Proposal for a Composite Architecture #2
Description
Dear MiniMax team,
I read with great interest the details of your latest model. Your approach to enhancing efficiency is a significant step in the right direction, moving beyond the conventional scaling paradigms.
While the field often focuses on the reduction of active parameters for efficiency gains, I share the perspective that this alone may not be the path to AGI. Two critical limitations are:
1. Gains from sparse activation do not inherently translate to improved long-range reasoning or deeper understanding.
2. The architectural complexity of these systems presents a significant barrier to the continuous, lifelong learning required for a true AGI.
However, your work has directly inspired a solution. What if your model could serve as the foundational "teacher" for a more dynamic architecture?
I have been developing a model called the Compositional Latent Thought Model (CLTM). Instead of a mixture of experts, it constructs responses by compositing outputs from a vast network of thousands of small, specialized models, each storing discrete "thoughts" or reasoning chains. The key innovation is a Multiple Layer Attention Block that operates across all these models, creating a virtually infinite attention window and enabling complex, multi-step reasoning.
The CLTM architecture is inherently modular and efficient, as inference is run on only one small model at a time, building the output iteratively. This makes it a perfect substrate for continuous learning.
I believe there is a powerful synergy here. Using your sophisticated model as a teacher to populate and guide the "thought maps" within the CLTM framework could bridge the gap between short-term efficiency and the long-term goal of adaptive, general intelligence.
I would be delighted to discuss this potential synthesis further.
Best regards,
Rodrigo Benitez.