@inproceedings{wang-etal-2026-deft,
    title = "{DEFT}: Demystifying {VLN} Failures via a Unified Dual-View Explainability Framework for {LLM}-based Agents",
    author = "Wang, Yawen  and
      Dai, Yihan  and
      Chen, Jianming  and
      Wang, Junjie  and
      Wang, Qing",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1363/",
    pages = "29541--29560",
    ISBN = "979-8-89176-390-6",
    abstract = "Large Language Models (LLMs) have emerged as central planners in Vision-and-Language Navigation (VLN), yet their complexity increasingly obscures their internal decision-making. Existing interpretability methods typically isolate temporal criticality from feature salience, creating an alignment gap and failing to account for the behavioral instability of black-box agents. To address this, we propose DEFT, a unified dual-view framework that demystifies agent behavior by jointly analyzing $\textit{when}$ a decision is pivotal and $\textit{what}$ visual evidence grounds it. Featuring a dual-head architecture with a shared latent representation, DEFT employs a $\textit{Mask Head}$ for counterfactual-based criticality detection and an $\textit{Action Head}$ that leverages an ensemble of surrogates to recover robust visual cues. Extensive experiments on MatterPort3D across three LLM-based agents demonstrate that DEFT outperforms baselines in both temporal and feature fidelity. User studies further validate its utility, showing 78{\%} alignment with human intuition."
}