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[논문] SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics https://arxiv.org/abs/2506.01844 SmolVLA: A Vision-Language-Action Model for Affordable and Efficient RoboticsVision-language models (VLMs) pretrained on large-scale multimodal datasets encode rich visual and linguistic knowledge, making them a strong foundation for robotics. Rather than training robotic policies from scratch, recent approaches adapt VLMs into visarxiv.org해당 논문을 보고 작성하였습니다. Abst.. 연구실 공부 2026. 8. 22.
[논문] EgoVLA: Learning Vision-Language-Action Models from Egocentric Human Videos https://arxiv.org/abs/2507.12440 EgoVLA: Learning Vision-Language-Action Models from Egocentric Human VideosReal robot data collection for imitation learning has led to significant advancements in robotic manipulation. However, the requirement for robot hardware in the process fundamentally constrains the scale of the data. In this paper, we explore training Visarxiv.org해당 논문을 보고 작성하였습니다. Abstra.. 연구실 공부 2026. 8. 22.
[논문] OmniSIFT: Modality-Asymmetric Token Compression for Efficient Omni-modal Large Language Models https://arxiv.org/abs/2602.04804 OmniSIFT: Modality-Asymmetric Token Compression for Efficient Omni-modal Large Language ModelsOmni-modal Large Language Models (Omni-LLMs) have demonstrated strong capabilities in audio-video understanding tasks. However, their reliance on long multimodal token sequences leads to substantial computational overhead. Despite this challenge, token comarxiv.org해당 논문을.. 연구실 공부 2026. 8. 19.
[논문] OmniFocus: Query-Guided Modality-Balanced Token Compression for Omni-Modal Large Language Models https://arxiv.org/abs/2607.03050 OmniFocus: Query-Guided Modality-Balanced Token Compression for Omni-Modal Large Language ModelsOmni modal large language models (OmniLLMs) have attracted wide attention for their ability to jointly process audio and video, but they generate large token sequences under audio-visual inputs, leading to substantial inference cost. Existing audio-visualarxiv.org해당 논문.. 연구실 공부 2026. 8. 19.
[논문] Omni-Prune: Query-Aware Unified Token Pruning for Efficient Omnimodal Large Language Models https://arxiv.org/abs/2607.23445 Omni-Prune: Query-Aware Unified Token Pruning for Efficient Omnimodal Large Language ModelsOmnimodal large language models (OmniLLMs) are rapidly extending multimodal reasoning to cover synchronized audio and video. However, the resulting audio-video token sequences are long, leading to high prefill latency and GPU memory usage at inference timearxiv.org해당 논문을 보고.. 연구실 공부 2026. 8. 18.
[논문] Token Pruning in Multimodal Large Language Models: Are We Solving the Right Problem? https://aclanthology.org/2025.findings-acl.802/ Token Pruning in Multimodal Large Language Models: Are We Solving the Right Problem?Zichen Wen, Yifeng Gao, Weijia Li, Conghui He, Linfeng Zhang. Findings of the Association for Computational Linguistics: ACL 2025. 2025.aclanthology.org해당 논문을 보고 작성하였습니다. AbstractMLLMs은 cross-modal understanding and generation에서 뛰어난 성능을 보여주지만 inference cost가 많이 든다는 .. 연구실 공부 2026. 8. 16.
[논문] FastAV: Efficient Token Pruning for Audio-Visual Large Language Model Inference https://ieeexplore.ieee.org/abstract/document/11463130 FastAV: Efficient Token Pruning for Audio-Visual Large Language Model InferenceIn this work, we present FastAV, the first token pruning framework tailored for audio-visual large language models (AV-LLMs). While token pruning has been actively explored in standard large language models (LLMs) and vision-language models (LVLMs), its apieeexplo.. 연구실 공부 2026. 8. 14.
[논문] GSR: Learning Structured Reasoning for Embodied Manipulation https://arxiv.org/abs/2602.01693 GSR: Learning Structured Reasoning for Embodied ManipulationDespite rapid progress, embodied agents still struggle with long-horizon manipulation that requires maintaining spatial consistency, causal dependencies, and goal constraints. A key limitation of existing approaches is that task reasoning is implicitly embarxiv.org해당 논문을 보고 작성하였습니다. Abstractembodied agen.. 연구실 공부 2026. 8. 12.
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