Papers by Jaehong Yoon
Video-RTS: Rethinking Reinforcement Learning and Test-Time Scaling for Efficient and Enhanced Video Reasoning (2025.emnlp-main)
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| Challenge: | Despite advances in reinforcement learning, data collection and fine-tuning remain costly and hard to scale. |
| Approach: | They propose a video-adaptive test-time scaling strategy that combines RL with a supervised fine-tuning strategy to improve video reasoning capability. |
| Outcome: | The proposed method surpasses existing models by 2.4% in accuracy using only 3.6% training samples. |
DART: Leveraging Multi-Agent Disagreement for Tool Recruitment in Multimodal Reasoning (2026.eacl-long)
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Nithin Sivakumaran, Justin Chen, David Wan, Yue Zhang, Jaehong Yoon, Elias Stengel-Eskin, Mohit Bansal
| Challenge: | a key strength of human intelligence is the ability to debate and discuss reasoning with others. |
| Approach: | They propose a multi-agent framework that uses disagreements between visual agents to identify useful visual tools that can resolve inter-agency disagreement. |
| Outcome: | The proposed framework beats the strongest baseline on A-OKVQA and MMMU, respectively. |
MEXA: Towards General Multimodal Reasoning with Dynamic Multi-Expert Aggregation (2025.findings-emnlp)
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| Challenge: | MEXA is a training-free framework that performs modality- and task-aware aggregation of multiple expert models to enable effective multimodal reasoning across diverse domains. |
| Approach: | MEXA is a training-free framework that performs modality- and task-aware aggregation of multiple expert models. |
| Outcome: | MEXA performs modality- and task-aware aggregation of multiple expert models . it generates interpretable textual reasoning outputs and reasons over them using a Large Reasoning Model (LRM) MEX A consistently delivers performance improvements over strong multimodal benchmarks . |
RACCooN: Versatile Instructional Video Editing with Auto-Generated Narratives (2025.emnlp-main)
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| Challenge: | Recent video generative models rely on detailed, labor-intensive text prompts for tasks, limiting adaptability for personal/raw videos. |
| Approach: | They propose a video-to-paragraph-to video editing method that supports diverse video editing capabilities, such as removal, addition, and modification, through a unified pipeline. |
| Outcome: | The proposed method supports diverse video editing capabilities, such as removal, addition, and modification, through a unified pipeline. |
Self-Correcting Text-to-Video Generation with Misalignment Detection and Localized Refinement (2026.findings-acl)
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| Challenge: | Recent text-to-video models struggle to faith-fully follow text prompts, authors say . authors propose a new refinement framework that detects fine-grained misalignments . |
| Approach: | They propose a video refinement framework that detects fine-grained misalignments . they propose preserving regions that should be preserved rather than regenerated . |
| Outcome: | The proposed framework detects fine-grained misalignments and performs targeted corrections . it preserves correctly generated entities, segments regions across frames, and regenerates problematic regions . |
Mementos: A Comprehensive Benchmark for Multimodal Large Language Model Reasoning over Image Sequences (2024.acl-long)
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Xiyao Wang, Yuhang Zhou, Xiaoyu Liu, Hongjin Lu, Yuancheng Xu, Feihong He, Jaehong Yoon, Taixi Lu, Fuxiao Liu, Gedas Bertasius, Mohit Bansal, Huaxiu Yao, Furong Huang
| Challenge: | Multimodal Large Language Models (MLLMs) have demonstrated proficiency in handling a variety of visual-language tasks, but their ability to extrapolate from image sequences has been less investigated. |
| Approach: | They propose a new benchmark to assess MLLMs’ sequential image reasoning abilities. |
| Outcome: | The proposed benchmark features 4,761 diverse image sequences with varying lengths. |
Video-Skill-CoT: Skill-based Chain-of-Thoughts for Domain-Adaptive Video Reasoning (2025.findings-emnlp)
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| Challenge: | Existing methods for chain-of-thought reasoning fail to adapt to domain-specific skills over video content. |
| Approach: | They propose a framework that automatically constructs and leverages skill-aware CoT supervisions for domain-adaptive video reasoning. |
| Outcome: | The proposed framework outperforms strong baselines on three video understanding benchmarks. |
Carpe diem: On the Evaluation of World Knowledge in Lifelong Language Models (2024.naacl-long)
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| Challenge: | Current language models are trained on static data, implying that the encoded knowledge could go wrong as time passes. |
| Approach: | They propose a temporally evolving question-answering benchmark for language models . they use Wikipedia databases to test language models for dynamic knowledge in ever-changing world . |
| Outcome: | The proposed task aims to model the evolution-adaptability of language models in the real world. |
Glider: Global and Local Instruction-Driven Expert Router (2025.emnlp-main)
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| Challenge: | Existing methods for routing-based expert models favor generalization over performance on held-in tasks. |
| Approach: | They propose a global and local instruction driven expert router that leverages recent LLMs' semantic reasoning capabilities to generate task-specific instructions from the input query. |
| Outcome: | The proposed method improves held-in performance while maintaining strong generalization on held-out tasks. |