Explaining LLM Adaptation Decisions in Self-Adaptive Systems via Faithful Input Attribution

Large language models (LLMs) are increasingly investigated as decision-making components in MAPE-K self-adaptive loops. For such autonomous behavior to be trustworthy, stakeholders need evidence with which to assess and justify confidence in its decisions. We envision a modular and extensible platform for explaining adaptation decisions generated by LLMs. Each platform module checks one candidate hypothesis about what a decision depends on, operationalizes it with one or more explanation methods, and evaluates the explanation for faithfulness and plausibility. We identify four initial hypotheses with their respective modules and implement one module, which uses replay-based input attribution to trace each recorded action to the information that supports it, separating which component to act on from what to do to it. We evaluate the module with three LLM planners on a well-established self-adaptivesystem exemplar. The results distinguish system-wide dependence in component selection from stronger component grounding in action specification.