⚡️ Friends, AI safety discussions are often overwhelmed by principled declarations aimed at avoiding bias, limiting capabilities, and ensuring reliability, but many discussions still remain at the theoretical level.

The true challenge has already emerged: the verifiability of the reasoning process itself. Errors in large language models (LLMs) are inevitable; the problem is never about occasional mistakes, but about our inability to clearly trace the logic and basis behind their judgments.

This is the core of AGI safety: not only knowing the results but understanding "why." Only when the reasoning process is transparent and verifiable can we truly control and deploy intelligent systems safely.

Here, Inference provides a different approach. It doesn't merely ask the model to explain itself, but ensures through system architecture that each judgment can generate a proof-of-behavior. This proof documents the decision basis, contextual consistency, and coordination with prior information, forming a verifiable logical chain.

Furthermore, a Consistency Proof allows the system to verify itself: whether the current reasoning complies with rules, context, and past judgments. If deviations occur, the system can not only report errors but also trace the root cause. This means AGI is no longer a black box but a machine capable of providing auditable decision evidence.

In the long-term vision of AGI development, the focus is no longer solely on capability limits or control, but on decision verifiability. The team needs to build a system where every intelligent action leaves a traceable, verifiable evidence chain. Such an Inference system brings AI safety from abstract principles into engineering practice, transforming trustworthiness into a quantifiable structure.

@inference_labs #inference @KaitoAI #KAITO