Restore README section order

README.md

@@ -11,6 +11,35 @@ size_categories:
 
 # EgoTL-DATA
 
+## EgoTL Overview
+
+EgoTL is an egocentric benchmark for long-horizon household tasks introduced in **"EgoTL: Egocentric Think-Aloud Chains for Long-Horizon Tasks."** The project studies how well current vision-language models and world models handle step-by-step reasoning, spatial grounding, navigation, and manipulation in real home environments.
+
+Unlike many egocentric datasets that rely on post-hoc labels or noisy automatic annotations, EgoTL is built around a real-time **say-before-act** collection protocol. Before taking an action, the participant verbalizes the next goal or intention, producing think-aloud supervision aligned with the visual stream. The project also emphasizes spatial grounding and long-horizon task structure, aiming to support research on embodied reasoning rather than only short-horizon recognition.
+
+According to the official project website, EgoTL is designed to expose failure modes of modern foundation models on long-horizon egocentric reasoning and to support improvement through human-aligned supervision. The benchmark highlights errors such as hallucinated objects, skipped steps, weak spatial grounding, and inconsistent long-horizon planning.
+
+Official project website: https://ego-tl.github.io/
+
+## What EgoTL Provides
+
+EgoTL centers on egocentric household tasks with think-aloud chains, task structure, and clip-level grounding. The project website describes the dataset and benchmark as supporting:
+
+- long-horizon household task reasoning
+- egocentric navigation and manipulation understanding
+- think-aloud chain-of-thought style supervision
+- action and scene reasoning under cluttered real environments
+- spatially grounded evaluation and long-horizon generation analysis
+
+The official EgoTL-Bench presentation highlights six task dimensions across three layers, including:
+
+- memory-conditioned planning
+- scene-aware action reasoning
+- next action prediction
+- action recognition
+- direction recognition
+- distance estimation
+
 ## This Repository
 
 This Hugging Face dataset repository hosts video clips and JSON annotations associated with EgoTL-format data. The uploaded files are organized as:
@@ -51,35 +80,6 @@ This repository is suitable for work on:
 - alignment between video and think-aloud text
 - spatially grounded embodied AI evaluation
 
-## EgoTL Overview
-
-EgoTL is an egocentric benchmark for long-horizon household tasks introduced in **"EgoTL: Egocentric Think-Aloud Chains for Long-Horizon Tasks."** The project studies how well current vision-language models and world models handle step-by-step reasoning, spatial grounding, navigation, and manipulation in real home environments.
-
-Unlike many egocentric datasets that rely on post-hoc labels or noisy automatic annotations, EgoTL is built around a real-time **say-before-act** collection protocol. Before taking an action, the participant verbalizes the next goal or intention, producing think-aloud supervision aligned with the visual stream. The project also emphasizes spatial grounding and long-horizon task structure, aiming to support research on embodied reasoning rather than only short-horizon recognition.
-
-According to the official project website, EgoTL is designed to expose failure modes of modern foundation models on long-horizon egocentric reasoning and to support improvement through human-aligned supervision. The benchmark highlights errors such as hallucinated objects, skipped steps, weak spatial grounding, and inconsistent long-horizon planning.
-
-Official project website: https://ego-tl.github.io/
-
-## What EgoTL Provides
-
-EgoTL centers on egocentric household tasks with think-aloud chains, task structure, and clip-level grounding. The project website describes the dataset and benchmark as supporting:
-
-- long-horizon household task reasoning
-- egocentric navigation and manipulation understanding
-- think-aloud chain-of-thought style supervision
-- action and scene reasoning under cluttered real environments
-- spatially grounded evaluation and long-horizon generation analysis
-
-The official EgoTL-Bench presentation highlights six task dimensions across three layers, including:
-
-- memory-conditioned planning
-- scene-aware action reasoning
-- next action prediction
-- action recognition
-- direction recognition
-- distance estimation
-
 ## Citation
 
 If you use EgoTL or files from this repository, please cite the EgoTL project and paper from the official website: