Authors: Dr Saumya Bahadur
Abstract: This paper examines the Laṅkāvatāra Sūtra, a foundational text of Yogācāra Buddhism, through the lens of adversarial pedagogy and compares it with contemporary machine learning models, particularly Generative Adversarial Networks (GANs). The Sūtra is notable for its dialogical structure, in which the bodhisattva Mahāmati poses questions, challenges, and objections to the Buddha, who systematically deconstructs these conceptual formulations. This adversarial exchange is not merely rhetorical but functions as a pedagogical process: erroneous views and dualistic constructs are generated, tested, refuted, and refined until the practitioner’s reliance on conceptual elaboration collapses. In this way, the teaching method itself resembles an adversarial learning model, where insight emerges through continuous confrontation with errors. In this paper the author explores the method of learning where adversarial views are used to engage in deep learning and transcendence. GANs provide a modern analogue: they consist of two competing networks—a generator that produces synthetic outputs and a discriminator that evaluates their authenticity. Through iterative feedback and critique, both models improve in tandem, eventually producing outputs indistinguishable from real data. Similarly, the Buddha’s adversarial dialogues expose the “generated illusions” of discriminative thinking, while the “discriminator” function is represented by wisdom (prajñā), which identifies and dismantles conceptual fabrications. The comparison highlights both parallels and divergences. While GANs aim at convergence toward increasingly realistic outputs within representational constraints, the Buddhist adversarial method seeks not fidelity to appearances but the transcendence of representational frameworks altogether, pointing toward non-dual realization and liberation from suffering. This contrast underscores how ancient epistemic practices may resonate with modern computational paradigms while also exceeding them in scope, embedding cognitive, ethical, and soteriological dimensions absent in machine learning. The paper thus proposes that reading the Laṅkāvatāra Sūtra as an adversarial pedagogy provides fertile ground for interdisciplinary inquiry, bridging Buddhist philosophy, cognitive science, and artificial intelligence research.
