When Algorithmic Exploration Becomes Cheap: A Case Study of Agentic Research in EDA
As EDA researchers, we conducted eight deliberate trials of agentic algorithm exploration, selecting several topics outside our areas of depth.
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Key points
- With limited intervention in the algorithms, agents developed mathematical constructions, analyzed existing tools, and implemented improvements; some efforts fell short of their practical goals.
- We also used AI to collect, classify, and analyze 8,420 papers from four EDA conferences and two journals over 2022-2026.
- Among 2,380 primary-core papers, we classified 97.7% from titles and abstracts as computationally closed, including work on new formulations.
- Together, these observations suggest that much of EDA offers an executable environment for increasingly accessible algorithm research.
Sources (1)
- [1]When Algorithmic Exploration Becomes Cheap: A Case Study of Agentic Research in EDAarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 02:10 PM
As EDA researchers, we conducted eight deliberate trials of agentic algorithm exploration, selecting several topics outside our areas of depth.
With limited intervention in the algorithms, agents developed mathematical constructions, analyzed existing tools, and implemented improvements; some efforts fell short of their practical goals.
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