Efficacy of multimodal interleaved chain-of-thought for surpassing mathematical performance limits

Determine whether multimodal interleaved verbal–visual chain-of-thought reasoning can fundamentally surpass current performance limits in mathematical reasoning, given the completeness of symbolic mathematical representations and the extensive optimization of mathematical reasoning in current large language models.

Background

The paper discusses the use of visual generation for STEM reasoning, such as diagram editing in mathematics, which mirrors human use of visual sketchpads to support understanding. However, it emphasizes that mathematical symbolism is largely complete and that LLM-based mathematical reasoning has been heavily optimized.

Given these conditions, the authors question whether adding interleaved visual generation to verbal reasoning can truly break existing performance ceilings in mathematics, identifying this as an unresolved issue requiring further investigation.

References

However, as symbolic representations in mathematics are largely complete, and mathematical reasoning has been extensively optimized in modern LLMs, it remains unclear whether multimodal interleaved CoT can fundamentally break through the performance limit, warranting further investigation.

— Visual Generation Unlocks Human-Like Reasoning through Multimodal World Models  (2601.19834 - Wu et al., 27 Jan 2026) in Section 6: Discussions — Limitations and future work