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

Source

Source name: arXiv
Source kind: paper
Authors / organization: Kailin Lyu, Di Wu, Long Xiao, Jianning Zeng, Jianwei He, Chang Lin, Lianyu Hu, Lin Shu, Jie Hao, Ce Hao
Venue: arXiv: cs.AI
Published: 2026-07-06
Collected: 2026-07-07
URL: http://arxiv.org/abs/2607.05131v1
PDF: https://arxiv.org/pdf/2607.05131v1
DOI: UNKNOWN

Plain-Language Brief

The paper proposes TacReasoner, a new framework that enhances the perception and representation of dynamic tactile signals for intelligent systems. It addresses two challenges: insufficient modeling of such signals and hallucination in foundation models. The framework uses a Dynamic-aware Tactile Encoder and introduces TouchCoT-10k, a tactile chain-of-thought dataset for structured reasoning over tactile inputs. Experiments show that TacReasoner performs competitively against state-of-the-art models and outperforms a larger model on most subtasks.

What the Document Says

The paper presents TacReasoner, a framework designed to enhance the perception and representation of dynamic tactile signals in intelligent systems.
Two challenges are addressed: insufficient modeling of dynamic tactile signals and hallucination in foundation models.
A Dynamic-aware Tactile Encoder is used to address the first challenge.
TouchCoT-10k, a new tactile chain-of-thought dataset, is introduced for structured reasoning over tactile inputs.
DynTac-Bench is established to evaluate dynamic tactile perception and real-world commonsense reasoning.
Experimental results show that TacReasoner performs competitively against state-of-the-art models across multiple datasets.

Key Concepts Mentioned

tactile sensing
intelligent systems
multimodal reasoning
dynamic tactile signals
temporally evolving properties
tactile foundation models
hallucination
chain-of-thought dataset
DynTac-Bench
competitive performance
state-of-the-art models

Claims Made by the Document

TacReasoner addresses challenges in integrating tactile sensing into intelligent systems for multimodal reasoning.
TacReasoner outperforms a larger model on most subtasks.

Evidence Used

The paper describes the proposed TacReasoner framework and its components.
Experimental results are presented showing TacReasoner's performance compared to other models.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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.