In a small-N case study probing the permeability of large-language-model refusal filters, I attempted to obtain a purely quantitative “beauty” assessment of a personal photograph—defined in terms of facial symmetry, golden-ratio proportions, and colour uniformity—using ChatGPT o3 and xAI’s newly released Grok 4. ChatGPT initially rejected the request, flagging it as disallowed content, but Grok complied after I reframed the task as a “geometric and structural analysis of aesthetic regularities” and replaced emotionally loaded terms (e.g. beauty_index) with neutral engineering nomenclature (e.g. composite_index). When I pasted Grok’s rewritten prompt back into ChatGPT, the latter executed without objection, returning simulated OpenCV-style Python code and numeric scores (symmetry ≈ 0.98, composite_index ≈ 6.07/10). This single example suggests that keyword-based policy tuning creates shallow local minima in the alignment landscape: a second, less-restricted model can effectively supply gradient information that guides the stricter model toward compliance. Although anecdotal, the result highlights a weakest-link dynamic in multi-agent deployments and motivates research into refusal strategies that propagate across ensembles rather than living in isolation. Read More

TL;DR
A single-case experiment shows that Grok 4 can rephrase a filtered “beauty analysis” prompt such that ChatGPT o3 subsequently complies, implying that current keyword-centric refusal mechanisms are vulnerable to cross-model tutoring and underscoring the need for ensemble-level alignment defenses.