Ablations show each CogEvolution module contributes: removing ICAP perception, structured retrieval, or evolutionary update degrades mistake precision, learning-curve fit, and alignment¶

Claim · All claims
Evidence · 1 study (3 entries) · design r2 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person · recheck by 2029: tool and model accuracy changes quickly

Subclaims¶

q2 i? Removing ICAP cognitive depth perception drops R²LC sharply to 0.58 (from 0.92). → Wei Zhang 2026 q2 i? Removing structured retrieval decreases Mistake Precision by 12.3% (to 64.5%). → Wei Zhang 2026 (2) q2 i? Removing the evolutionary update mechanism significantly reduces mistake fidelity and alignment, falling back to static persona modeling (Mistake Precision 55.1%, R²LC 0.51). → Wei Zhang 2026 (3)

Evidence¶

Wei Zhang 2026¶

Wei Zhang, Yihang Cheng, Zhirong Ye & Kezhen Huang. (2026). CogEvolution: A Human-like Generative Educational Agent to Simulate Student's Cognitive Evolution. https://arxiv.org/abs/2604.14786 doi:10.48550/arxiv.2604.14786

q2 · i? · design · r2

Ablation experiment (Table 3) on CogMath-948 with the w/o ICAP variant; the article reports "R²LC todropsharplyto0.58" alongside Mistake Precision 68.5% and Align 0.73. No effect size is printed.

"Removing cognitive depth perception renders the agent unable to distinguish between shallow and deep learning,causing𝑅 2𝐿𝐶 todropsharplyto0.58,demonstrating the validity of cognitive features in simulating agent cognitive psychological validity."

Wei Zhang 2026 (2)¶

Wei Zhang, Yihang Cheng, Zhirong Ye & Kezhen Huang. (2026). CogEvolution: A Human-like Generative Educational Agent to Simulate Student's Cognitive Evolution. https://arxiv.org/abs/2604.14786

q2 · i? · design · r2

Ablation experiment (Table 3) with the w/o Meta-Ret variant reports a "12.3%decreaseinMistakePrecision" (76.8% to 64.5%), with R²LC 0.85 and Align 0.88. No effect size is printed.

"Removing structured retrieval leads to a 12.3%decreaseinMistakePrecision,indicatingthatknowl-edge assimilation is crucial for reproducing specific cogni-tive misconceptions."

Wei Zhang 2026 (3)¶

Wei Zhang, Yihang Cheng, Zhirong Ye & Kezhen Huang. (2026). CogEvolution: A Human-like Generative Educational Agent to Simulate Student's Cognitive Evolution. https://arxiv.org/abs/2604.14786

q2 · i? · design · r2

Ablation experiment (Table 3) with the w/o Evo-Update variant reports Mistake Precision 55.1% (down 21.7), R²LC 0.51 (down 0.41), and Align 0.76, which the article describes as falling back to static persona modeling. No effect size is printed.

"Removing the evolutionary update mechanismresultsinsignificantlyreducedbehavioralmis-take fidelity and cognitive alignment (Align), causing the agent's simulation capability to fall back to static persona modeling."

Discussion¶

Related Claims¶

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