1 00:00:03,000 --> 00:00:06,000 Evan: Welcome to Daily Paper Cast. 2 00:00:06,048 --> 00:00:15,368 Ashley: Today, we dive into one of the selected papers from Hugging Face's daily paper list of September 2, 2026, with 49 upvotes. 3 00:00:15,432 --> 00:00:19,512 Evan: The paper is titled 'UI-Venus-2 Technical Report'. 4 00:00:19,560 --> 00:00:26,620 Ashley: It's authored by Zhuohan Cai and Haoxing Chen, with Shuheng Shen as the corresponding author from Ant Group. 5 00:00:26,664 --> 00:00:28,984 Evan: Let's dive into their introduction. 6 00:00:28,984 --> 00:00:32,924 What's the background and main objectives of this paper, Ashley? 7 00:00:32,976 --> 00:00:33,936 Ashley: Sure, Evan. 8 00:00:33,936 --> 00:00:41,786 The paper begins by highlighting the significant strides made in creating multimodal Graphical User Interface, or GUI, agents. 9 00:00:41,786 --> 00:00:46,116 These agents represent a promising approach for automating digital tasks. 10 00:00:46,116 --> 00:00:54,296 Essentially, rather than relying on text inputs alone, these agents can interpret and interact with visual elements in applications. 11 00:00:54,360 --> 00:00:56,320 Evan: That sounds quite advanced. 12 00:00:56,320 --> 00:00:59,280 But what challenges do they face with these agents? 13 00:00:59,328 --> 00:01:01,298 Ashley: There are several key challenges. 14 00:01:01,298 --> 00:01:07,808 Transitioning from models that do well on benchmarks to those that are reliable in real-world applications is tough. 15 00:01:07,808 --> 00:01:14,828 They point out issues like limited environment coverage, brittle task construction, and unreliable reward verification. 16 00:01:14,828 --> 00:01:19,708 These need addressing before GUI agents can be widely deployed in practical settings. 17 00:01:19,752 --> 00:01:25,212 Evan: Right, so what does 'UI-Venus-2' propose to tackle these challenges? 18 00:01:25,272 --> 00:01:28,022 Ashley: UI-Venus-2 is quite ambitious. 19 00:01:28,022 --> 00:01:31,612 It's designed as a general-purpose foundation GUI agent. 20 00:01:31,612 --> 00:01:41,212 The main innovation here is its ability to operate across mobile, web, and desktop environments using a unified closed-loop reasoning-action framework. 21 00:01:41,256 --> 00:01:44,186 Evan: Closed-loop reasoning-action framework? 22 00:01:44,186 --> 00:01:45,676 Can you break that down? 23 00:01:45,720 --> 00:01:55,880 Ashley: Basically, the agent observes the current interface, reasons about the task state, executes an action, and then reacts to the environmental feedback for the next decision. 24 00:01:55,880 --> 00:02:01,340 This closed-loop cycle is crucial for it to function effectively across different environments. 25 00:02:01,392 --> 00:02:03,012 Evan: That’s interesting. 26 00:02:03,012 --> 00:02:06,992 How do they propose to bridge the gap towards practical deployment? 27 00:02:07,056 --> 00:02:13,946 Ashley: Their approach scales three critical dimensions simultaneously: environments, tasks, and verification. 28 00:02:13,946 --> 00:02:21,446 They’ve expanded environment coverage to over 170 multilingual mobile apps and native desktop operating systems. 29 00:02:21,446 --> 00:02:27,246 For tasks, they use a research pipeline that grounds instructions in actual app functionalities. 30 00:02:27,246 --> 00:02:38,076 Verification is enhanced with trace- and sample-level evaluators, leveraging visual keypoints and multi-model voting, to ensure the reinforcement learning signals are reliable. 31 00:02:38,136 --> 00:02:41,606 Evan: It's clear they are making comprehensive advancements. 32 00:02:41,606 --> 00:02:44,116 Any notable contributions from their work? 33 00:02:44,160 --> 00:02:47,560 Ashley: Yes, the paper outlines a few key contributions. 34 00:02:47,560 --> 00:02:53,540 Firstly, they have scaled up the multilingual mobile-use environments with reliable task generation. 35 00:02:53,540 --> 00:03:02,100 They’ve added coverage for over 100 Chinese and 70 English language apps, improving task executability even as interfaces change. 36 00:03:02,160 --> 00:03:04,120 Evan: That's quite an achievement. 