MISID: A Multimodal Multi-turn Dataset for Complex Intent Recognition in Strategic Deception Games

TL;DR

MISID is a new multimodal, multi-turn dataset from strategic games designed to challenge and improve intent recognition models in complex, deceptive, multi-participant interactions.

Contribution

The paper introduces MISID, a comprehensive dataset with detailed annotations for long-context intent recognition, and proposes FRACTAM, a novel framework that enhances model performance in complex scenarios.

Findings

State-of-the-art models show deficiencies in complex intent recognition tasks.

FRACTAM improves intent detection accuracy in multi-turn, multimodal scenarios.

The dataset reveals critical challenges like visual hallucination and limited causal chaining in current models.

Abstract

Understanding human intent in complex multi-turn interactions remains a fundamental challenge in human-computer interaction and behavioral analysis. While existing intent recognition datasets focus mainly on single utterances or simple dialogues, real-world scenarios often involve sophisticated strategic interactions where participants must maintain complex deceptive narratives over extended periods. To address this gap, we introduce MISID, a comprehensive multimodal, multi-turn, and multi-participant benchmark for intent recognition. Sourced from high-stakes social strategy games, MISID features a fine-grained, two-tier multi-dimensional annotation scheme tailored for long-context discourse analysis and evidence-based causal tracking. Our systematic evaluation of state-of-the-art Multimodal Large Language Models (MLLMs) on MISID reveals critical deficiencies in complex scenarios, including text-prior visual hallucination, impaired cross-modal synergy, and limited capacity in chaining causal cues. Consequently, we propose FRACTAM as a baseline framework. Using a ``Decouple-Anchor-Reason'' paradigm, FRACTAM reduces text bias by extracting pure unimodal factual representations, employs two-stage retrieval for long-range factual anchoring, and constructs explicit cross-modal evidence chains. Extensive experiments demonstrate that FRACTAM enhances mainstream models' performance in complex strategic tasks, improving hidden intent detection and inference while maintaining robust perceptual accuracy. Our dataset is available at https://naislab.cn/datasets/MISID.