{"slug": "scaffolding-the-strategist-architecture-dependent-reasoning-interventions-in", "title": "Scaffolding the Strategist: Architecture-Dependent Reasoning Interventions in Hotelling Spatial Markets", "summary": "Researchers at an undisclosed institution tested GPT-4.1-mini and GPT-5-mini on Hotelling's linear city model and found that reasoning interventions interact with model architecture: commitment scaffolding improved the standard model but degraded the reasoning model, while principled separation showed the opposite pattern. Adversarial stress-testing harmed both models, with the reasoning model suffering 2.6 times greater degradation. The study highlights a declarative-procedural gap where models identify correct strategies but fail to execute them.", "body_md": "arXiv:2607.09743v1 Announce Type: new\nAbstract: We investigate whether structured reasoning interventions improve the strategic economic reasoning of large language models, and whether their effects depend on model architecture. Using Hotelling's linear city model as a diagnostic vehicle, we evaluate GPT-4.1-mini (a standard instruction-following model) and GPT-5-mini (a reasoning-optimized model) under five conditions - an unscaffolded baseline and four reasoning interventions - across eight questions spanning deductive and abductive reasoning, three prompt framings, and three repetitions per condition, yielding 720 individually judged responses. We find a statistically significant crossover interaction between scaffolding type and model architecture ($t(7) = 4.79$, $p = 0.002$, $d = 1.69$): commitment scaffolding improves the standard model ($+0.21$) while degrading the reasoning model ($-0.63$), and principled separation shows the opposite pattern ($-0.40$ vs. $+0.31$). Both crossovers are individually significant (commitment: $p = 0.040$; separation: $p = 0.002$) and hold across all eight questions with 7/8 directional consistency. Adversarial stress-testing harms both models, with $2.6\\times$ greater degradation for the reasoning model ($-1.47$ vs. $-0.57$; $p = 0.038$), and the damage correlates negatively with baseline difficulty ($R^2 = 0.36$, $p = 0.014$). We further document a persistent declarative-procedural gap in which both models identify correct strategies at rates far exceeding their ability to execute them; separation fully closes this gap for the reasoning model while no intervention helps the standard model.", "url": "https://wpnews.pro/news/scaffolding-the-strategist-architecture-dependent-reasoning-interventions-in", "canonical_source": "https://arxiv.org/abs/2607.09743", "published_at": "2026-07-14 04:00:00+00:00", "updated_at": "2026-07-14 04:24:17.967064+00:00", "lang": "en", "topics": ["large-language-models", "artificial-intelligence", "ai-research"], "entities": ["GPT-4.1-mini", "GPT-5-mini"], "alternates": {"html": "https://wpnews.pro/news/scaffolding-the-strategist-architecture-dependent-reasoning-interventions-in", "markdown": "https://wpnews.pro/news/scaffolding-the-strategist-architecture-dependent-reasoning-interventions-in.md", "text": "https://wpnews.pro/news/scaffolding-the-strategist-architecture-dependent-reasoning-interventions-in.txt", "jsonld": "https://wpnews.pro/news/scaffolding-the-strategist-architecture-dependent-reasoning-interventions-in.jsonld"}}