What might change if a capability already acquired could continue to help with later tasks? CCA explores that question through capability reuse, independent upgrades, and on-demand computation, with the aim of reducing repeated training. These are connected research motivations, not verified benefits.
Figure:Research goals; the diagram does not establish efficiency, cost, or performance benefits.
Giving capabilities a chance to remain useful
Once a basic capability has formed, the research asks whether it can continue to be used under suitable conditions or support other tasks. This concerns long-term growth: how a system might keep learning on the basis of existing capabilities and experience, rather than assuming that every new step requires reacquiring everything.
Capability transfer is part of this direction. If a basic capability could transfer from individual A to B, the goal would be for B to gain a usable capability while retaining its own identity and experience. This is a design requirement to study, not an available transfer procedure.
Developing capabilities separately and computing as needed
Independent upgrades ask whether one capability can improve within clearer boundaries, without treating all capability changes as a single reconstruction. On-demand computation asks how computation can be directed around the needs of different tasks.
These goals need to be tested together. A more flexible part does not necessarily mean less computation overall; a useful change does not by itself show that other capabilities and responsibilities are preserved. Higher intelligence with less computation is a research vision. Current material does not support promises of efficiency, cost, or performance benefits.
Studying how growth itself changes
CCA’s self-evolution goal includes changes in internal capability structure and learning methods. The research asks not only whether capabilities can accumulate, but whether the way growth occurs can itself develop. Specific mechanisms, development order, and degree of autonomy remain open questions.
As of 2026-10-27, core semantics and complete V3 had not passed the relevant acceptance review. To support continual growth, the work still needs to explain how learning changes are adopted, actually used, saved, and restored while protecting the individual’s identity, experience, and existing responsibilities.
