DeltaEvolve: Accelerating Scientific Discovery through Momentum-Driven Evolution
Jiachen Jiang, Tianyu Ding, Zhihui Zhu

TL;DR
DeltaEvolve introduces a momentum-driven evolutionary framework that uses semantic deltas instead of full-code histories, enabling more efficient and effective scientific discovery with less token consumption.
Contribution
It proposes a novel semantic delta approach within an EM framework, improving over full-code histories for guiding evolution in scientific discovery tasks.
Findings
Achieves better solutions with less token consumption.
Outperforms full-code-based evolutionary agents across diverse tasks.
Demonstrates efficiency and effectiveness in scientific discovery.
Abstract
LLM-driven evolutionary systems have shown promise for automated science discovery, yet existing approaches such as AlphaEvolve rely on full-code histories that are context-inefficient and potentially provide weak evolutionary guidance. In this work, we first formalize the evolutionary agents as a general Expectation-Maximization framework, where the language model samples candidate programs (E-step) and the system updates the control context based on evaluation feedback (M-step). Under this view, constructing context via full-code snapshots constitutes a suboptimal M-step, as redundant implement details dilutes core algorithmic ideas, making it difficult to provide clear inspirations for evolution. To address this, we propose DeltaEvolve, a momentum-driven evolutionary framework that replaces full-code history with structured semantic delta capturing how and why modifications between…
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Taxonomy
TopicsEvolutionary Algorithms and Applications · Machine Learning in Materials Science · Scientific Computing and Data Management
