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Tesuji theory, the study of skillful tactical moves in Go, has evolved through distinct paradigms that reflect broader shifts in the game's strategic understanding. The earliest major framework, the Classical Tesuji Tradition, emerged from the Japanese house system during the Edo period. This paradigm treated tesuji as a collection of locally optimal tactical patterns—such as the snapback, the ladder, and the net—that were transmitted through oral teaching and game records. The focus was on recognizing and applying these patterns in concrete positions, with an emphasis on reading and calculation within a territory-first strategic context.
The Modern Professional Tesuji Paradigm, which developed in the 20th century, systematized tesuji study by categorizing moves by their tactical function (e.g., cutting, connecting, capturing, or escaping). This framework, championed by Japanese professionals and later adopted internationally, treated tesuji as a learnable skill set that could be practiced through problem sets and game analysis. The Korean Fighting School further refined this approach by emphasizing aggressive, high-risk tesuji that aimed to create complications and force errors, often prioritizing fighting efficiency over local perfection.
The AI-Assisted Tesuji Paradigm, emerging after 2016, has revolutionized the subfield by revealing non-human tactical sequences that often sacrifice local efficiency for global advantage. This framework treats tesuji not as isolated patterns but as context-dependent moves that must be evaluated through neural-network-based value judgments. The paradigm has expanded the repertoire of recognized tesuji, including moves that were previously considered suboptimal or anti-intuitive, and has shifted the focus from pattern recognition to holistic positional understanding.