space of controllable interaction factors for synthetic data is treated as a revisable structure driven by task-agent feedback.
Why this paper
From AWS AI and University of Southern California
In one line
EvoSyn evolves a revisable synthesis space using learner feedback, reallocating and expanding generation controls, and improves agent task success by 13.3 points.
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