Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-05T11:23:44.920457Z
Paper Citation Record · LEDGER
A-SEA3L-QA: A Fully Automated Self-Evolving, Adversarial Workflow for Arabic Long-Context Question-Answer Generation
As of 10 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 0 inbound Pith citation observations for arXiv:2509.02864.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
16 of 16 outbound references displayed
- verified exact0
- verified fuzzy2
- unresolved11
- parse uncertain0
- malformed identifier0
- metadata mismatch3
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation dc4977e0-00ad-44fb-9ee1-b9910cf09e4b · outbound
This paper cites Qalam : A Multimodal LLM for Arabic Optical Character and Handwriting Recognition.
A-SEA3L-QA: A Fully Automated Self-Evolving, Adversarial Workflow for Arabic Long-Context Question-Answer Generation Qalam : A Multimodal LLM for Arabic Optical Character and Handwriting Recognition
Reference 3
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-05T11:23:46.116707Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation f1f32459-e2fb-423d-933c-cc8b11cd97c9 · outbound
This paper cites M-Longdoc: A Benchmark For Multimodal Super-Long Document Understanding And A Retrieval-Aware Tuning Framework.
A-SEA3L-QA: A Fully Automated Self-Evolving, Adversarial Workflow for Arabic Long-Context Question-Answer Generation M-Longdoc: A Benchmark For Multimodal Super-Long Document Understanding And A Retrieval-Aware Tuning Framework
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T11:23:42.739073Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation db14d8fb-aa78-4730-a34c-681ddafcff13 · outbound
This paper cites LongDocURL: a Comprehensive Multimodal Long Document Benchmark Integrating Understanding, Reasoning, and Locating.
A-SEA3L-QA: A Fully Automated Self-Evolving, Adversarial Workflow for Arabic Long-Context Question-Answer Generation LongDocURL: a Comprehensive Multimodal Long Document Benchmark Integrating Understanding, Reasoning, and Locating
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T11:23:42.973037Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5fef2b21-9dd3-4327-8a8c-3837ad43861a · outbound
This paper cites InternLM-XComposer2-4KHD: A Pioneering Large Vision-Language Model Handling Resolutions from 336 Pixels to 4K HD.
A-SEA3L-QA: A Fully Automated Self-Evolving, Adversarial Workflow for Arabic Long-Context Question-Answer Generation InternLM-XComposer2-4KHD: A Pioneering Large Vision-Language Model Handling Resolutions from 336 Pixels to 4K HD
Reference 6
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T11:23:43.135156Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0a48fad0-50f4-45ab-acbc-9479ce3902ec · outbound
This paper cites Accessed: 2025-02-11.
A-SEA3L-QA: A Fully Automated Self-Evolving, Adversarial Workflow for Arabic Long-Context Question-Answer Generation Accessed: 2025-02-11
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T11:23:46.422218Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 9d8713de-f410-463f-89cc-fc4a9316a4c2 · outbound
This paper cites CAMEL-Bench: A Comprehensive Arabic LMM Benchmark.
A-SEA3L-QA: A Fully Automated Self-Evolving, Adversarial Workflow for Arabic Long-Context Question-Answer Generation CAMEL-Bench: A Comprehensive Arabic LMM Benchmark
Reference 8
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T11:23:43.534326Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c97f5a42-b1d1-455e-a20e-a55081a197d1 · outbound
This paper cites MMLongBench-Doc: Benchmarking Long-context Document Understanding with Visualizations.
A-SEA3L-QA: A Fully Automated Self-Evolving, Adversarial Workflow for Arabic Long-Context Question-Answer Generation MMLongBench-Doc: Benchmarking Long-context Document Understanding with Visualizations
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T11:23:43.927169Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2957c55a-fa71-4fe8-aac7-ead9678bef28 · outbound
This paper cites SARD: A Large-Scale Synthetic Arabic OCR Dataset for Book-Style Text Recognition.
A-SEA3L-QA: A Fully Automated Self-Evolving, Adversarial Workflow for Arabic Long-Context Question-Answer Generation SARD: A Large-Scale Synthetic Arabic OCR Dataset for Book-Style Text Recognition
Reference 12
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-05T11:23:45.680450Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation ae8a7989-cf4c-4d87-a82f-68e64818835e · outbound
This paper cites Arabic-Nougat: Fine-Tuning Vision Transformers for Arabic OCR and Markdown Extraction.
A-SEA3L-QA: A Fully Automated Self-Evolving, Adversarial Workflow for Arabic Long-Context Question-Answer Generation Arabic-Nougat: Fine-Tuning Vision Transformers for Arabic OCR and Markdown Extraction
Reference 13
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-05T11:23:45.317541Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation f805a4a5-b2a4-47cd-842d-4e0d4795e7ac · outbound
This paper cites CogVLM: Visual Expert for Pretrained Language Models.
A-SEA3L-QA: A Fully Automated Self-Evolving, Adversarial Workflow for Arabic Long-Context Question-Answer Generation CogVLM: Visual Expert for Pretrained Language Models
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T11:23:44.632529Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 65cc583b-0952-4c72-ba8c-e838df959c5a · outbound
This paper cites LLMRefine: Pinpointing and Refining Large Language Models via Fine-Grained Actionable Feedback.
A-SEA3L-QA: A Fully Automated Self-Evolving, Adversarial Workflow for Arabic Long-Context Question-Answer Generation LLMRefine: Pinpointing and Refining Large Language Models via Fine-Grained Actionable Feedback
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T11:23:44.798688Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2303c084-9131-47f2-a313-d5735f4488aa · outbound
This paper cites DocLayout-YOLO: Enhancing Document Layout Analysis through Diverse Synthetic Data and Global-to-Local Adaptive Perception.
A-SEA3L-QA: A Fully Automated Self-Evolving, Adversarial Workflow for Arabic Long-Context Question-Answer Generation DocLayout-YOLO: Enhancing Document Layout Analysis through Diverse Synthetic Data and Global-to-Local Adaptive Perception
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T11:23:44.920457Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e35e19a4-6c26-4b93-ba06-48003982aab0 · outbound
This paper cites ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning.
A-SEA3L-QA: A Fully Automated Self-Evolving, Adversarial Workflow for Arabic Long-Context Question-Answer Generation ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning
Reference 2022
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T11:23:44.107199Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8a845640-0b10-4278-b0b6-2ef983ae27c9 · outbound
This paper cites GPT-4 Technical Report.
A-SEA3L-QA: A Fully Automated Self-Evolving, Adversarial Workflow for Arabic Long-Context Question-Answer Generation GPT-4 Technical Report
Reference 2023
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T11:23:42.160826Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5be62823-261a-42ee-8363-2a7f4af4bb01 · outbound
This paper cites Accessed: 2025-02-11.
A-SEA3L-QA: A Fully Automated Self-Evolving, Adversarial Workflow for Arabic Long-Context Question-Answer Generation Accessed: 2025-02-11
Reference 2024
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-05T11:23:46.874653Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation 950d333c-e850-4d78-b47d-d520b358db3d · outbound
This paper cites KITAB-Bench: A Comprehensive Multi-Domain Benchmark for Arabic OCR and Document Understanding.
A-SEA3L-QA: A Fully Automated Self-Evolving, Adversarial Workflow for Arabic Long-Context Question-Answer Generation KITAB-Bench: A Comprehensive Multi-Domain Benchmark for Arabic OCR and Document Understanding
Reference 2025
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T11:23:43.769465Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.