Step complexity, distance to conclusion, and inference rule explain negligible diagnostic variance, with complexity predicting misdiagnosis only for optimal steps¶

Claim · All claims
Evidence · 1 study (3 entries) · causal r2 · q2
Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q2 i? Step complexity predicted misdiagnosis only for optimal solutions (misdiagnosed M = 2.46 vs correctly classified M = 1.95, p < .05) and explained negligible variance overall (η² < .01). → Tahreem Yasir 2026 q2 i? Over-rejection was highest at near distances to the conclusion, and distance to conclusion explained negligible variance (η² < .01). → Tahreem Yasir 2026 (2) q2 i? Classification failures concentrated on structurally complex inference rules (MT, CD, DeM). → Tahreem Yasir 2026 (3)

Evidence¶

Tahreem Yasir 2026¶

Tahreem Yasir, Wenbo Li, Sam Gilson, Sutapa Dey Tithi, Xiaoyi Tian, Tiffany Barnes. (2026). Confirming Correct, Missing the Rest: LLM Tutoring Agents Struggle Where Feedback Matters Most. arXiv:2605.16207. https://arxiv.org/abs/2605.16207 doi:10.48550/arxiv.2605.16207

q2 · i? · causal · r2

Complexity analysis (RQ2) pooling feedback agents across models after conditions showed negligible effects. For valid-alternative and incorrect solutions complexity did not predict errors (p > .08); agents rejected simpler valid-alternative solutions (M = 2.97) more often than complex ones (M = 3.62).

"complexity predicted misdiagnosis only for Optimal solutions: more complex steps were misdiagnosed more often (M= 2.46 ) than correctly classified ones (M= 1.95;p<.05)."

Tahreem Yasir 2026 (2)¶

Tahreem Yasir, Wenbo Li, Sam Gilson, Sutapa Dey Tithi, Xiaoyi Tian, Tiffany Barnes. (2026). Confirming Correct, Missing the Rest: LLM Tutoring Agents Struggle Where Feedback Matters Most. arXiv:2605.16207. https://arxiv.org/abs/2605.16207

q2 · i? · causal · r2

Distance-tier analysis (RQ2) using near (d ≤ 2), mid, and far partitions, reported via Figure 19 and accompanying text. The result is a plotted pattern described in text, supporting model-level rather than position-driven failure.

"Figure 19 shows that over-rejection is highest at near distances, meaning agents most often penalize valid-alternative reasoning when solutions are closest to the conclusion. Distance to conclusion explained negligible variance (η2 < .01)"

Tahreem Yasir 2026 (3)¶

Tahreem Yasir, Wenbo Li, Sam Gilson, Sutapa Dey Tithi, Xiaoyi Tian, Tiffany Barnes. (2026). Confirming Correct, Missing the Rest: LLM Tutoring Agents Struggle Where Feedback Matters Most. arXiv:2605.16207. https://arxiv.org/abs/2605.16207

q2 · i? · design · r2

Per-inference-rule F1 comparison (RQ2, Figure 20). LLaMA showed minimal gains across most rule types while Gemini and DeepSeek consistently achieved the highest scores; no effect size is printed.

"Analysis of inference rules and F1 scores (Figure 20) shows that failures con- centrate on structurally complex rules (MT, CD, DeM)."

Discussion¶

Related Claims¶

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