On the PRM800K validation dataset, detection rates dropped by 10.5 percentage points when final answers were correct, consistent with the correct answer trap extending beyond the Eedi data¶
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Currency · newest study 2026 · not yet checked against its sources · not reviewed by a person · recheck by 2029: tool and model accuracy changes quickly
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q2 i? On PRM800K, the best model's detection rates dropped by 10.5 percentage points when final answers were correct (83% vs 93.5%, Fisher's exact p <0.003). → Moiz Imran and Sahan Bulathwela 2026
Evidence¶
Moiz Imran and Sahan Bulathwela 2026¶
Moiz Imran and Sahan Bulathwela. (2026). Catching TheCorrectAnswerTrap: Characterising AI Tutor Blind Spots When Analysing Student Reasoning. arXiv preprint. https://arxiv.org/abs/2605.23925 doi:10.48550/arxiv.2605.23925
q2 · i? · associational · r?
Validation on PRM800K, a process supervision dataset with human-labelled reasoning errors in model-generated competition mathematics, sampling 200 correct-answer error cases and 200 matched wrong-answer cases. Detection dropped "by 10.5 percentage points" for correct-answer cases.
"Detection rates dropped by 10.5 percentage points when final answers were correct (83% vs 93.5%, Fisher's exactp <0.003), consistent with our primary findings."
Discussion¶
Related Claims¶
- Frontier LLMs achieve the best balance between detecting misconceptions in correct-answer cases and not over-flagging correct students, while fine-tuned local models achieve under 58% TM recall — related
- Fine-tuned classifiers detect only about 57% of correct-answer misconceptions despite near-perfect wrong-answer classification — related
- Correct-answer-trap cases concentrate in specific question types where common flawed reasoning happens to produce the correct numerical answer, with the top two question types accounting for 71% of all TM cases — related
- LLMs systematically underestimate fraction items whose difficulty is driven by misconceptions (the \"Easy Trap\") — related
- Without step-level supervision, neither the student model nor the tutor model can acquire misconceptions regardless of training data size — related
- The system's error detection recall rate for complex rhythm errors was 91.2% ± 1.8% — related
- A task-specific prompt with explicit guidance about correct-answer cases significantly outperforms literature-based PedCoT prompting on TM test cases (84% vs 59% accuracy) — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
cat-extends-to-prm800k-validation |
title |
On the PRM800K validation dataset, detection rates dropped by 10.5 percentage points when final answers were correct, consistent with the correct answer trap extending beyond the Eedi data |
description |
On the PRM800K validation dataset, detection rates dropped by 10.5 percentage points when final answers were correct, consistent with the correct answer trap extending beyond the Eedi data |
status |
review |
evidence_strength |
weak |
newest_evidence |
2026 |
recheck_by |
2029 |
generated |
by process:wiki-ingest on 2026-10-10 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — moiz-imran-and-sahan-bulathwela-2026 |
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