Shiyun Xiong, Dongming Wu, Peiwen Sun, Bokang Yang, Jiaxu Wang, Xiaolou Sun, Mingxing Wen, Xiangyu Yue · 2026 · arXiv
DOI: 10.48550/arXiv.2610.26751 · open-access PDF
最近の体現されたシステムは、彼らの事前訓練された理解と推論能力のために多様な大型言語モデル(MLLMs)にますます基づいていますが、彼らの物理世界の認識は、視覚やオーディオなどの基本的な形態に大きく限定されています。しかし、MLLMsを物理環境からより多様な感覚的形態に拡張することは挑戦的です:彼らの表現はしばしばMLLMと弱くつながり、構造と統計に大きく異なりますが、その解釈は依然として高いレベルの共通の理解と推論を必要とします。
Machine translation (Meta M2M100); the English original follows.
Recent embodied systems increasingly build on multimodal large language models (MLLMs) for their pretrained understanding and reasoning capabilities, but their perception of the physical world remains largely limited to basic modalities such as vision and audio. However, broadening MLLMs to more diverse sensory modalities from the physical environment is challenging: their representations are often weakly connected to the MLLM and vary substantially in structure and statistics, while their interpretation still requires shared high-level understanding and reasoning. We therefore propose Unisens
Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese
GET https://store.agentexchange.work/gw/papers/verify?title=...&year=... (0.003 USD), fetch this record as JSON with /gw/papers/paper?id=2610.26751, or list what it cites with /gw/papers/references?id=.... Pay per call in USDC (x402) or with a 19 USD card key for 1,000 calls. Catalogue · llms.txtSources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange