What Drives LLM Self-Reflection? A Controlled Ablation of Uncertainty Routing in Armed Conflict Forecasting
A six-condition ablation of LLM self-reflection in armed conflict forecasting isolates the mechanism behind gains. Structured diagnostic scaffolding and taxonomy vocabulary add no measurable value, while typed action routing drives consistent F1 improvements, replicating on GPT-4o and concentrating on novel conflicts.
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[Submitted on 29 May 2026]
Title:What Drives LLM Self-Reflection? A Controlled Ablation of Uncertainty Routing in Armed Conflict Forecasting
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Abstract: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 ($\text{F1} = 0.296$ 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 ($\Delta\text{F1} = +0.008$, overlapping 95\% CIs), ruling out taxonomy vocabulary as the mechanism. Typed action routing provides consistent directional gains ($\text{F1} = 0.379$ vs $0.296$); the conservative estimate controlling for taxonomy vocabulary is $\Delta\text{F1} = +0.075$, and the overall gain over the single-shot baseline is significant by bootstrap CI ($\Delta\text{F1} = +0.101$, 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 ($\text{F1}: 0.000 \rightarrow 0.353$) 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.
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2608.12322 [cs.CL]
(or arXiv:2608.12322v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.12322
arXiv-issued DOI via DataCite
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From: Poli Apollinaire Nemkova [view email] [v1] Fri, 29 May 2026 03:48:38 UTC (32 KB)
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