@Article{cmc.2026.083087,
AUTHOR = {Pan Li, Zhi Li, Yingyou Wen},
TITLE = {TATA: A Trust-Aware Task-Oriented Agent Framework for Industrial Intelligence Scenarios},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/cmc/online/detail/27025},
ISSN = {1546-2226},
ABSTRACT = {The rapid advancement of edge intelligence in Industrial Internet of Things (IIoT) is transforming human–computer interaction from conventional “command execution” to complex “human–AI deep collaboration”. Within such safety-critical industrial environments, establishing robust mutual understanding and trust mechanisms
becomes a significant prerequisite for decision reliability and efficiency. However, existing industrial interaction systems predominantly focus on task progression and explicit command responses, lacking fine-grained, dynamic tracking of operators’ trust states, cognitive evolution, and behavioral dynamics. Moreover, current LLM-based user simulation in evaluation often exhibit an “over-cooperation” bias, failing to capture the cognitive conflicts and trust crises characteristic of high-pressure, high-risk industrial conditions. To address these challenges, we first propose a trust-aware user behavior model, which utilizes an LLM-parameterized Hidden Markov Model (HMM) to formalize collaborative trust as a dynamic latent variable, thereby structurally characterizing the psychological and behavioral dynamics of operators across multi-turn interactions. Building on this, we introduce TATA, a task-oriented agent framework integrating trust-awareness and cognitive alignment. Through a dual-track state monitoring mechanism and adaptive interaction policy coordination, TATA effectively advances collaborative tasks and fosters relationship maintenance in realistic collaborative environments. Comprehensive evaluations on six industrial task scenarios demonstrates that TATA achieves an optimal balance between collaboration depth and task efficiency, outperforming the strongest baseline by achieving 1.6 to 2.6 times higher collaboration efficiency and an absolute increase of over 15 percentage points in task completion rate. These findings provide valuable insights for developing resilient and adaptive deep human-AI collaboration tailored to IIoT scenarios.},
DOI = {10.32604/cmc.2026.083087}
}