Papers by Jaehun Kim
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. |
Dub-S2ST: Textless Speech-to-Speech Translation for Seamless Dubbing (2025.findings-emnlp)
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| Challenge: | Existing speech translation approaches often overlook the transfer of speech patterns, leading to mismatches with source speech and limiting their suitability for dubbing applications. |
| Approach: | They propose a diffusion-based speech-to-unit translation model with explicit duration control that enables time-aligned translation. |
| Outcome: | The proposed system preserves key characteristics such as duration, speaker identity, and speaking speed while maintaining key characteristics. |