Engineering Emergence: From Prompting to a New Topological Discipline?

I think there is a structural limit here that is easy to miss if “coherence” is treated as a property of the prompt alone.

A prompt-based system can be written abstractly as

y_t = M(p_t, c_t, x_t),

where (M) is the model, (p_t) the prompt or scaffold, (c_t) the current context window, and (x_t) the current task input. A sufficiently disciplined prompt can certainly improve local behavioral stability: it can reduce variance, preserve constraints for some horizon, and make responses more regular under perturbation.

But long-horizon epistemic coherence requires a different object. It requires an explicit state transition system:

S\_{t+1} = G(S_t, e_t, o_t, a_t),

where (S_t) is a persistent epistemic state, (e_t) incoming evidence, (o_t) the update operation, and (a_t) the authority/provenance attached to that operation.

The model may then produce language from a view of that state:

y_t = M(V(S_t), x_t),

but the coherence is no longer carried by the prompt. It is carried by the governed update system.

This distinction matters because a prompt does not by itself preserve a complete, auditable history of claims, revisions, conflicts, provenance, authority levels, rejected hypotheses, superseded claims, or unresolved tensions. It can instruct the model to “remain consistent,” but it does not supply a durable, replayable, externally checkable object with which consistency is defined.

In other words, prompting can create local behavioral coherence; it cannot by itself guarantee durable epistemic coherence.

For long-horizon coherence, the system needs at least:

• persistent claim state,
• explicit status transitions,
• provenance and authority tracking,
• conflict preservation rather than automatic smoothing,
• update rules independent of the language model,
• replayability or auditability of state changes.

Without such an external persistent epistemic state, the system’s “coherence” remains bounded by the context window, retrieval quality, summarization choices, prompt placement, sampling variation, and the model’s implicit reconstruction of prior commitments.

So I would distinguish two claims:

1. Structured prompting may improve in-context constraint retention and behavioral stability.
2. Structured prompting alone cannot produce robust, corrigible, auditable long-term epistemic coherence.

The first claim is empirically testable and may well be true.

The second claim fails structurally unless the prompt is embedded in a stateful epistemic architecture. At that point, however, the core mechanism is no longer prompting alone; it is a persistent epistemic state with governed updates, while the LLM mainly provides linguistic realization and bounded inference.