explainable-ai

The paper presents SimXAI, a lightweight LLM-centric conversational layer that replaces the multi-module GUS pipelines used by ConvXAI agents with a single tool-calling LLM, along with an evaluation suite; a user study swapping only the conversational layer of TalkToModel shows users complete tasks more accurately in fewer turns.

PhotoGuider 提出一种 measure-then-frame 方法，利用共享的构图感知 token 进行 360° 全景图的观看方向选择与取景，并通过微调 MLLM 生成以测量结果为条件的构图解释，在两项 2D 裁剪基准上取得最高 mIoU，并在 120 人盲测中以 53.2% 的偏好率排名第一。

DocXEval introduces the first comprehensive explainability framework for multimodal document understanding, evaluating 13 XAI methods across 3 tasks, 8 datasets, 4 models, and 17 metrics, finding Occlusion as the most faithful and interpretable method overall.

该论文提出一个基于本体引导的多智能体差分诊断框架，将诊断推理锚定在结构化的医学知识库上，通过显式、可审计的推理步骤减少幻觉并提升可解释性，同时自动生成追问以降低不确定性。

This paper presents an AI-augmented decision-support system for ambulance travel time estimation using historical Skåne region data, showing that travel times vary by hour, day, and trip priority while leveraging SHAP and permutation importance for interpretability.

A comprehensive review of the NDE 4.0 landscape, analyzing non-destructive inspection through computational algorithms, physical techniques, industrial applications, and research objectives, highlighting the shift toward deep learning and generative models for automated defect recognition and identifying key gaps like open datasets, explainable AI, and standardized digital twin protocols.

本文提出一种融合 Temporal Fusion Transformer 与 N-BEATS 的注意力混合框架，用于统一多时间尺度的光伏功率预测，在 DKASC 真实数据集上相比最佳基线 TFT 实现 35–40% 的 RMSE 降低。

提出了Spectral-Spatial Interpretation (SSI)，一种在位置-通道玩家上进行全网格归因的方法，利用局部Shapley值生成光谱-空间归因矩阵，在HLS Burn Scar、CUB-200和EuroSAT数据集上优于现有基线方法。

这篇论文将音乐愉悦度预测构建为回归问题，利用MUSIN-G数据集的128通道EEG数据，比较了六种深度学习模型（DeepConvNet、CNN-LSTM、GNN-CNN、TCN等），并通过可解释性分析识别影响预测的关键时间片段。

该论文提出一个基于回答集编程(ASP)的混合声明式框架，用于对交互式运动轨迹进行可解释的前瞻性推理，并在Argoverse 2基准上进行评估，每个预测轨迹都附带可检查的事件级假设。

CRISP is a neurosymbolic framework that reconstructs the last-layer activation vectors of binary neural teachers as Tsetlin Machine clauses, enabling clause-level symbolic traces of hidden activations. Evaluated on MNIST, KMNIST, FashionMNIST, SVHN, and CIFAR10, it reports up to 87.52% reconstruction fidelity on SVHN.

A new paper proposes Dual-Stream OSSE-LSTM, an episodic metric-learning framework combining an Omni-Scale CNN with a Bidirectional LSTM for label-efficient time series classification, plus Counterfactual Integrated Gradients (C-IG) for explainable prototype-based decisions, achieving strong and stable accuracy on 19 UCR datasets with only 2-8 labeled examples per class.

论文提出 HXAI 框架，结合局部与分区两级模型，在分布式能源系统中实现隐私保护下的可解释 AI 分析，用于电网负荷管理，同时在差分隐私约束下保持解释的语义结构而非最小化数值误差。

The paper proposes PX-UAP, a method using probabilistic robustness and explainable AI to generate universal adversarial perturbations against deep reinforcement learning-based intrusion detection systems, demonstrating improved attack effectiveness in experiments.

This paper proposes a synthetic ground-truth framework for evaluating explainable AI methods, using controlled interventions to create reliable benchmarks across different data domains.

This paper introduces SciGen-Verifier, a multimodal reasoning model for explainable verification of scientific image generation, along with a new benchmark and a reinforcement learning training pipeline.

The paper proposes XACT, a framework for learning sparse attribution masks over time-frequency transforms to explain time-series classifiers, demonstrating improved precision and interpretability over baselines.

This paper presents a validation protocol for knowledge tracing models, evaluating predictive performance and the stability and faithfulness of explanations using XGBoost and deep learning baselines on ASSISTments datasets.

This paper proposes an explainable hate speech detection framework integrating DistilBERT embeddings, BiLSTM, and an attention mechanism, achieving high F1-scores on benchmark datasets for both binary and multi-class classification.

This paper introduces a dual-model masking metric to benchmark ten explainable methods for temporal attribution in sequential recommendation systems, finding that gradient-based methods like GradientSHAP and Integrated Gradients yield the most faithful and robust attributions.