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[논문] OmniRefine: Alignment-Aware Cooperative Compression for Efficient Omnimodal Large Language Models https://arxiv.org/abs/2605.12056 OmniRefine: Alignment-Aware Cooperative Compression for Efficient Omnimodal Large Language ModelsOmnimodal large language models (Omni-LLMs) show strong capability in audio-video understanding, but their practical deployment remains limited by high inference cost of long video streams and dense audio sequences. Despite recent progress, existing comprearxiv.org해당 .. 연구실 공부 2026. 10. 30.
[논문] AVOC: Enhancing Hour-Level Audio-Video Understanding in Omni-Modal LLMs via Retrieval-Inspired Token Compression https://arxiv.org/abs/2606.24286 AVOC: Enhancing Hour-Level Audio-Video Understanding in Omni-Modal LLMs via Retrieval-Inspired Token CompressionMultimodal Large Language Models have achieved remarkable progress in short-form audio-video understanding, yet long-form audio-video comprehension remains challenged by limited context windows and severe information redundancy. To address these bottlen.. 연구실 공부 2026. 10. 16.
[논문] OmniFit: Bridging Modalities via Layer-Adaptive Token Compression for Omnimodal Large Language Models https://openreview.net/forum?id=8RY20mLzup&noteId=5ynjDMkxB1 Verifying your browser | OpenReviewVerifying your browser Complete the check below to continue to OpenReview Please complete the verification above. Have an OpenReview account? Sign in to skip this check.openreview.net해당 논문을 보고 작성하였습니다. Abstract기존의 token compression strategy는 일반적으로 특정 modality에 편향된 사전 가정에 의존하거나, 모든 layer와 modality에 동일한.. 연구실 공부 2026. 10. 15.
[논문] MMG-Vid: Maximizing Marginal Gains at Segment-level and Token-level for Efficient Video LLMs https://arxiv.org/abs/2508.21044 MMG-Vid: Maximizing Marginal Gains at Segment-level and Token-level for Efficient Video LLMsVideo Large Language Models (VLLMs) excel in video understanding, but their excessive visual tokens pose a significant computational challenge for real-world applications. Current methods aim to enhance inference efficiency by visual token pruning. Howeverarxiv.org해당 논문을 보.. 연구실 공부 2026. 10. 15.
[논문] The Imperfective Paradox in Large Language Models https://aclanthology.org/2026.acl-long.689/ The Imperfective Paradox in Large Language ModelsBolei Ma, Yusuke Miyao. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.aclanthology.org해당 논문을 보고 작성하였습니다. AbstractLLM은 사건의 compositional semantic을 실제로 이해하는가, 아니면 표면적인 heuristic에 의존하는가? 본 연구에서는 imperfective paradox를 조사한다. 이는 과거진행형이 활동 .. 연구실 공부 2026. 10. 9.
[논문] Preserving LLM Capabilities through Calibration Data Curation: From Analysis to Optimization https://arxiv.org/abs/2510.10618 Preserving LLM Capabilities through Calibration Data Curation: From Analysis to OptimizationPost-training compression has been a widely employed approach to scale down large language model (LLM) and facilitate efficient inference. In various proposed compression methods, including pruning and quantization, calibration data plays a vital role by iarxiv.org해당 논문을 보.. 연구실 공부 2026. 10. 8.
[논문] EchoingPixels: Aliasing-Resistant Joint Token Reduction for Audio-Visual LLMs https://openreview.net/forum?id=e8JTAKZYt6 Verifying your browser | OpenReviewVerifying your browser Complete the check below to continue to OpenReview Please complete the verification above. Have an OpenReview account? Sign in to skip this check.openreview.net해당 논문을 보고 작성하였습니다. AbstractAudio-Visual LLM (AV-LLM)은 많고 중복되는 audio-visual token을 처리할 때 사용되는 많은 연산량 문제가 존재한다. 기존 unimodal compression tec.. 연구실 공부 2026. 8. 31.
[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.
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