The investigate–solve–write scaffold, with personalization disabled, improves all five backbone model families on general agentic benchmarks, with average relative gains of 25.69%–32.03%¶

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? With the Hybrid Personalization Engine disabled, the solver-only pipeline improves all five backbone families across HLE, GPQA-Diamond, LiveBench, GAIA, and AA-LCR, with average relative gains ranging from 25.69% to 32.03%. → Bingxi Zhao 2026

Evidence¶

Bingxi Zhao 2026¶

Bingxi Zhao, Jiahao Zhang, Xubin Ren, Zirui Guo, Tianzhe Chu, Yi Ma, Chao Huang. (2026). DeepTutor: Towards Agentic Personalized Tutoring. Technical Report, The University of Hong Kong. https://arxiv.org/abs/2604.26962 doi:10.48550/arxiv.2604.26962

q2 · i? · design · r2

Extended generalization evaluation on five public benchmarks (HLE, GPQA-Diamond, LiveBench, GAIA, AA-LCR) with personalization disabled, per backbone (Gemini-3-Flash, Sonnet-4.5, Qwen-3.5-Plus, GPT-5-Mini, Minimax-M2.5). Printed gains are relative percentages, not standardized effect sizes.

"As shown in Table 3, the solver-only pipeline improves all five backbone families, with average relative gains ranging from 25.69% to 32.03%. Because personalization, SKG, and DPM are disabled in this setting, the gains point to the general value of the investigate–solve–write scaffold"

Discussion¶

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