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arXiv · 2506.08927

Socratic-MCTS: Test-Time Visual Reasoning by Asking the Right Questions

David Acuna·Ximing Lu·Jaehun Jung·Hyunwoo Kim·Amlan Kar·Sanja Fidler·Yejin Choi
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Abstract

Recent research in vision-language models (VLMs) has centered around the possibility of equipping them with implicit long-form chain-of-thought reasoning -- akin to the success observed in language models -- via distillation and reinforcement learning. But what about the non-reasoning models already trained and deployed across the internet? Should we simply abandon them, or is there hope for a search mechanism that can elicit hidden knowledge and induce long reasoning traces -- without any additional training or supervision? In this paper, we explore this possibility using a Monte Carlo Tree Search (MCTS)-inspired algorithm, which injects subquestion-subanswer pairs into the model's output stream. We show that framing reasoning as a search process -- where subquestions act as latent decisions within a broader inference trajectory -- helps the model "connect the dots" between fragmented knowledge and produce extended reasoning traces in non-reasoning models. We evaluate our method across three benchmarks and observe consistent improvements. Notably, our approach yields a 2% overall improvement on MMMU-PRO, including a significant 9% gain in Liberal Arts.

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David Acuna, Ximing Lu, Jaehun Jung, Hyunwoo Kim, Amlan Kar, Sanja Fidler, Yejin Choi. 2025-06-10. Socratic-MCTS: Test-Time Visual Reasoning by Asking the Right Questions. https://arxiv.org/abs/2506.08927

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