Rethinking Code Similarity for Automated Algorithm Design with LLMs
arXiv:2603.02787v1 Announce Type: new Abstract: The rise of Large Language Model-based Automated Algorithm Design (LLM-AAD) has transformed algorithm development by autonomously generating code implementations of expert-level algorithms. Unlike traditional expert-driven algorithm development, in the LLM-AAD paradigm, the main design principle behind an algorithm is often implicitly embedded in the generated code. Therefore, assessing algorithmic similarity directly from code, distinguishing genuine algorithmic innovation from mere syntactic variation, becomes essential. While various code similarity metrics exist, they fail to capture algorithmic similarity, as they focus on surface-level syntax or output equivalence rather than the underlying algorithmic logic. We propose BehaveSim, a novel method to measure algorithmic similarity through the lens of problem-solving behavior as a sequence of intermediate solutions produced during execution, dubbed as problem-solving trajectories (P
arXiv:2603.02787v1 Announce Type: new Abstract: The rise of Large Language Model-based Automated Algorithm Design (LLM-AAD) has transformed algorithm development by autonomously generating code implementations of expert-level algorithms. Unlike traditional expert-driven algorithm development, in the LLM-AAD paradigm, the main design principle behind an algorithm is often implicitly embedded in the generated code. Therefore, assessing algorithmic similarity directly from code, distinguishing genuine algorithmic innovation from mere syntactic variation, becomes essential. While various code similarity metrics exist, they fail to capture algorithmic similarity, as they focus on surface-level syntax or output equivalence rather than the underlying algorithmic logic. We propose BehaveSim, a novel method to measure algorithmic similarity through the lens of problem-solving behavior as a sequence of intermediate solutions produced during execution, dubbed as problem-solving trajectories (PSTrajs). By quantifying the alignment between PSTrajs using dynamic time warping (DTW), BehaveSim distinguishes algorithms with divergent logic despite syntactic or output-level similarities. We demonstrate its utility in two key applications: (i) Enhancing LLM-AAD: Integrating BehaveSim into existing LLM-AAD frameworks (e.g., FunSearch, EoH) promotes behavioral diversity, significantly improving performance on three AAD tasks. (ii) Algorithm analysis: BehaveSim clusters generated algorithms by behavior, enabling systematic analysis of problem-solving strategies--a crucial tool for the growing ecosystem of AI-generated algorithms. Data and code of this work are open-sourced at https://github.com/RayZhhh/behavesim.
Executive Summary
This article introduces BehaveSim, a novel method to measure algorithmic similarity by analyzing problem-solving behavior as a sequence of intermediate solutions produced during execution. Unlike existing code similarity metrics, BehaveSim captures the underlying algorithmic logic, distinguishing genuine innovation from syntactic variation. The authors demonstrate BehaveSim's utility in enhancing LLM-AAD frameworks and algorithm analysis. While the proposed method shows promise, its effectiveness in complex scenarios and scalability needs further investigation. The open-sourcing of BehaveSim's code and data enables the broader AI and algorithm development communities to build upon this work.
Key Points
- ▸ BehaveSim is a novel method to measure algorithmic similarity based on problem-solving behavior.
- ▸ The method captures the underlying algorithmic logic, distinguishing genuine innovation from syntactic variation.
- ▸ BehaveSim is demonstrated to enhance LLM-AAD frameworks and facilitate algorithm analysis.
Merits
Strength in capturing algorithmic similarity
BehaveSim's focus on problem-solving behavior enables the identification of underlying algorithmic logic, addressing the limitations of existing code similarity metrics.
Enhancement of LLM-AAD frameworks
The integration of BehaveSim into existing LLM-AAD frameworks promotes behavioral diversity, improving performance on AAD tasks.
Facilitation of algorithm analysis
BehaveSim clusters generated algorithms by behavior, enabling systematic analysis of problem-solving strategies and promoting the growth of AI-generated algorithms.
Demerits
Limitation in complex scenarios
The effectiveness of BehaveSim in complex scenarios and its scalability need further investigation to ensure its practical applicability.
Potential overfitting
The reliance on dynamic time warping (DTW) for quantifying alignment between problem-solving trajectories may lead to overfitting, especially in cases with limited training data.
Expert Commentary
BehaveSim is a significant contribution to the field of algorithm development, addressing the long-standing challenge of capturing algorithmic similarity. While the proposed method shows promise, its effectiveness in complex scenarios and scalability need further investigation. The open-sourcing of BehaveSim's code and data enables the broader AI and algorithm development communities to build upon this work. As BehaveSim continues to evolve, it is essential to consider its potential implications on the development and deployment of AI-generated algorithms, including the need for policy adjustments to ensure accountability and transparency.
Recommendations
- ✓ Further investigation into BehaveSim's effectiveness in complex scenarios and scalability is necessary to ensure its practical applicability.
- ✓ The AI and algorithm development communities should explore the potential applications of BehaveSim in various domains, including robotics, finance, and healthcare.