CogSynth: From Data Diagnosis to Production-Ready Scientific Software

Accelerating scientific discovery is hindered by the ``Triple Chasm''---the cognitive, methodological, and implementation divides separating raw data from valid computational solutions. We introduce the \textbf{Cognitive Synthesis Framework (CogSynth)}, an autonomous system that bridges these divides through two synergistic phases: Cognition (diagnosing data pathologies) and Synthesis (architecting executable software). Unlike rigid coding assistants, CogSynth functions as a ``Research Architect'' capable of reframing problem spaces. In geometric optimization, it autonomously derived symmetry constraints (first principles) from raw objectives. For pathological long-tailed data (CIFAR-100), it diagnosed imbalance and synthesized a composite architecture, achieving competitive accuracy. Furthermore, in symbolic regression, it discovered interpretable physical laws with a $>50$-fold reduction in token consumption compared to state-of-the-art LLM methods, identifying critical physical constraints like zero-inflation. This work demonstrates a paradigm shift from human-guided trial-and-error to autonomous methodological invention.