37 00:03:04,120 --> 00:03:05,200 What else? 38 00:03:05,256 --> 00:03:15,446 Ashley: They’ve also extended capabilities for desktop use from scratch, training the agent to handle tasks across mobile, web, and OS environments in a unified framework. 39 00:03:15,446 --> 00:03:24,416 This means UI-Venus-2 can perceive, reason, and act within desktop environments, efficiently managing a broader range of interactions. 40 00:03:24,480 --> 00:03:28,680 Evan: It sounds like they’ve made significant progress on verification, too. 41 00:03:28,728 --> 00:03:29,748 Ashley: That's correct. 42 00:03:29,748 --> 00:03:39,788 Verification mechanisms have been fine-tuned to evaluate task completion more precisely, using task-relevant visual keypoints and aggregating judgments through model voting. 43 00:03:39,788 --> 00:03:44,388 This makes the reward signal more robust and less prone to exploitation. 44 00:03:44,448 --> 00:03:48,628 Evan: They've put in a lot of effort into making this open-source, right? 45 00:03:48,672 --> 00:03:49,492 Ashley: Indeed. 46 00:03:49,492 --> 00:03:54,022 The code and full parameter weights of UI-Venus-2 are publicly available. 47 00:03:54,022 --> 00:04:01,732 This lowers the barrier for researchers in GUI agent research, facilitating reproducible studies in reinforcement learning and verification. 48 00:04:01,732 --> 00:04:07,632 They hope it accelerates community-driven innovation towards more generalizable and reliable agents. 49 00:04:07,740 --> 00:04:09,000 Evan: Impressive. 50 00:04:09,000 --> 00:04:12,390 And that wraps up our introduction of UI-Venus-2. 51 00:04:12,390 --> 00:04:18,200 Stay tuned as we delve into the methodology and other detailed sections in the following segments. 52 00:04:19,466 --> 00:04:24,706 Evan: Alright Ashley, let's dive into the methods they used for UI-Venus-2. 53 00:04:24,706 --> 00:04:26,946 What's their overall system setup? 54 00:04:27,032 --> 00:04:28,432 Ashley: Great question, Evan. 55 00:04:28,432 --> 00:04:33,172 UI-Venus-2 is essentially a general-purpose foundation GUI agent. 56 00:04:33,172 --> 00:04:39,672 Its standout feature is the combination of visual perception and advanced reasoning within one unified framework. 57 00:04:39,672 --> 00:04:49,242 This allows the agent to operate autonomously across a diverse array of digital environments, including mobile apps, desktop OS, and web platforms. 58 00:04:49,298 --> 00:04:50,728 Evan: Interesting. 59 00:04:50,728 --> 00:04:54,998 So, how do they integrate these diverse capabilities? 60 00:04:55,058 --> 00:04:58,778 Ashley: The key here is their unified reasoning-action paradigm. 61 00:04:58,778 --> 00:05:15,918 The process involves the agent observing the rendered interface images, interpreting the current visual context, translating high-level user intent into executable GUI actions, and continuously adapting its decisions based on the feedback from the environment until the task is accomplished. 62 00:05:15,962 --> 00:05:18,012 Evan: That sounds comprehensive. 63 00:05:18,012 --> 00:05:19,542 Let's break it down. 64 00:05:19,542 --> 00:05:22,002 How was UI-Venus-2 trained? 65 00:05:22,058 --> 00:05:32,078 Ashley: Their training pipeline is divided into three stages: multimodal mid-training, offline reinforcement learning, and multi-teacher on-policy distillation. 66 00:05:32,198 --> 00:05:34,318 Evan: Let's take them one by one. 67 00:05:34,318 --> 00:05:37,198 Start with the multimodal mid-training. 68 00:05:37,250 --> 00:05:47,000 Ashley: In the multimodal mid-training stage, they first trained UI-Venus-2 on a large-scale, heterogeneous mixture of synthetic and interaction-oriented data. 69 00:05:47,000 --> 00:05:52,790 Mobile, web, and OS navigation tasks form the dominant part of their training corpus. 70 00:05:52,790 --> 00:06:02,990 They simulated a variety of interactive environments, spanning different application interfaces and operating systems, collecting executable trajectories within these environments. 71 00:06:03,050 --> 00:06:08,250 Evan: So, what about the realism and quality of these trajectories? 