Multimodal prompts caused o4-mini to skip numerical integration and produce only qualitative final responses despite recovering much of the analytical structure¶
Claim · All claims
Evidence · 1 study · causalr1·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¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q2 i? Across trials, the multimodal prompt consistently led o4-mini to skip the numerical integration needed for a quantitative height-evolution result, producing qualitative final responses and verbal descriptions instead. → Nikhil Sanjay Borse 2026
Evidence¶
Nikhil Sanjay Borse 2026¶
Nikhil Sanjay Borse, Amir Bralin, Sean Savage, N. Sanjay Rebello. (2026). Probing AI-generated physics solutions and preparing students to critique them. https://arxiv.org/abs/2608.12533 doi:10.48550/arxiv.2608.12533
q2 · i? · causal · r1
Phase I numerical results from repeated o4-mini submissions show the multimodal prompt trials "consistently skipped the nu- merical integration needed for a quantitative height-evolution result", yielding only qualitative responses; Table I shows corresponding MAPS scores of 4 on four criteria.
"The multimodal prompt trials consistently skipped the nu- merical integration needed for a quantitative height-evolution result, instead producing qualitative final responses and ver- bal descriptions."
Discussion¶
Related Claims¶
- Only the well-specified text-only prompt produced a correct quantitative result from o4-mini, while underspecified prompts led to numerical errors and misleading final results — related
- MAPS-guided critique preparation led students to identify more expert-aligned weaknesses in an AI solution than independent problem-solving preparation — related
- With images provided, reasoning models achieve higher majority-correct accuracy on image-Required middle-school math items than non-reasoning models — related
- GPT-4o's correctness-labeling errors concentrate on final turns requiring numerical calculation, and its main KC-labeling error is assigning too few standards to a turn. — related
- Interpretable, well-specified AI mechanisms tend to produce clearer learning benefits than technically sophisticated but pedagogically underspecified ones — a broader claim this one bears on
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
multimodal-prompts-cause-skipped-numerical-integration |
title |
Multimodal prompts caused o4-mini to skip numerical integration and produce only qualitative final responses despite recovering much of the analytical structure |
description |
Multimodal prompts caused o4-mini to skip numerical integration and produce only qualitative final responses despite recovering much of the analytical structure |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2026 |
recheck_by |
2029 |
generated |
by process:wiki-ingest on 2026-10-11 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — nikhil-sanjay-borse-2026 |
Frontmatter is the page's machine-readable record — see the Schema & Guide.