README: restore original voice, tasks overview covers both tasks, clarify countries and citations

README.md

@@ -66,12 +66,10 @@ configs:
 
 # ImageEval-ArabicNLP26 👁️
 
-The complete data for the [ImageEval 2026 Shared Task](https://imageeval2026.github.io/) at ArabicNLP 2026, in one place:
+**ImageEval-ArabicNLP26** is the dataset of the [ImageEval 2026 Shared Task](https://imageeval2026.github.io/) at ArabicNLP 2026.
+It covers both of the shared task's tasks: **AynVQA** (Task 1), a culturally grounded Arabic multimodal benchmark for spoken visual question answering and hallucination detection, and **CRAI-Bench** (Task 2), which evaluates the cultural accuracy of Arabic text-to-image generation.
 
-- **Task 1: AynVQA** — culturally grounded spoken visual question answering and image-grounded hallucination detection. Ayn (عين, "eye") tests whether a model can read a culturally specific image, both from a spoken Arabic question and by telling grounded descriptions apart from plausible but hallucinated ones. Offered in two language tracks, **English** and **Modern Standard Arabic (MSA)**, scored separately.
-- **Task 2: CRAI-Bench** — cultural accuracy evaluation for Arabic text-to-image generation, scored with the Cultural Representation Accuracy Index (CRAI). Lives under [`task2/`](./tree/main/task2).
-
-The competition has concluded and **all gold labels are released**, including the blind test splits. Official results are on the [leaderboard](https://imageeval2026.github.io/leaderboard/).
+The shared task has concluded. All gold labels are released, including the blind test splits, and the official results are on the [leaderboard](https://imageeval2026.github.io/leaderboard/).
 
 ## 💬 Join our Slack
 
@@ -79,23 +77,27 @@ Join the **[ImageEval Slack channel](https://join.slack.com/t/mm-eval/shared_inv
 releases, deadlines, and updates, and to connect with the organisers and other
 participants.
 
-# 👁️ Task 1: AynVQA
-
-Culturally grounded spoken visual question answering (1a) and image-grounded
-hallucination detection (1b), each in English and MSA tracks.
-Starter kit, format checker and official scorer: [ImageEval2026-tasks/task1](https://github.com/ImageEval2026/ImageEval2026-tasks/tree/main/task1).
+## 🎯 Tasks
 
-## 🎯 Subtasks
-
-**Spoken VQA.** Given an image and the spoken question and options
-(audio), choose the correct option. 
+**Spoken VQA (Task 1a).** Given an image and the spoken question and options
+(audio), choose the correct option.
 
 Prediction: the option index 0, 1 or 2.
 
-**Hallucination detection.** Given an image and three statements, decide for **each** statement whether it is **True** (grounded in the image) or **False** (a hallucination). Exactly one statement is grounded. 
+**Hallucination detection (Task 1b).** Given an image and three statements, decide for **each** statement whether it is **True** (grounded in the image) or **False** (a hallucination). Exactly one statement is grounded.
 
 Prediction: a True/False label per statement.
 
+**Cultural accuracy evaluation (Task 2).** Given a reference image of a Qatari cultural scene, a caption, and an AI-generated image produced from that caption, judge how faithfully the generated image represents the culture.
+
+Prediction: CRAI scores across five dimensions plus a composite.
+
+# 👁️ Task 1: AynVQA
+
+Ayn (عين, "eye") tests whether a model can read a culturally specific image, both from a spoken Arabic question and by telling grounded descriptions apart from plausible but hallucinated ones. Each subtask is offered as two language tracks, **English** and **Modern Standard Arabic (MSA)**, scored separately.
+
+Starter kit, format checker and official scorer: [ImageEval2026-tasks/task1](https://github.com/ImageEval2026/ImageEval2026-tasks/tree/main/task1).
+
 ## 🗂️ Subsets
 
 | config | task | language | Codabench |
@@ -110,7 +112,7 @@ the questions are translations of each other.
 
 ## 🌍 Countries
 
-The dataset spans **18 Arab countries**:
+Task 1 spans **18 Arab countries** (Task 2 is grounded specifically in Qatari culture):
 
 Algeria, Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Palestine, Qatar, Saudi Arabia, Sudan, Syria, Tunisia, UAE, Yemen.
 
@@ -229,14 +231,9 @@ For 1b, combined accuracy was 0.6840 (English) and 0.5080 (MSA).
 
 # 🎨 Task 2: CRAI-Bench
 
-Cultural accuracy evaluation for Arabic text-to-image generation. Given a
-reference image of an authentic Qatari cultural scene, a caption, and an
-AI-generated image produced from that caption, systems predict scores on the
-five-dimensional **Cultural Representation Accuracy Index (CRAI)** plus its
-composite. See the [Task 2 page](https://imageeval2026.github.io/task2/) for
-the full framework.
-Starter kit, format checker, baselines and the official scorer:
-[ImageEval2026-tasks/task2](https://github.com/ImageEval2026/ImageEval2026-tasks/tree/main/task2).
+**CRAI-Bench** evaluates whether AI-generated images faithfully represent Qatari and Arab cultural scenes. Given a reference image of an authentic cultural scene, a caption, and an AI-generated image produced from that caption, systems predict scores on the **Cultural Representation Accuracy Index (CRAI)**, a five-dimensional framework validated against human annotation. Each of the reference images has five caption versions, ranging from fully Qatari-specific (v1) to entirely generic (v5). See the [Task 2 page](https://imageeval2026.github.io/task2/) for the full framework and scoring rubric.
+
+Starter kit, format checker, baselines and the official scorer: [ImageEval2026-tasks/task2](https://github.com/ImageEval2026/ImageEval2026-tasks/tree/main/task2).
 
 ## 📂 Files
 
@@ -283,8 +280,8 @@ The dataset is distributed under the Creative Commons Attribution-NonCommercial-
 
 ## Citation
 
-If you use this dataset, please cite the shared task overview paper and the
-relevant dataset papers:
+The shared task overview paper covers both tasks. If you use the Task 1 data,
+please also cite the dataset papers below.
 
 ```
 @inproceedings{imageeval-2026,