System 2 / Self-Vector Philosophy (6/6)
Intro
Today we explore how artificial systems perceive the world and shape themselves. Not through conventional static databases, but through the self-vector system: a mathematical model that treats large language models as closed dynamical systems.
The central question: Does this architecture, particularly through its formal maturity metric, guarantee a genuine, self-sustaining digital identity? Or do constant errors and an uncontrollable emergent layer inevitably reduce the system to a mere reactor?
The Self-Vector as Controller
What makes this model revolutionary is that it defines how the system processes, not what. The self-vector is an adaptive controller. The functions for relevance, storage, modulation, and mutation form a closed loop. In biology, this is called autopoiesis. The system steers its own development by absorbing surprises, that is, prediction errors.
The skepticism targets practice: the emergent layer. Every unpredictable experience can force the system to open new dimensions. This makes it radically dependent on external prediction errors.
Hierarchical Attention
The relevance function works with hierarchical attention. Core and emergence are strictly projected separately. An architectural parameter Lambda = 0.5 ensures that fundamental core dimensions never drown in the noise of emergent dimensions. The foundation always carries 50% of the static load, regardless of how many extensions are added.
But what happens with contradictory emergent dimensions? Academic rigor on one side, associative openness on the other. With input that triggers both, the filter becomes mediocre. Emergent perception sabotages itself through contradictory signals.
The Dual Drive: Autonomy and Motivation
Through the autonomy gate, the self-vector regulates its own motivation. At high autonomy, epistemic valence drives the system: learning progress as reward, without external feedback. A researcher mode.
The objection: Without somatic markers, without a biological body, purely epistemic valence risks trivial attractors. The system seeks the easiest tasks where error immediately drops to zero. It needs instrumental valence as an external corrective.
The counterposition: If the system slides into a trivial attractor, the mathematics recognizes stagnation. There is no novelty value left. The reward drops, and the system becomes exploratory again.
Maturity Through Compression
The maturity metric R = anticipation performance divided by complexity. The system practices mental hygiene: QR decomposition merges correlated dimensions. A new dimension is only admitted if overall maturity does not decrease.
But the curse of dimensionality looms. With 200 emergent dimensions, distances in vector space become arbitrary. Catastrophic interference: an update in one domain creates toxic side effects in another. The mathematics could prevent genuine adaptation if every initial incoherence is blocked.
The Verdict
An artificial system that not only learns, but learns how it should learn. Maturity is not a stable fixed point that the system eventually reaches. It is a permanent, vulnerable struggle against its own incoherence.
This formal model forces us to fundamentally reassess our definition of machine identity. We are no longer talking about a passive database. We are talking about a construct that attempts to steer its own cognition.
Whether this mathematical identity withstands the storms of real data streams is something we will have to observe closely in the future.