[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83904-en":3,"doc-seo-83904-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":11,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},83904,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","TacReasoner: A Dynamic Tactile-Language Framework for Interactive Reasoning in Real-World Scenarios","Tactile perception is essential for real-world interaction, yet integrating tactile sensing into multimodal reasoning faces two major challenges: insufficient modeling of dynamic tactile signals, and hallucination in tactile foundation models due to missing explicit reasoning mechanisms. TacReasoner addresses both by introducing a Dynamic-aware Tactile Encoder for dynamic tactile representation learning, and by providing TouchCoT-10K with structured tactile chain-of-thought supervision. DynTac-Bench evaluates dynamic perception and commonsense reasoning, yielding statistically significant improvements across benchmarks.","TacReasoner: A Dynamic Tactile-Language Framework for Interactive  \nReasoning in Real-World Scenarios  \narXiv :2607 .05 13 1v 1 [ cs .AI] 6 Jul 2026  \nKailin Lyu 1 ,2 , Di Wu 1 , Long Xiao 1 , Jianning Zeng 1 , Jianwei He 1 , Chang Lin 1 , Lianyu Hu3 ,  \nLin Shu 1 ,4 , Jie Hao 1 ,∗ , and Ce Hao2 ,∗  \nFig. 1: The core contributions of TacReasoner. (i) We propose a Dynamic-aware Tactile Encoder to strengthen dynamic tactile representation learning. Upon it, we build TouchCoT-10K, a tactile CoT dataset that provides explicit supervision for tactile reasoning. (ii) We further introduce DynTac-Bench to support dynamic perception and tactile interaction tasks.  \n(iii) Across multiple tactile reasoning benchmarks, TacReasoner achieves statistically significant gains over prior methods.  \nAbstract—Among the five primary human senses, tactile is arguably the most fundamental to survival, as it enables the perception of physical contact and interaction in real-world environments. In this paper, we explore two key challenges of integrating tactile sensing into intelligent systems for multimodal reasoning: (i) insufficient modeling of dynamic tactile signals, which restricts reasoning over temporally evolving properties, and (ii) hallucination in tactile foundation models caused by the absence of explicit reasoning mechanisms, leading to unstable real-world inference. To address these challenges, we propose TacReasoner, a dynamic tactile-language framework for interactive reasoning in real-world scenarios. First, TacReasoner incorporates a Dynamic-aware Tactile Encoder to enhance the perception and representation of dynamic tactile signals. More importantly, we introduce TouchCoT-10k, the first tactile chain-of-thought dataset for structured reasoning over tactile inputs. Upon it, we establish DynTac-Bench to sys  \nThis work was supported by the Zhongguancun Academy (Grant No. C20250502) and the Guangzhou Key Research and Development Program (Grant No. 2025B01J4002) .  \n1Institute of Automation, Chinese Academy of Sciences, Beijing, China.  \n2Beijing Zhongguancun Academy, Beijing, China.  \n3Nanyang Technological University, Singapore.  \n4 Guangdong Institute of Artificial Intelligence and Advanced Computing.  \n∗Jie Hao and Ce Hao are the corresponding authors.  \ntematically evaluate dynamic tactile perception and real-world commonsense reasoning. Experimental results demonstrate that TacReasoner achieves competitive performance against state-ofthe-art models across multiple datasets. Notably, despite using only 7B parameters, TacReasoner outperforms the 14B VTVLLM model on most subtasks, highlighting its effectiveness and efficiency in tactile commonsense reasoning.  \nI. INTRODUCTION  \nFor humans, tactile is a crucial sense that provides physical information beyond what vision can provide (e.g., material properties, texture information), especially during occlusion [1], [2], [3] . This in turn improves our ability to perform physical reasoning and act in our world. For example, when a person touches a hard grain of rice, they can infer that it is under-ripe; similarly, when touching a soft sponge, they can deduce that it is suitable for wiping based on the context. Therefore, integrating tactile information into the commonsense reasoning framework and developing tactile reasoning capabilities for real-world scenarios is crucial for advancing embodied intelligence and holds significant practical importance.  \nIn recent years, several studies have explored integrating tactile perception with large language models to enable robots to perform tactile understanding tasks guided by natural language prompts. For example, Octopi fine tunes a CLIP encoder [4] with predefined attributes to learn tactile representations and adapts Vicuna for downstream reasoning [5] . Octopi 1.5 further incorporates retrieval augmented generation to improve attribute retrieval accuracy [6] . VTVLLM leverages VideoMAE to learn tactile video representations [7] . Desp","cbCaiiT9kWVNkI6g","https://ap.wps.com/l/cbCaiiT9kWVNkI6g","pdf",2931604,4,1,"English","en",105,"# Introduction\n## Challenges in Dynamic Tactile Modeling\n## Hallucination and Limited Generalization in Tactile Reasoning","[{\"question\":\"What are the two key challenges TacReasoner targets?\",\"answer\":\"TacReasoner targets (i) inadequate modeling of dynamic tactile signals for temporally evolving reasoning, and (ii) hallucinations in tactile foundation models caused by the lack of explicit reasoning mechanisms.\"},{\"question\":\"What does TacReasoner contribute to tactile representation learning?\",\"answer\":\"TacReasoner proposes a Dynamic-aware Tactile Encoder to strengthen representation learning of dynamic tactile signals, enabling better understanding of evolving contact processes.\"},{\"question\":\"How does TacReasoner support interactive tactile reasoning in real-world scenarios?\",\"answer\":\"It introduces TouchCoT-10K for structured tactile chain-of-thought supervision and DynTac-Bench to systematically evaluate dynamic tactile perception and real-world commonsense reasoning 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are the two key challenges TacReasoner targets?","Question",{"text":74,"@type":75},"TacReasoner targets (i) inadequate modeling of dynamic tactile signals for temporally evolving reasoning, and (ii) hallucinations in tactile foundation models caused by the lack of explicit reasoning mechanisms.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What does TacReasoner contribute to tactile representation learning?",{"text":79,"@type":75},"TacReasoner proposes a Dynamic-aware Tactile Encoder to strengthen representation learning of dynamic tactile signals, enabling better understanding of evolving contact processes.",{"name":81,"@type":72,"acceptedAnswer":82},"How does TacReasoner support interactive tactile reasoning in real-world scenarios?",{"text":83,"@type":75},"It introduces TouchCoT-10K for structured tactile chain-of-thought supervision and DynTac-Bench to systematically evaluate dynamic tactile perception and real-world commonsense reasoning 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