In the world of binary analysis, code similarity detection has traditionally been a field where accuracy and interpretability rarely go hand in hand. Classic methods based on hand-crafted features — such as operation ratios or instruction frequencies — offered transparency, but their shallowness made them insufficient for complex tasks like vulnerability identification across different architectures or optimization levels. On the other hand, modern embedding-based approaches have achieved robustness and generalization, but at the cost of turning code into opaque vectors that hinder quick verification and scaling without precision loss. This dilemma has led the community to ask a fundamental question: is it possible to obtain representations that are at once interpretable, scalable, and accurate?
The answer seems to come from a hybrid approach that combines language models with structured reasoning agents. Instead of relying solely on predefined features or vector representations, a system is proposed that analyzes assembly code by extracting semantic elements such as input/output types, side effects, notable constants, and even algorithmic intent. These features, generated by a language-based agent, are much richer than manually crafted ones and, at the same time, fully human-readable. This allows security and development teams to understand which part of the code is being compared, why it is considered similar, and how it relates to other fragments, facilitating audits and maintenance tasks.
From a practical standpoint, the implications are enormous. For a company like Q2BSTUDIO, which specializes in custom software development, having tools that not only identify similar binary code but also explain the reasoning behind that identification means a qualitative leap in cybersecurity and software quality. For example, when analyzing binaries from different versions of the same product or from different platforms (x86, ARM, etc.), AI agents can generate reports that detail not only that two functions are equivalent, but that both perform the same cryptographic operation, or that they share a suspicious data leakage behavior. This is especially relevant in environments where traceability and justification are regulatory or compliance requirements.
Scalability is not left behind. Unlike embeddings, which require expensive nearest-neighbor searches in high-dimensional spaces — and often need approximations that reduce precision — interpretable features can be indexed using inverted or relational indexes, enabling fast and exact searches. According to available data, without any prior training, this method achieves a recall@1 of 42% in cross-architecture tasks and 62% in cross-optimization, comparable to trained embedding methods (39% and 34% respectively). And when combined with embeddings, it significantly outperforms the state of the art, demonstrating that the coexistence of precision, scalability, and interpretability is possible.
For a company working in the field of cybersecurity, this ability to fuse the best of both worlds has incalculable strategic value. Pentesting and malware analysis teams need to understand not only which code is malicious, but why it is. With interpretable features, dashboards can be built that link suspicious binaries to already cataloged similar functions, and in turn feed AI systems that automate classification. Moreover, integration with cloud platforms like AWS or Azure allows these agents to be deployed as serverless services, scaling on demand and reducing operational costs.
Another key aspect is the synergy with Business Intelligence tools. By converting binary code analysis into structured data sets (types, constants, effects), BI and Power BI techniques can be applied to generate visibility on code evolution, reuse patterns, or the propagation of vulnerabilities over time. This transforms a traditionally isolated technical process into a business intelligence asset.
At Q2BSTUDIO, we understand that innovation does not consist only in adopting the latest technology, but in making it useful and understandable for decision makers. That is why our approach combines AI agents with custom software engineering, integrating cybersecurity, cloud, and BI solutions into a coherent ecosystem. Whether for analyzing binaries from IoT devices, mobile applications, or legacy software, having interpretable features allows developers and analysts to work confidently, knowing that every decision is backed by clear and traceable data.
In short, the future of binary code similarity detection is not about choosing between embeddings or hand-crafted features, but about a third path that leverages the power of language models to generate semantic descriptions. These descriptions, being directly readable and searchable, remove the opacity barrier and open the door to a new generation of reverse engineering tools that are smarter, faster, and more reliable. And while this technology matures, Q2BSTUDIO is already working to incorporate it into its services of development, cybersecurity, and artificial intelligence, offering its clients a differential value in every project.
