Papers by David Acuna
Socratic-MCTS: Test-Time Visual Reasoning by Asking the Right Questions (2025.emnlp-main)
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| Challenge: | Recent research in vision-language models has centered around the possibility of equipping them with implicit long-form chain-of-thought reasoning via distillation and reinforcement learning. |
| Approach: | They propose a Monte Carlo Tree Search-inspired algorithm that injects subquestion–subanswer pairs into the model’s output stream to elicit hidden knowledge and induce long reasoning traces. |
| Outcome: | The proposed method yields a 2% improvement on MMMU-PRO, including a significant 9% gain in Liberal Arts. |
Reasoning Paths with Reference Objects Elicit Quantitative Spatial Reasoning in Large Vision-Language Models (2024.emnlp-main)
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| Challenge: | Despite recent advances in visual language models, their ability to quantitatively reason about object sizes and distances remains underexplored. |
| Approach: | They propose a manually annotated benchmark of 241 questions designed for quantitative spatial reasoning and a zero-shot prompting technique that encourages VLMs to use reference objects as visual cues. |
| Outcome: | The proposed technique improves the performance of the top-performing VLMs by 19 points when a reasoning path using a reference object emerges naturally in the response. |