Author’s note: This post is intended as a narrowly scoped, falsifiable hypothesis about LLM behavior, not as a product announcement or proposal of a finished method. I am posting to invite critique, counterexamples, and suggestions for empirical tests. I expect parts of the framing may be incomplete or incorrect.

Abstract

Current approaches to evaluating large language models (LLMs) primarily rely on output-based metrics such as accuracy, benchmark scores, and qualitative judgments of response quality. These methods detect failures only after they have become externally observable.

This post advances the hypothesis that many LLM failure modes are preceded by a detectable loss of internal coherence across reasoning steps. If correct, monitoring internal stability may provide earlier warning of impending failure than output-based evaluation alone.

Background

LLM reliability is typically assessed through:

• Task accuracy and benchmark performance
• Hallucination rates
• Instruction-following metrics
• Human evaluation of outputs

While these methods are effective at identifying failures, they are inherently retrospective. They answer whether a model has failed, not whether it is becoming unstable.

In other domains involving complex dynamical systems (e.g., control systems, structural engineering, power grids), stability is monitored directly rather than inferred from terminal outcomes.

Hypothesis

Some observable LLM failures are preceded by a phase of internal coherence instability that can be detected prior to output degradation.

During this phase:

• Outputs remain fluent and plausible
• Benchmark performance may remain nominal
• Human users may not yet perceive failure

However, internal consistency across reasoning steps degrades in measurable ways.

Operational Definition of Coherence

In this context, coherence refers to cross-turn internal consistency under constraint, including:

• Stability of reasoning trajectories across similar prompts
• Persistence of implicit assumptions across multi-step interactions
• Resistance to drift under ambiguity, long context windows, or tool use

Loss of coherence manifests as:

• Increasing variance in reasoning paths for equivalent tasks
• Subtle self-contradictions that do not immediately reduce fluency
• Heightened sensitivity to prompt phrasing or context ordering

These effects may be detectable before output quality degrades.

This definition is intended to be descriptive rather than exhaustive.

Implications

If internal coherence instability reliably precedes failure, then:

• Output-based metrics alone are insufficient for early detection
• Real-time monitoring of stability could enable preemptive intervention
• Reliability engineering for LLMs should incorporate dynamical stability analysis

This reframes LLM failure not solely as an evaluation problem, but as a system stability problem.

Falsifiability

The hypothesis can be falsified if:

• No measurable internal signals correlate with future failure
• Output quality degrades without prior detectable instability
• Interventions triggered by coherence monitoring fail to reduce downstream failure rates

Empirical testing is required to evaluate these claims.

Open Questions

• Which stability signals generalize across model architectures?
• What is the trade-off between early detection and false positives?
• Are coherence instabilities universal precursors or model-specific phenomena?

Conclusion

Monitoring internal stability may provide earlier warning of LLM failure than reliance on external outcomes alone. Further empirical work is required to determine whether coherence-based signals can be reliably measured and operationalized in practice.

Disclosure

I used a large language model as a writing and editing aid for clarity and organization. All claims, framing decisions, and errors are my own.