Centralized Refinement with Planning converges fastest and to the lowest failure count on authenticity¶

Claim · All claims
Evidence · 1 study · design r2 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? For authenticity, Centralized Refinement with Planning converges quicker and to the lowest failure count after the first refinement iteration, while Decentralized Refinement converges more slowly. → Fareya Ikram 2026

Evidence¶

Fareya Ikram 2026¶

Fareya Ikram, Nischal Ashok Kumar, Junyang Lu, Hunter McNichols, Candace Walkington, Neil Heffernan, and Andrew S. Lan. (2026). A Multi-Agent Approach to Validate and Refine LLM-Generated Personalized Math Problems. arXiv preprint. https://arxiv.org/abs/2604.05160 doi:10.48550/arxiv.2604.05160

q2 · i? · design · r2

Strategy comparison on the same 600-problem ASSISTments evaluation, reported in the quantitative analysis section with failure counts across refinement iterations in Fig. 2c. The article reports the authenticity convergence contrast between Centralized with Planning and Decentralized Refinement; no effect size is printed.

"For authenticity, we observe that Centralized with Planning converges quicker and to the lowest failure count after the first refinement iteration. Decentralized Refinement, however, converges more slowly."

Discussion¶

Related Claims¶

• Decentralized Refinement reduces realism and readability failures faster than both Centralized strategies — related
• Authenticity and realism are the most frequent failure modes in initial LLM-personalized math problems, and a single refinement iteration substantially reduces failures — related
• The three refinement strategies make distinct trade-offs when personalizing problems to topics that resist natural alignment with the original math — related
• Validator failure patterns vary by interest topic and math curriculum unit — related