What Drives LLM Self-Reflection? A Controlled Ablation of Uncertainty Routing in Armed Conflict Forecasting

Self-reflection is widely assumed to improve LLM reasoning, yet which component drives the gain remains poorly understood. We present a controlled six-condition ablation isolating four components of LLM self-reflection: evidence exposure, diagnostic scaffolding, taxonomy vocabulary, and action routing. Two precise null results converge on a single mechanism. First, structured diagnostic questions add no measurable value over unstructured reflection (F1 = 0.296 vs 0.297, p = 1.000, 95% CI [-0.041, +0.040]). Second, presenting the full uncertainty taxonomy while collapsing the action space to a single generic action also adds no value (ΔF1 = +0.008, overlapping 95% CIs), ruling out taxonomy vocabulary as the mechanism. Typed action routing provides consistent directional gains (F1 = 0.379 vs 0.296); the conservative estimate controlling for taxonomy vocabulary is ΔF1 = +0.075, and the overall gain over the single-shot baseline is significant by bootstrap CI (ΔF1 = +0.101, 95% CI [+0.020, +0.185]). The vocabulary-routing decomposition replicates on GPT-4o: taxonomy vocabulary adds no significant value over generic reflection (p = 0.773), while action routing provides significant gains (p = 0.025), confirming the mechanism holds across backbones. Gains concentrate on structurally novel conflicts: in Myanmar (F1: 0.000 \rightarrow 0.353) and Ukraine (0.167 \rightarrow 0.500), the vocabulary-only condition recovers no more than generic reflection while action routing breaks the degenerate prior. These findings identify typed action routing -- not diagnostic scaffolding or taxonomy vocabulary -- as a promising design principle for metacognitive LLM forecasting agents, while motivating larger-scale evaluation across conflict typologies.