Update README.md

Author: Luodian

README.md

@@ -20,21 +20,19 @@
 
 ## Annoucement
 
-- [2025-04] 🚀🚀 Introducing [Aero-1-Audio](https://www.lmms-lab.com/posts/aero_audio/) — a compact yet mighty audio model. We have officially supports evaluation for Aero-1-Audio and it supports batched evaluations! Feel free to try out.
+We warmly welcome contributions from the open-source community!
 
+- [2025-07] 🎉🎉 We welcome the new task [PhyX](https://phyx-bench.github.io/), the first large-scale benchmark designed to assess models capacity for physics-grounded reasoning in visual scenarios.
+- [2025-06] 🎉🎉 We welcome the new task [VideoMathQA](https://mbzuai-oryx.github.io/VideoMathQA), designed to evaluate mathematical reasoning in real-world educational videos.
+- [2025-04] 🚀🚀 Introducing [Aero-1-Audio](https://www.lmms-lab.com/posts/aero_audio/) — a compact yet mighty audio model. We have officially supports evaluation for Aero-1-Audio and it supports batched evaluations! Feel free to try out.
 - [2025-02] 🚀🚀 We have integrated [`vllm`](https://github.com/EvolvingLMMs-Lab/lmms-eval/pull/544) into our models, enabling accelerated evaluation for both multimodal and language models. Additionally, we have incorporated [`openai_compatible`](https://github.com/EvolvingLMMs-Lab/lmms-eval/pull/546) to support the evaluation of any API-based model that follows the OpenAI API format. Check the usages [here](https://github.com/EvolvingLMMs-Lab/lmms-eval/tree/main/miscs/model_dryruns).
 
-- [2025-01] 🎓🎓 We have released our new benchmark: [Video-MMMU: Evaluating Knowledge Acquisition from Multi-Discipline Professional Videos](https://arxiv.org/abs/2501.13826). Please refer to the [project page](https://videommmu.github.io/) for more details.
+<details>
+<summary>Below is a chronological list of recent tasks, models, and features added by our amazing contributors. </summary>
 
+- [2025-01] 🎓🎓 We have released our new benchmark: [Video-MMMU: Evaluating Knowledge Acquisition from Multi-Discipline Professional Videos](https://arxiv.org/abs/2501.13826). Please refer to the [project page](https://videommmu.github.io/) for more details.
 - [2024-12] 🎉🎉 We have presented [MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs](https://arxiv.org/pdf/2411.15296), jointly with [MME Team](https://github.com/BradyFU/Video-MME) and [OpenCompass Team](https://github.com/open-compass).
-
 - [2024-11] 🔈🔊 The `lmms-eval/v0.3.0` has been upgraded to support audio evaluations for audio models like Qwen2-Audio and Gemini-Audio across tasks such as AIR-Bench, Clotho-AQA, LibriSpeech, and more. Please refer to the [blog](https://github.com/EvolvingLMMs-Lab/lmms-eval/blob/main/docs/lmms-eval-0.3.md) for more details!
-
-<details>
-<summary>We warmly welcome contributions from the open-source community! Below is a chronological list of recent tasks, models, and features added by our amazing contributors. </summary>
-
-- [2025-07] 🎉🎉 We welcome the new task [PhyX](https://phyx-bench.github.io/), the first large-scale benchmark designed to assess models capacity for physics-grounded reasoning in visual scenarios.
-- [2025-06] 🎉🎉 We welcome the new task [VideoMathQA](https://mbzuai-oryx.github.io/VideoMathQA), designed to evaluate mathematical reasoning in real-world educational videos.
 - [2024-10] 🎉🎉 We welcome the new task [NaturalBench](https://huggingface.co/datasets/BaiqiL/NaturalBench), a vision-centric VQA benchmark (NeurIPS'24) that challenges vision-language models with simple questions about natural imagery.
 - [2024-10] 🎉🎉 We welcome the new task [TemporalBench](https://huggingface.co/datasets/microsoft/TemporalBench) for fine-grained temporal understanding and reasoning for videos, which reveals a huge (>30%) human-AI gap.
 - [2024-10] 🎉🎉 We welcome the new tasks [VDC](https://rese1f.github.io/aurora-web/) for video detailed captioning, [MovieChat-1K](https://rese1f.github.io/MovieChat/) for long-form video understanding, and [Vinoground](https://vinoground.github.io/), a temporal counterfactual LMM benchmark composed of 1000 short natural video-caption pairs. We also welcome the new models: [AuroraCap](https://github.com/rese1f/aurora) and [MovieChat](https://github.com/rese1f/MovieChat).