Cognitive-YOLO: LLM-Driven Architecture Synthesis from First Principles of Data for Object Detection

• URL: http://arxiv.org/abs/2512.12281v1
• Date: Sat, 13 Dec 2025 10:52:54 GMT
• Title: Cognitive-YOLO: LLM-Driven Architecture Synthesis from First Principles of Data for Object Detection
• Authors: Jiahao Zhao,
• Abstract summary: We propose Cognitive-YOLO, a novel framework for Large Language Models (LLMs)-driven architecture synthesis.<n>Our method consists of three stages: first, an analysis module extracts key meta-features from the target dataset.<n>Second, the LLM reasons upon these features, augmented with state-of-the-art components retrieved via Retrieval-Augmented Generation (RAG), to synthesize the architecture into a structured Neural Architecture Description Language (NADL)<n>Third, a compiler instantiates this description into a deployable model.
• Score: 3.5554162308775408
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Designing high-performance object detection architectures is a complex task, where traditional manual design is time-consuming and labor-intensive, and Neural Architecture Search (NAS) is computationally prohibitive. While recent approaches using Large Language Models (LLMs) show promise, they often function as iterative optimizers within a search loop, rather than generating architectures directly from a holistic understanding of the data. To address this gap, we propose Cognitive-YOLO, a novel framework for LLM-driven architecture synthesis that generates network configurations directly from the intrinsic characteristics of the dataset. Our method consists of three stages: first, an analysis module extracts key meta-features (e.g., object scale distribution and scene density) from the target dataset; second, the LLM reasons upon these features, augmented with state-of-the-art components retrieved via Retrieval-Augmented Generation (RAG), to synthesize the architecture into a structured Neural Architecture Description Language (NADL); finally, a compiler instantiates this description into a deployable model. Extensive experiments on five diverse object detection datasets demonstrate that our proposed Cognitive-YOLO consistently generates superior architectures, achieving highly competitive performance and demonstrating a superior performance-per-parameter trade-off compared to strong baseline models across multiple benchmarks. Crucially, our ablation studies prove that the LLM's data-driven reasoning is the primary driver of performance, demonstrating that a deep understanding of data "first principles" is more critical for achieving a superior architecture than simply retrieving SOTA components.

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