Touch is a key modality for embodied agents to understand the physical world. Although recent work has incorporated tactile signals into lan- guage systems for tactile Commonsense Rea- soning, scaling such systems to realistic open- world settings remains challenging due to two key bottlenecks: (1) current tactile reasoning datasets remain limited in format and scale, pro- viding insufficient supervision for reasoning from tactile observations to physical common- sense and hindering the learning of transferable tactile commonsense; (2) Tactile signals are inherently redundant and action-specific, yet existing methods often overlook these proper- ties, resulting in inefficient representations with limited semantic expressiveness. To address these limitations, we propose TouchThinker, a tactile-language framework that scales tac- tile commonsense reasoning to the open world from both data and representation perspec- tives. First, we construct TouchThinker-1M, a million-scale, multi-source tactile reasoning dataset covering 410 objects, 8 scenarios, and 7 sensor types, providing a solid data founda- tion for open-world generalization. We fur- ther introduce TouchThinker-Bench, an open- world benchmark with more realistic and di- verse tasks. Then, we further introduce action- aware tactile modeling and a two-stage training paradigm to improve tactile representation effi- ciency and enable efficient reasoning. Experi- mental results demonstrate that TouchThinker achieves competitive performance against state- of-the-art models across multiple datasets. Our code and dataset will be released.

The advent of artificial intelligence has led to a growing emphasis on data-driven modeling in macroeconomics, with agent-based modeling (ABM) emerging as a prominent bottom-up simulation paradigm. In ABM, agents (e.g., households, firms) interact within a macroeconomic environment, collectively generating market dynamics. Existing agent modeling typically employs predetermined rules or learning-based neural networks for decision-making. However, customizing each agent presents significant challenges, complicating the modeling of agent heterogeneity. Additionally, the influence of multi-period market dynamics and multifaceted macroeconomic factors are often overlooked in decision-making processes.In this work, we introduce EconAgent, a large language model-empowered agent with human-like characteristics for macroeconomic simulation. We first construct a simulation environment that incorporates various market dynamics driven by agents’ decisions regarding work and consumption. Through the perception module, we create heterogeneous agents with distinct decision-making mechanisms. Furthermore, we model the impact of macroeconomic trends using a memory module, which allows agents to reflect on past individual experiences and market dynamics.Simulation experiments show that EconAgent can make realistic decisions, leading to more reasonable macroeconomic phenomena compared to existing rule-based or learning-based agents. Our codes are released at https://github.com/tsinghua-fib-lab/ACL24-EconAgent.