A-SEA3L-QA: A Fully Automated Self-Evolving, Adversarial Workflow for Arabic Long-Context Question-Answer Generation
On the map: Synthetic data and self-improvement for LLMs → Synthetic reasoning data and bootstrapping
Abstract (arXiv)
We present an end-to-end, self-evolving adversarial workflow for long-context Question-Answer (QA) Generation in Arabic. By orchestrating multiple specialized LVLMs: a question generator, an evaluator, and a swarm of answer generators, our system iteratively refines its own performance without any human intervention. Starting from raw, multi-page Arabic documents across diverse domains, the question generator produces fine-grained, context-aware queries to be tackled by the answer generator swarm, and the evaluator assesses and feeds back quality metrics. This closed-loop cycle enables continuous learning: low-confidence outputs trigger automated re-generation and model updates, progressively enhancing question difficulty and relevance. Moreover, we set the quality metrics as a tunable hyperparameter, enabling question generation at controllable and customizable difficulty levels. We release AraLongBench, a large-scale Arabic benchmark of single- and multi-page challenges spanning hundreds of pages, and demonstrate that our self-evolving workflow substantially outperform static pipelines, markedly boosting the long-context comprehension capabilities of leading Arabic Large Vision Language Models (LVLMs). Lastly, we also meticulously architect a fully automated agentic workflow for long-context Arabic document collection.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Synthetic reasoning data and bootstrapping, one of 13 directions in Synthetic data and self-improvement for LLMs. Builds on 13 of its 30 references that are in the map. First posted on arXiv 2 Sep 2025.
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References · 13
- 2022ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoningcited by 1,479
- 2020DocVQA: A Dataset for VQA on Document Imagescited by 1,317
- 2024General OCR Theory: Towards OCR-2.0 via a Unified End-to-end Modelcited by 180
- 2022Towards Complex Document Understanding By Discrete Reasoningcited by 82
- 2024DocLayout-YOLO: Enhancing Document Layout Analysis through Diverse Synthetic Data and Global-to-Local Adaptive Perceptioncited by 82
- 2024LongDocURL: a Comprehensive Multimodal Long Document Benchmark Integrating Understanding, Reasoning, and Locatingcited by 72
- 2024M-Longdoc: A Benchmark For Multimodal Super-Long Document Understanding And A Retrieval-Aware Tuning Frameworkcited by 36
- 2024CAMEL-Bench: A Comprehensive Arabic LMM Benchmarkcited by 17
- 2025KITAB-Bench: A Comprehensive Multi-Domain Benchmark for Arabic OCR and Document Understandingcited by 15
- 2019BADAM: A Public Dataset for Baseline Detection in Arabic-script Manuscriptscited by 12
- 2024Qalam : A Multimodal LLM for Arabic Optical Character and Handwriting Recognitioncited by 7
- 2025SARD: A Large-Scale Synthetic Arabic OCR Dataset for Book-Style Text Recognitioncited by 6
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- 2025Generate, Discriminate, Evolve: Enhancing Context Faithfulness via Fine-Grained Sentence-Level Self-Evolution
- 2025Multi-Agent Interactive Question Generation Framework for Long Document Understanding
- 2024RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answering
- 2024SimRAG: Self-Improving Retrieval-Augmented Generation for Adapting Large Language Models to Specialized Domains
- 2025The Self-Improvement Paradox: Can Language Models Bootstrap Reasoning Capabilities without External Scaffolding?
- 2022Leveraging QA Datasets to Improve Generative Data Augmentation
- 2024LongRAG: A Dual-Perspective Retrieval-Augmented Generation Paradigm for Long-Context Question Answering
- 2026Agentic Adversarial QA for Improving Domain-Specific LLMs