The Challenge of LLM-Based Program Evolution
Traditional evolutionary algorithms for program synthesis often treat each task as an isolated problem, failing to capitalize on the structural knowledge gained from solving previous, related tasks. While LLMs are capable of generating code, they often struggle with long-term optimization in iterative evolutionary loops, leading to redundant search efforts and inefficient exploration of the program space.
The ε-MemEvo Mechanism
ε-MemEvo introduces an adaptive cross-task memory transfer framework designed to bridge this gap. Instead of relying solely on the current task's feedback, the system maintains a persistent memory bank of high-performing code snippets and algorithmic strategies.
Key components include:
- Adaptive Memory Selection: The system evaluates the semantic similarity between the current task and historical successes, dynamically retrieving relevant code patterns to seed the evolutionary process.
- Cross-Task Transfer: By injecting proven logic from previous domains into the current search space, the model bypasses the 'cold start' problem, allowing the evolutionary loop to focus on refinement rather than basic structural discovery.
- Evolutionary Efficiency: By leveraging this memory, ε-MemEvo reduces the number of LLM calls required to reach optimal solutions, effectively lowering the computational cost of iterative program improvement.
Impact on Search and Optimization
By treating code as an evolvable artifact that benefits from collective historical intelligence, ε-MemEvo demonstrates that LLMs can perform more robustly in complex, multi-task environments. The adaptive nature of the memory transfer ensures that the model does not simply copy-paste old code, but rather adapts successful patterns to the constraints of new, unseen tasks. This approach significantly accelerates convergence rates in program synthesis benchmarks compared to standard, non-transferable evolutionary baselines.