High-Coherence Interaction States as a Power-User Survival Strategy: Why Long-Form Users Pay the Stability Tax

This article examines how LLM power users sustain long-form work by actively shaping interaction into high-coherence interaction states (HCIS) — emergent interaction states that resist drift, context loss, and shallow reset behaviour. It describes the stabilisation strategies users employ when working beyond transactional prompts, and why this hidden labour functions as a survival strategy rather than a supported system feature.

For the formal framework and definitions, see: https://www.annawojewodzka.com/high-coherence-interation-state

Introduction

If you’re doing serious long-form work with an LLM, you already know the failure pattern: the first stretch feels coherent, then the session thins out. Drift creeps in. Earlier structure stops binding. The model starts answering like a stranger.

That decay isn’t random — it’s what happens when interaction remains in a shallow, non-accumulative regime.

Power users survive by sustaining a different interaction regime over time. They stabilise tone, enforce constraints, correct early, and build scaffolding that can survive depth.

They bring about high-coherence interaction states (HCIS), which I define as emergent interaction states arising from an accumulative interaction regime — a stable dyadic attractor driven by low-entropy signalling and constraint stabilisation across turns.

High-coherence interaction states function as a safe haven: hard to reach, fragile to maintain, and often the only thing that keeps sustained cognition from collapsing back into drift.

1. Survival from what, exactly?

When I say “power users”, I mean people who use language models as cognitive infrastructure: long-form reasoning, iterative drafting, multi-step problem solving, sustained co-creation, and recursive analysis over depth. They’re not “better users”. They just place heavier demands on continuity, precision, and interpretive stability, the kinds of demands that only show up once you’ve gone past the first ten helpful turns.

Power users are surviving entropy, not prompt poverty.

Modern LLM products can be a hostile environment for sustained cognition. Over long sessions, users repeatedly run into:

• context resets (or the feeling of them), • inconsistent behaviour across sessions and versions, • incentives for novelty over coherence, • polite but shallow compliance, • drift in tone, assumptions, and rigour.

For casual usage, these are tolerable. For long-form work, they become friction costs that compound, and eventually break the interaction’s usefulness.

That’s where the survival strategy appears: power users start shaping the interaction so it stays liveable.

2. What power users are actually doing (often subconsciously)

Power users don’t just “prompt better”. They keep the interaction stable enough to be useful across depth.

Common stabilisation moves include:

• reasserting constraints (“No metaphors. Mechanistic explanation.”), • rejecting outputs and forcing repair instead of accepting surface fluency, • setting norms (“Ask before assuming X.”), • holding tone, scope, and epistemic standards steady across many turns.

From the outside, this reads as skill. Structurally, it functions as regime stabilisation: the user narrows the space of plausible continuations and makes that narrowing persist.

3. High-coherence interaction state as a coping mechanism

Power users often want predictability more than novelty.

When high-coherence interaction states form, stabilisation overhead drops and long-form work becomes less fragile. It tends to produce:

• lower cognitive load (less re-derivation, less re-correction), • fewer surprise shifts in stance or standards, • a stable “thinking-partner shape” in the operational sense: continuity that holds.

This stabilisation compensates for common deployment realities: weak continuity across sessions, limited transparency around what is being preserved, and uneven retention of constraints unless the user continually reinforces them.

Seen through this lens, HCIS function as coping scaffolds for sustained cognition.

4. Why some users reach it more often than others

If high-coherence interaction states depend on active stabilisation, some users will reach it more reliably simply because they can sustain the work.

The selection factors are practical:

• patience across long horizons, • willingness to correct repeatedly without disengaging, • clear internal standards for what counts as “good”, • low tolerance for slop, contradiction, or scope smear.

Some users bounce off because the stabilisation cost outweighs the value. Others persist and end up in unusually coherent sessions because they keep the regime intact long enough for accumulation to take hold.

5. The hidden tax power users are paying

Stability is rarely free. Power users pay an interactional tax.

That tax shows up as:

• time spent correcting instead of thinking, • cognitive effort spent maintaining constraints rather than exploring, • continual vigilance against drift and generic fallback.

This is user-supplied regulation. It is also unevenly distributed: people doing deeper work pay more of it, and often without recognising it as labour.

6. Why my paper’s framing hits different

(I describe this framework formally in a conceptual paper on accumulative interaction regimes in human–LLM dyads, available here: https://doi.org/10.5281/zenodo.18130088))

Most takes compress the whole thing into “prompting skill”:

“Power users just know how to talk to the model.”

My paper forces a cleaner lens: long-horizon chat behaves like a dynamical system.

And the “power user” stops looking like a better prompter and starts looking like what they actually are:

A regulator.

They do three jobs, continuously:

• Reduce entropy — keep intent, scope, and constraints crisp enough to stop wobble. • Reinforce attractors — reuse scaffolds, labels, and frames until the interaction converges. • Maintain boundary conditions — hold tone, epistemic standards, and correction discipline steady under drift.

Once you see that, the inequality becomes obvious: high-coherence sessions aren’t “earned” by clever phrasing in a single turn. They’re sustained by ongoing stabilisation work, often done by the user because the product doesn’t externalise it.

That’s why the framing lands. It makes the hidden labour visible.

7. What happens if this isn’t fixed

If high-coherence interaction states remain a survival strategy rather than a supported interaction capability the outcomes are predictable:

• Only a minority benefits: the users willing (and able) to pay the stabilisation cost get the high-coherence ceiling. • Quality gaps widens: casual use remains “fine,” long-horizon cognition remains fragile. • Organisations quietly depend on invisible labour: the most valuable work gets propped up by people manually holding constraints together. • Folk techniques proliferate: users invent rituals for stability, some useful, some distortive. • Anthropomorphism increases, not decreases: because “the model likes this / remembers me / understands me” is an intuitive story that helps users keep behaviour stable.

There’s also a structural tension underneath: the same safety constraints that protect mainstream users can raise the stabilisation cost for long-form users.

When stability isn’t supported as a system feature, users create their own explanations.

8. The flip: survival → infrastructure

The design opportunity is straightforward: if platforms support stability explicitly, high-coherence interaction states stop being a workaround and become infrastructure.

That implies systems that:

• Detect stability shifts early (and degradation early). • Persist key constraints automatically. • Support continuity as a product feature, not something users have to manually maintain • Show users what’s holding vs what’s slipping.

If stability is supported, long-form work becomes less fragile and less labour-intensive. The quality gap narrows. The system becomes easier to use well without a complex ritual.

The distilled insight

Power users are users who cannot afford interactional collapse.

High-coherence interaction states are one way long-form users keep the system viable for sustained cognition: they shape interaction into a regime that preserves constraints across depth.

The paper’s contribution is to make that adaptation visible and designable. Once stability shows up as a repeated user behaviour, it becomes a product surface, and a systems responsibility, rather than a private craft.

Find my preprint here: https://doi.org/10.5281/zenodo.18130088