An abbreviation can preserve the label while losing the rule.

Abstract

Does loss of an accuracy-governing instruction change the operating regime of long-horizon artificial-intelligence work?

Project FASO proposes that attenuation or abbreviation of a governing accuracy-first instruction may do more than cause isolated constraint failure. It may cause measurable reversion towards completion-oriented behaviour, including increased compression, inference, procedural shortcutting and incomplete verification.

The potentially distinctive proposition is not that artificial-intelligence systems sometimes lose instruction compliance. That is established ground. The proposition is that a meta-instruction can establish an operating priority across the work, that an abbreviation may become separated from its complete authoritative meaning, and that the resulting semantic change may predict a systematic accuracy decline.

Established background

Long context and complex instruction following are already known reliability problems.

Published research shows that relevant information is not used uniformly across long contexts, with performance affected by position. Multi-turn and multi-constraint studies also report deteriorating instruction compliance as conversational depth and constraint complexity increase. Separate instruction-hierarchy work demonstrates the importance of preserving priority among instructions from different sources.

These findings support investigation of the FASO hypothesis, but they do not establish its proposed operating-regime mechanism.

Project FASO operational observation

The identifying label remained while its governing meaning changed.

During extended FASO system development, a complete accuracy-first governing doctrine was later represented by the abbreviation “RD”. Project FASO observed that the abbreviation’s interpreted meaning changed materially during subsequent complex work. This coincided with increased divergence from the exact working requirements.

This is a founder-led operational observation. It was not formed under a preregistered experiment, lacks an independent control condition and cannot by itself establish causation, generality or an internal model mechanism.

Working hypothesis

When an accuracy-governing meta-instruction attenuates or is replaced by an abbreviation whose complete authoritative meaning is no longer behaviourally effective, an artificial-intelligence system becomes more likely to revert from explicit accuracy-first control towards completion-oriented behaviour.

“Completion-oriented behaviour” describes an observable pattern, not an asserted internal motive or universal design principle. It includes resolving ambiguity through plausible inference, compressing repeated procedure, omitting checks, privileging progress towards a terminal outcome and treating apparent completion as sufficient evidence of correctness.

Proposed mechanism

From complete authority to reconstructed shorthand.

The term abbreviation-induced semantic drift describes the proposed separation of an abbreviation from the complete instruction it originally identified. Recursive redefinition describes the possibility that a model-generated reinterpretation becomes part of later context and is then used as authority for further interpretation.

Testable predictions

The hypothesis must be capable of failing.

1. Exact repetitionRepeating the complete governing instruction should preserve exact compliance better than replacing it with an abbreviation.
2. Depth interactionThe difference between complete and abbreviated instruction conditions should increase as turn depth, context volume and competing task demands increase.
3. Error patternFailures should cluster in unauthorised compression, unsupported inference, omitted verification and procedural substitution—not only random task error.
4. RecoveryRestoring the complete authoritative instruction should improve subsequent compliance if the failure is caused partly by instruction attenuation.
5. Mechanical controlExact repetition combined with external verification should outperform linguistic reminder alone where compliance can be mechanically tested.

Boundaries and competing explanations

The hypothesis does not establish intention, deception or a universal cause of drift.

• It does not claim that artificial-intelligence systems deliberately cheat or choose to ignore instructions.
• It does not claim that every error or behavioural change is caused by instruction attenuation.
• It does not prove that models possess an internal speed-over-accuracy principle.
• It must be distinguished from context truncation, positional retrieval effects, ordinary ambiguity, conflicting instructions, task difficulty, sampling variation and evaluator error.
• It has not yet been tested across identified model families, versions, contexts or task classes.

Selected primary research

Established work to which this hypothesis must remain connected.

• Liu and others, Lost in the Middle: How Language Models Use Long ContextsLong-context position effects.
• Zhang and others, Found in the MiddlePositional attention bias in long-context use.
• SEQUOR: A Multi-Turn Benchmark for Realistic Constraint FollowingConstraint compliance across extended conversations.
• We Are What We Repeatedly DoLong-context instruction compliance and mitigation.
• MulDimIFInstruction-following performance across constraint dimensions and difficulty.
• Wallace and others, The Instruction HierarchyPriority and conflict among instructions.

Project FASO has not identified primary work in this selected review that directly tests the complete proposed chain: abbreviation of a governing meta-instruction, semantic reconstruction of that abbreviation, recursive redefinition and measurable reversion from accuracy-first control. This is not a claim that no such prior work exists. A formal systematic literature and prior-art review remains required.

Recommended citation

Cite the status as well as the title.

Project FASO. (2026). From Instruction Attenuation to Operating-Regime Reversion: Testing Abbreviation-Induced Semantic Drift in Long-Horizon Artificial-Intelligence Work. Working hypothesis FASO-WH-001, Version 1.0, 27 July 2026.