Proprietary interaction mechanism theory enables evaluation and alignment of neural network logic, establishing a new paradigm for large model training and deployment.

Equivalent And-Or Interaction theory decodes the black box at the mathematical level, providing theoretical foundations for high-stakes AI applications.

QuanShi Zhang shared entrepreneurship practices, rejecting end-to-end black box to shift AI from trial and error to science-driven development.

As models hit the Scaling Law ceiling, breakthroughs at the representation mechanism level are needed to reduce training costs.

We find most interaction mechanisms in two classic LLMs are similar. This mechanistic convergence implies a shared first-principles optimal representation.

Numerical rigor, semantic clarity, and the ability to explain model generalizability constitute the three core challenges of mechanism interpretability.