Institution profile

Mohamed bin Zayed University of Artificial Intelligence

Academic institutionasia · ae
Official website
Research library1,236linked papers
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Selected work

Representative Papers

Retrieval-Augmented Generation for Natural Language Processing: A Survey

Jul 18, 2024arXiv.org

To address hallucination, knowledge staleness, and poor domain adaptability in large language models (LLMs), this paper conducts a systematic study of retrieval-augmented generation (RAG). We propose a full-stack RAG framework encompassing retriever design (dense, sparse, and hybrid), query rewriting, context fusion, LLM fine-tuning, and prompt engineering. We introduce the first taxonomy for dynamic knowledge updating and establish a multidimensional evaluation benchmark that balances academic rigor with industrial practicality. Additionally, we release a structured RAG knowledge graph and fully reproducible open-source code. Our contributions significantly enhance RAG’s robustness and maintainability in real-world deployments, providing both theoretical foundations and engineering best practices for knowledge-enhanced generative systems.

15 citationsRead paper

On the Identification of Temporally Causal Representation with Instantaneous Dependence

May 24, 2024arXiv.org

Existing time-series causal representation learning methods typically neglect instantaneous causal relationships, while emerging approaches accommodating such dependencies rely on latent-variable interventions or grouped observational data—conditions rarely satisfied in practice. To address this, we propose IDOL, the first framework enabling unique identification of latent causal processes with instantaneous dependencies without requiring interventions or data grouping. Theoretically, IDOL introduces sparse influence constraints—unifying delayed and instantaneous causal modeling—and temporal context variability, establishing strong identifiability guarantees. Methodologically, it integrates temporal variational inference with gradient-driven sparse regularization to jointly estimate latent variables and the causal graph. Experiments demonstrate that IDOL achieves exact structural recovery on synthetic benchmarks and significantly improves long-horizon prediction accuracy and causal interpretability across multiple human motion forecasting datasets.

11 citationsRead paper

Never-Ending Behavior-Cloning Agent for Robotic Manipulation

Mar 01, 2024

Embodied robots struggle with 3D scene understanding and human-level task generalization in unstructured environments due to reliance on multimodal observations. Method: This paper proposes a lifelong language-conditioned behavioral cloning framework tailored for real-world scenarios. It introduces the first lifelong behavioral cloning paradigm; designs a skill-sharing semantic rendering and representation distillation module to mitigate 3D representation blind spots; and develops a skill-specific evolutionary planner enabling human-like incremental knowledge embedding in a low-rank latent space. Contribution/Results: Evaluated on a newly established lifelong manipulation benchmark, the method significantly outperforms state-of-the-art approaches. The code, dataset, and visualization results are publicly released, demonstrating strong cross-task sequential adaptability and robustness to continual learning.

5 citationsRead paper

Causal Explanations for Image Classifiers

Nov 13, 2024arXiv.org

Existing explanation methods for image classifiers lack rigorous formal definitions of causality and explanation, relying predominantly on heuristic strategies. Method: This paper introduces the Halpern–Pearl theory of actual causality to black-box image classification interpretability—the first systematic application of this causal framework to the domain. We propose REX, a causally grounded explanation generation framework that formally defines “cause” and “explanation,” designs a provably terminating algorithm for approximating minimal explanations, and implements an iterative solving mechanism with controllable computational complexity. Contribution/Results: The implemented tool REX outperforms state-of-the-art black-box explanation methods across explanation compactness, computational efficiency, and standard quality metrics (e.g., fidelity, stability, and comprehensibility). Experiments demonstrate that REX produces the most concise explanations and achieves the fastest convergence. This work establishes a rigorous causal foundation for explainable AI while delivering a practical, scalable technical solution.

4 citationsRead paper

Vision-Language Models Meet Meteorology: Developing Models for Extreme Weather Events Detection with Heatmaps

Jun 14, 2024arXiv.org

Existing vision-language models (VLMs) exhibit color perception bias and imprecise spatial localization when interpreting meteorological heatmaps, leading to unreliable explanations for extreme weather event detection (EWED). To address this, we formulate EWED as a vision-language question answering (VQA) task and introduce three key contributions: (1) ClimateIQA—the first domain-specific VQA dataset for meteorology; (2) SPOT, a novel algorithm that enhances precise localization of heatmap color boundaries and critical regions; and (3) Climate-Zoo, a family of meteorology-specialized VLMs. Experiments demonstrate that our approach elevates EWED accuracy from 0% to over 90%, substantially outperforming general-purpose VLMs. All datasets, source code, and pretrained models are publicly released, establishing a reproducible benchmark and foundational infrastructure for AI-driven meteorology.

3 citationsRead paper
Recent publications

Latest Papers

The Robot Data Factory

Sep 15, 2026

本文提出Robot Data Factory，通过持续生成、验证和重用机器人经验来解决物理AI中的知识获取问题，采用基础设施和方法论实现部署-测量-学习-重复的闭环。

0 citationsRead paper