Socratic-MCTS: Test-Time Visual Reasoning by Asking the Right Questions
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| Format: | Preprint |
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2025
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| author | Acuna, David Lu, Ximing Jung, Jaehun Kim, Hyunwoo Kar, Amlan Fidler, Sanja Choi, Yejin |
| author_facet | Acuna, David Lu, Ximing Jung, Jaehun Kim, Hyunwoo Kar, Amlan Fidler, Sanja Choi, Yejin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_08927 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Socratic-MCTS: Test-Time Visual Reasoning by Asking the Right Questions Acuna, David Lu, Ximing Jung, Jaehun Kim, Hyunwoo Kar, Amlan Fidler, Sanja Choi, Yejin Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language 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. |
| title | Socratic-MCTS: Test-Time Visual Reasoning by Asking the Right Questions |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2506.08927 |