Innovative Techniques: we present techniques including stealth compression, latent anchor anchoring, recursive prompt folding, and context decay detection, each designed to enhance reasoning stability and auditability in extended LLM interactions.

As large language models (LLMs) become increasingly embedded in high-stakes domains such as legal analysis, financial modeling, and scientific validation, the precision and reliability of their outputs are no longer mere conveniences—they are critical to operational integrity. While much attention has been paid to improving LLM performance through model scaling, finetuning, and retrieval augmentation, relatively little focus has been directed toward the structural integrity of the prompts themselves.

Prompting errors are often diagnosed at the level of content hallucination, factual drift, or omitted constraints. However, these symptomatic failures are frequently the product of a deeper, structural malfunction: the failure to collapse or explicitly reframe a prior latent structure before introducing new logic.

In the domain of technical editing, residual frame errors refer to a class of cognitive mistakes wherein an outdated conceptual structure persists beyond its relevance, contaminating subsequent revisions. These errors typically arise when an editor—familiar with an earlier draft—revises a document without fully collapsing or reprocessing prior logic.

We define residual frame error in prompt engineering as the unintentional persistence of a previously valid semantic structure across multiple interaction turns or prompt layers. It manifests when users build new logic atop previous prompts without explicitly dissolving or reframing the latent structures embedded in the earlier exchange.

Transformer-based language models, despite their extended context windows, do not possess true long-term memory. Instead, they operate on a sliding window of active attention, which prioritizes recency and proximity over durability.

Context window decay refers to the progressive erosion of semantic fidelity over long-form interactions. Unlike catastrophic forgetting, which denotes the loss of model weights during retraining, context decay is transient, dynamic, and local to the current session.

Subtle indicators of early decay include:

Complex analytical tasks often require the resolution of nested logic chains: conditions embedded within subconditions, procedures with branching paths, and systems that unfold iteratively. Traditional flat prompting structures fail to express this complexity efficiently.

Recursive frame architecture introduces a hierarchical structuring method wherein prompts are designed not merely as linear instructions, but as nested evaluative scaffolds. Each layer of the prompt defines a frame with its own scope, resolution rules, and termination cues.

Large language models are not blank slates; they are recursive inference machines operating within latent attention scaffolds. To harness their full potential in domains that demand logical integrity and epistemic precision, we must move beyond superficial prompt optimization and toward formal control of semantic structure.

This paper introduced the concept of residual frame error as a fundamental failure mode in prompt-based interaction. Through techniques such as stealth compression, latent frame anchoring, context decay detection, and dimensional prompt unfolding, we outlined a new theory and practice: Cognitive Prompt Architecture.