Research Analysis
Unlocking Algorithm Insights for Enterprise AI
Our latest research introduces EMOC, an Evaluation-Memory-Operations-Complexity framework designed to quantify algorithm similarity. By embedding algorithm implementations into a rich feature space, EMOC facilitates advanced tasks like clone detection, program synthesis, and benchmarking LLM-generated code diversity. This offers enterprises a robust method to analyze, compare, and optimize their algorithmic assets with unprecedented precision.
Quantifiable Impact
Dive into the measurable benefits of leveraging our EMOC framework for your enterprise's algorithmic challenges.
79.1%
Accuracy in Algorithm Classification
350+
Curated Python Algorithm Implementations
3
Core Algorithmic Problems Analyzed
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The Challenge of Algorithm Similarity
Determining if two algorithms for the same problem are 'meaningfully different' is a theoretically uncomputable problem. Empirically, it's complicated by various similarity notions, from functional equivalence to instruction-level identity. This complexity necessitates a pragmatic, consistent metric for real-world applications.
Uncomputable In Full Generality| Algorithm Change | Encoding Equivalence | AST Equivalence | Instructional Equivalence | Functional Equivalence |
|---|---|---|---|---|
| Rename variables | Not maintained | Maintained | Maintained | Maintained |
| Add extraneous calculation | Not maintained | Not maintained | Compiler dependent | Maintained |
| Store intermediate variables | Not maintained | Not maintained | Compiler dependent | Maintained |
| Change order of associative operations | Not maintained | Not maintained | Not maintained | Maintained |
| Change variable precision | Maintained | Maintained | Maintained | Not maintained |
EMOC Framework: From Code to Embeddings
The EMOC framework systematically transforms algorithm implementations into a comprehensive numeric vector. This process involves evaluating functional correctness, memory and runtime scaling behavior, and counting primitive operations, creating a feature space for similarity analysis.
Algorithm Implementation
→
Input Sampling & Execution
→
E-Component (Functional Equivalence)
→
M-Component (Memory Scaling)
→
O-Component (Operations Count)
→
C-Component (Runtime Scaling)
→
EMOC Numeric Embedding
PACD: A Curated Dataset for Algorithm Similarity
To validate EMOC, we compiled PACD (Python Algorithm Classification Dataset), comprising over 350 verified Python implementations across three problems: list sorting, primality testing, and list search. This dataset serves as a crucial benchmark for developing and testing algorithm similarity metrics.
350+ Verified Python ImplementationsLLM-Generated Code Diversity
EMOC proves highly effective in understanding the diversity of algorithms generated by Large Language Models. By analyzing the EMOC embedding, we can discern subtle differences that indicate true algorithmic novelty, enabling better fine-tuning and evaluation of generative AI for code.
Quantifying Novelty in Sorting Algorithms
Problem: Given two algorithms for the same problem, can we determine whether they are meaningfully different?
Solution: We demonstrate EMOC's utility in classifying human-written and LLM-generated sorting algorithms. By analyzing EMOC scores, we can detect potentially novel algorithms and quantify diversity in LLM outputs, offering insights beyond simple temperature scaling. The framework supports better understanding and guiding generative models for unique solutions.
Outcome: EMOC allows for a nuanced comparison of algorithms, supporting the discovery of novel approaches and robust evaluation of LLM performance in code generation.
- Clustering of algorithm types based on EMOC features.
- Detection of near-duplicate implementations.
- Quantification of diversity in LLM-generated programs.
Advanced ROI Calculator
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Your AI Implementation Roadmap
Our structured approach ensures a seamless integration of advanced AI solutions, tailored to your enterprise's unique needs.
Discovery & Strategy
Comprehensive analysis of existing systems and identification of key algorithmic optimization opportunities.
EMOC Integration & Benchmarking
Applying the EMOC framework to baseline current algorithms and identify areas for improvement.
Solution Design & Prototyping
Developing and testing custom AI solutions based on EMOC insights and enterprise requirements.
Deployment & Optimization
Seamless integration into your production environment, followed by continuous monitoring and performance tuning.
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