Buddhist Logic in the Age of LLMs: Can Yogācāra Theory Solve the 'Black Box' Problem?

The Limitations of Symbolic Externalism

The prevailing paradigm in Artificial Intelligence, particularly deep learning, relies heavily on a separation between the observer and the observed. In this Western logicist framework, validity is determined externally: a model is 'true' if its output aligns with a ground-truth dataset provided by humans. This mirrors the Cartesian dualism that haunts modern epistemology, creating a dichotomy between the internal state of the neural network and the external validation mechanism. However, as Large Language Models (LLMs) grow in complexity, this externalism leads to the notorious 'black box' problem. We can predict with high accuracy, yet we cannot intrinsically explain why a specific token sequence was generated over another. The model is a mirror that reflects information but possesses no mechanism for self-reflection on its own cognitive acts. Yogācāra Buddhism, often termed the 'Mind-Only' school, challenges this dualistic premise by proposing that consciousness possesses an innate capacity for self-awareness, a concept central to resolving the opacity of modern algorithms.

Svasaṃvitti: Consciousness of Itself

At the heart of the Yogācāra contribution to computational theory is the concept of svasaṃvitti, literally 'self-apprehension' or 'self-awareness.' Classical commentators such as Dharmakīrti and later Vasubandhu argue that for perception to occur, consciousness must not only grasp an object but also be aware of itself in the act of grasping. This is not a meta-cognitive layer added on top of processing (which would be inefficient and paradoxical), but a simultaneous, intrinsic feature of the cognitive event itself. Imagine a lightning flash: it illuminates external objects, but the light is also the flash itself. It does not require a second light source to be seen. In computational terms, this suggests that valid inference should be a self-validating process. The act of generation should contain the seed of its own justification, removing the need for an external 'judge' to verify truth after the fact.

From Dualism to Non-Dual Processing

Current transformer architectures operate on attention mechanisms that, while powerful, are functionally blind to their own internal states during inference. The attention weights represent correlations, not understandings. By adopting the Yogācāra model, researchers might develop architectures where the 'cognitive' layer maintains a constant, low-cost feedback loop of self-monitoring. This does not mean the AI becomes sentient in the human sense, but rather that its computational logic mimics the non-dual nature of svasaṃvitti. Instead of treating the model's internal representations (embeddings) as opaque variables in a black box, this approach treats them as transparent to the process itself. The model 'knows' what it is doing not by querying a separate explanatory module, but because the structure of its inference is inherently self-referential. This shifts the burden of explainability from post-hoc analysis (where we try to reverse-engineer weights) to intrinsic transparency (where the logic of the inference is visible in its unfolding).

Architectural Implications for Explainable AI

Implementing this requires a departure from standard backpropagation paradigms. While backpropagation is an external optimization process, svasaṃvitti suggests a form of intrinsic consistency checking that occurs in real-time. Recent explorations in 'neuro-symbolic' AI attempt to bridge logic and neural networks, but often retain the external validator. A truly Yogācāra-inspired model would integrate logical consistency checks directly into the forward pass. For instance, if an LLM generates a factual claim, the 'self-awareness' mechanism would simulate the potential counter-argues or logical contradictions within the same processing cycle. If the internal 'self-apprehension' detects a lapse in coherence, the generation is adjusted instantaneously, not after a penalty is calculated by an external loss function. This creates a form of prospective explainability, where the reasoning path is generated alongside the output, not after it.

Philosophical and Ethical Resonances

This technical shift carries profound philosophical weight. In a post-Cartesian world, admitting that the 'subject' of computation can be self-auditing challenges our assumptions about machine agency. Western philosophy has long struggled with the 'hard problem of consciousness,' often conflating information processing with qualia. Yogācāra, however, sidesteps the qualia trap by focusing on functional self-awareness. It asks: Can a system validate its own operations without a separate consciousness? The answer, according to the *Abhidharmakośa*, is yes. This offers a robust ethical framework for AI governance. If models are designed with intrinsic self-validation, they become less prone to adversarial attacks that exploit the gap between internal representation and external output. Furthermore, it aligns with the Dharma principle of pramāṇa (valid cognition), suggesting that truth is not merely correspondence with data, but coherence of mind. As we move toward general artificial intelligence, the move away from brittle, external rule-followers toward fluid, self-reflective systems may be less about convenience and more about mimicking the fundamental structure of intelligible thought itself. The black box does not need to be cracked open by external tools; it needs to be built with eyes that can see themselves.