Understanding Moral Reasoning Trajectories in Large Language Models: Toward Probing-Based Explainability

Large language models (LLMs) increasingly participate in morally sensitive decision-making, yet how they organize ethical frameworks across reasoning steps remains underexplored. We introduce $\mathit{\text{moral}}\mathit{\text{reasoning}}\mathit{\text{trajectories}}$, sequences of ethical framework invocations across intermediate reasoning steps, and analyze their dynamics across six models and three benchmarks. We find that moral reasoning involves systematic multi-framework deliberation: 55.4–57.7% of consecutive steps involve framework switches, and only 16.4–17.8% of trajectories remain framework-consistent. Unstable trajectories remain 1.29$\times$ more susceptible to persuasive attacks ($p=0.015$). At the representation level, linear probes localize framework-specific encoding to model-specific layers (layer 63/81 for Llama-3.3-70B; layer 17/81 for Qwen2.5-72B), achieving 13.8–22.6% lower KL divergence than the training-set prior baseline. Lightweight activation steering modulates framework integration patterns (6.7–8.9% drift reduction) and amplifies the stability–accuracy relationship. We further propose a Moral Representation Consistency (MRC) metric that correlates strongly ($r=0.715$, $p<0.0001$) with LLM coherence ratings, whose underlying framework attributions are validated by human annotators (mean cosine similarity $=0.859$).