diff --git a/update_template_or_data/update_paper_list.md b/update_template_or_data/update_paper_list.md
index 75c2c43..2a43dfb 100644
--- a/update_template_or_data/update_paper_list.md
+++ b/update_template_or_data/update_paper_list.md
@@ -14,7 +14,7 @@
     - 📑 Publisher: arXiv
     - 💻 Env: [GUI]
     - 🔑 Key: [model], [dataset], [Falcon-UI], [GUI understanding]
-    - 📖 TLDR: This paper introduces *Falcon-UI*, a GUI agent model that emphasizes understanding GUI contexts before following user instructions. The authors present the *Insight-UI Dataset*, an instruction-free GUI navigation dataset generated from the Common Crawl corpus, simulating various platforms like iOS, Android, Windows, and Linux across multiple resolutions on 312K domains. Falcon-UI is pretrained on this dataset and fine-tuned on Android and Web GUI datasets, achieving performance comparable to larger models, highlighting the importance of decoupling GUI understanding from instruction following in agent performance. :contentReference[oaicite:0]{index=0}
+    - 📖 TLDR: This paper introduces *Falcon-UI*, a GUI agent model that emphasizes understanding GUI contexts before following user instructions. The authors present the *Insight-UI Dataset*, an instruction-free GUI navigation dataset generated from the Common Crawl corpus, simulating various platforms like iOS, Android, Windows, and Linux across multiple resolutions on 312K domains. Falcon-UI is pretrained on this dataset and fine-tuned on Android and Web GUI datasets, achieving performance comparable to larger models, highlighting the importance of decoupling GUI understanding from instruction following in agent performance. 
 
 
 
@@ -547,7 +547,7 @@
     - 📑 Publisher: ACL 2024
     - 💻 Env: [Mobile]
     - 🔑 Key: [model], [framework], [benchmark]
-    - 📖 TLDR: This paper presents CoCo-Agent, a multimodal large language model (MLLM) designed for smartphone GUI automation. It introduces two novel approaches: Comprehensive Environment Perception (CEP) for enhanced GUI understanding, and Conditional Action Prediction (CAP) to improve action response accuracy. The proposed agent achieves state-of-the-art performance on GUI automation benchmarks such as AITW and META-GUI, showcasing its capabilities in realistic scenarios​:contentReference[oaicite:0]{index=0}​:contentReference[oaicite:1]{index=1}​:contentReference[oaicite:2]{index=2}​:contentReference[oaicite:3]{index=3}.
+    - 📖 TLDR: This paper presents CoCo-Agent, a multimodal large language model (MLLM) designed for smartphone GUI automation. It introduces two novel approaches: Comprehensive Environment Perception (CEP) for enhanced GUI understanding, and Conditional Action Prediction (CAP) to improve action response accuracy. The proposed agent achieves state-of-the-art performance on GUI automation benchmarks such as AITW and META-GUI, showcasing its capabilities in realistic scenarios.
 
 - [OfficeBench: Benchmarking Language Agents across Multiple Applications for Office Automation](https://arxiv.org/abs/2407.19056)
     - Zilong Wang, Yuedong Cui, Li Zhong, Zimin Zhang, Da Yin, Bill Yuchen Lin, Jingbo Shang
@@ -988,7 +988,7 @@
     - 📑 Publisher: arXiv
     - 💻 Env: [Web]
     - 🔑 Key: [benchmark], [dataset], [multi-modal reasoning], [TurkingBench], [Turking]
-    - 📖 TLDR: This paper introduces **Tur[k]ingBench**, a benchmark comprising 158 web-grounded tasks designed to evaluate AI agents' capabilities in complex web-based environments. Unlike prior benchmarks that utilize synthetic web pages, Tur[k]ingBench leverages natural HTML pages from crowdsourcing platforms, presenting tasks with rich multi-modal contexts. The benchmark includes 32.2K instances, each with diverse inputs, challenging models to interpret and interact with web pages effectively. Evaluations of state-of-the-art models reveal significant room for improvement, highlighting the need for advanced web-based agents capable of handling real-world web interactions. :contentReference[oaicite:0]{index=0}
+    - 📖 TLDR: This paper introduces **Tur[k]ingBench**, a benchmark comprising 158 web-grounded tasks designed to evaluate AI agents' capabilities in complex web-based environments. Unlike prior benchmarks that utilize synthetic web pages, Tur[k]ingBench leverages natural HTML pages from crowdsourcing platforms, presenting tasks with rich multi-modal contexts. The benchmark includes 32.2K instances, each with diverse inputs, challenging models to interpret and interact with web pages effectively. Evaluations of state-of-the-art models reveal significant room for improvement, highlighting the need for advanced web-based agents capable of handling real-world web interactions. 
 
 - [WorkArena: How Capable Are Web Agents at Solving Common Knowledge Work Tasks?](https://arxiv.org/abs/2403.07718)
     - Alexandre Drouin, Maxime Gasse, Massimo Caccia, Issam H. Laradji, Manuel Del Verme, Tom Marty, Léo Boisvert, Megh Thakkar, Quentin Cappart, David Vazquez, Nicolas Chapados, Alexandre Lacoste