[2601.21439] The Paradox of Robustness: Decoupling Rule-Based Logic from Affective Noise in High-Stakes Decision-Making

Computer Science > Artificial Intelligence arXiv:2601.21439 (cs) [Submitted on 29 Jan 2026 (v1), last revised 4 Apr 2026 (this version, v2)] Title:The Paradox of Robustness: Decoupling Rule-Based Logic from Affective Noise in High-Stakes Decision-Making Authors:Jon Chun, Katherine Elkins View a PDF of the paper titled The Paradox of Robustness: Decoupling Rule-Based Logic from Affective Noise in High-Stakes Decision-Making, by Jon Chun and 1 other authors View PDF HTML (experimental) Abstract:While Large Language Models (LLMs) are widely documented to be sensitive to minor prompt perturbations and prone to sycophantic alignment, their robustness in consequential, rule-bound decision-making remains under-explored. We uncover a striking "Paradox of Robustness": despite their known lexical brittleness, aligned LLMs exhibit strong robustness to emotional framing effects in rule-bound institutional decision-making. Using a controlled perturbation framework across three high-stakes domains (healthcare, finance, and education), we find a negligible effect size (Cohen's h = 0.003) compared to the substantial biases observed in analogous human contexts (h in [0.3, 0.8]), approximately two orders of magnitude smaller. This invariance persists across eight models with diverse training paradigms, suggesting the mechanisms driving sycophancy and prompt sensitivity do not translate to failures in logical constraint satisfaction. While LLMs may be "brittle" to how a query is formatted, t...