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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.</abstract>
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%0 Conference Proceedings
%T Socratic-MCTS: Test-Time Visual Reasoning by Asking the Right Questions
%A Acuna, David
%A Lu, Ximing
%A Jung, Jaehun
%A Kim, Hyunwoo
%A Kar, Amlan
%A Fidler, Sanja
%A Choi, Yejin
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F acuna-etal-2025-socratic
%X 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.
%U https://aclanthology.org/2025.emnlp-main.1230/
%P 24158-24171

Markdown (Informal)

[Socratic-MCTS: Test-Time Visual Reasoning by Asking the Right Questions](https://aclanthology.org/2025.emnlp-main.1230/) (Acuna et al., EMNLP 2025)

• Socratic-MCTS: Test-Time Visual Reasoning by Asking the Right Questions (Acuna et al., EMNLP 2025)

ACL

• David Acuna, Ximing Lu, Jaehun Jung, Hyunwoo Kim, Amlan Kar, Sanja Fidler, and Yejin Choi. 2025. Socratic-MCTS: Test-Time Visual Reasoning by Asking the Right Questions. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 24158–24171, Suzhou, China. Association for Computational Linguistics.