TacReasoner: A Dynamic Tactile-Language Framework for Interactive Reasoning in Real-World Scenarios

2026-07-06

AI Summary

This work addresses the limitations of existing tactile foundation models, which suffer from hallucinations due to the absence of explicit reasoning mechanisms and struggle to model dynamic tactile signals. To overcome these challenges, the authors propose a dynamic tactile-language reasoning framework that integrates a dynamics-aware tactile encoder with a structured chain-of-thought reasoning mechanism. They further introduce TouchCoT-10k, the first tactile chain-of-thought dataset, and DynTac-Bench, a benchmark for evaluating dynamic tactile reasoning. Leveraging a 7B-parameter large language model, the proposed approach significantly outperforms current methods—including the 14B-parameter VTV-LLM—across multiple tactile commonsense reasoning tasks, achieving more accurate and efficient multimodal interactive reasoning.

Abstract

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 systematically evaluate dynamic tactile perception and real-world commonsense reasoning. Experimental results demonstrate that TacReasoner achieves competitive performance against state-of-the-art models across multiple datasets. Notably, despite using only 7B parameters, TacReasoner outperforms the 14B VTV-LLM model on most subtasks, highlighting its effectiveness and efficiency in tactile commonsense reasoning.

Problem

• tactile sensing
• dynamic tactile signals
• multimodal reasoning
• hallucination
• real-world inference

Innovation

• Dynamic Tactile Encoding
• Tactile Chain-of-Thought
• Multimodal Reasoning
• TouchCoT-10k
• TacReasoner