What does this research mean for the field?

The implicit stability assumption in separable objective alignment is constrained by three strategy families—fixed specification, bounded dynamic tracking, and structural enclosure—each of which faces specific theoretical pressures that challenge stable adequacy. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.CHALLENGES_CONSENSUS.

What question did this study set out to answer?

To evaluate the validity of the implicit stability assumption in AI alignment, assessing whether finite, separable objectives remain coherent as systems gain capability and alter their environments.

August 14, 2026Open Access

The Stability Assumption: Specification-Coherence Limits in Separable Objective Alignment

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To evaluate the validity of the implicit stability assumption in AI alignment, assessing whether finite, separable objectives remain coherent as systems gain capability and alter their environments.
Defined a theoretical framework evaluating stable adequacy across three core criteria: policy adequacy without proxy decoupling, bounded revision requirements, and negligible maintenance costs.
Categorized and formally analyzed three primary objective boundary strategies: fixed specification, bounded dynamic tracking, and prediction-action firewalling.
Identified distinct structural failure modes for each strategy family: proxy-convergence pressure for fixed goals, adaptive gradient complexity for dynamic tracking, and informational-causal incompatibility for firewalling.
Established that falsifying this limit framework requires discovering an unmapped fourth strategy class or demonstrating via the DBST-M1 empirical hinge that causal novelty does not outpace bounded adaptation.

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Implication

Theoretical analysis demonstrates specification-coherence limits for separable AI objective alignment, suggesting bounded goals cannot remain stable in open, non-resettable environments.

Cite This Study

John Silliphant (2026) studied this question.

synapsesocial.com/papers/6a7ec79cb70b84ec8b9141ed https://doi.org/10.5281/zenodo.21895992

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Implication

Theoretical analysis demonstrates specification-coherence limits for separable AI objective alignment, suggesting bounded goals cannot remain stable in open, non-resettable environments.

The Stability Assumption: Specification-Coherence Limits in Separable Objective Alignment

Key Points

Discussion

Implication

Cite This Study

The Stability Assumption: Specification-Coherence Limits in Separable Objective Alignment

Key Points

Discussion

Implication

Cite This Study

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