72 00:06:08,336 --> 00:06:09,366 Ashley: Great point. 73 00:06:09,366 --> 00:06:17,356 They balanced realism, linguistic diversity, and task coverage by constructing task queries through a combination of curated query seeds. 74 00:06:17,356 --> 00:06:26,766 The resulting trajectories were then validated using a human–discriminator collaborative process, which filters invalid, ambiguous, or low-quality interactions. 75 00:06:26,870 --> 00:06:27,790 Evan: Got it. 76 00:06:27,790 --> 00:06:31,950 And the second stage, offline reinforcement learning? 77 00:06:31,994 --> 00:06:39,764 Ashley: For offline reinforcement learning, the focus shifts to task-specific supervision, tailored to different interaction scenarios. 78 00:06:39,764 --> 00:06:46,284 They constructed large-scale step-level reinforcement learning trajectories for Mobile, OS, and Web environments. 79 00:06:46,284 --> 00:06:54,554 This helps optimize state-aware action selection, multi-step navigation, transition consistency, and execution reliability. 80 00:06:54,602 --> 00:06:58,272 Evan: That covers Mobile, OS, and Web. 81 00:06:58,272 --> 00:07:02,242 How did they handle CAPTCHA and Grounding tasks? 82 00:07:02,306 --> 00:07:13,536 Ashley: For CAPTCHA and Grounding tasks, they used a programmatic synthesis framework to embed verified CAPTCHA instances and grounding targets into realistic web-page and app interfaces. 83 00:07:13,536 --> 00:07:21,826 This allowed the model to learn robust target identification and precise spatial localization even under visually cluttered conditions. 84 00:07:21,890 --> 00:07:26,090 Evan: They seem to have covered various types of environments and tasks. 85 00:07:26,090 --> 00:07:28,430 What's next in their training process? 86 00:07:28,490 --> 00:07:34,100 Ashley: The final stage is Multi-teacher On-policy Distillation, or MOPD. 87 00:07:34,100 --> 00:07:46,390 Here, they consolidate the capabilities acquired throughout the different stages by integrating multimodal reasoning and specialized skills from the foundation models with those gained from Grounding and CAPTCHA tasks. 88 00:07:46,502 --> 00:07:51,762 Evan: How do they prevent issues when merging these independent expert models? 89 00:07:51,818 --> 00:07:52,978 Ashley: Good question. 90 00:07:52,978 --> 00:08:00,498 During this stage, they use an on-policy distillation paradigm, where teachers score trajectories sampled by the student model. 91 00:08:00,498 --> 00:08:10,938 They focus on structured action-aware supervision to make sure the model learns the importance of different types of tokens, prioritizing executable actions over reasoning traces. 92 00:08:10,994 --> 00:08:16,314 Evan: So, the supervision is tailored to the nature of GUI tasks? 93 00:08:16,370 --> 00:08:17,360 Ashley: Exactly. 94 00:08:17,360 --> 00:08:22,370 They adapt the weighting of the distillation signal based on the correctness of the student’s actions. 95 00:08:22,370 --> 00:08:28,340 For instance, if the entire action is correct, they suppress the distillation signal for that action. 96 00:08:28,340 --> 00:08:34,850 If only the action type is correct but its parameters are not, they strengthen supervision over the action span. 97 00:08:34,978 --> 00:08:37,298 Evan: That sounds very meticulous. 98 00:08:37,298 --> 00:08:41,178 How do they finally unify all these aspects into the model? 99 00:08:41,234 --> 00:08:52,394 Ashley: The structured action-aware distillation essentially helps the model integrate broad multimodal reasoning capabilities with specific skills like precise spatial localization and robust navigation. 100 00:08:52,394 --> 00:09:02,374 By concentrating the distillation signal on critical executable behaviors, they're able to create a unified model that performs effectively across diverse GUI environments. 101 00:09:02,426 --> 00:09:04,726 Evan: This is all very detailed. 102 00:09:04,726 --> 00:09:09,406 It seems like a well-rounded approach to developing a robust GUI agent. 103 00:09:09,458 --> 00:09:10,468 Ashley: Indeed. 104 00:09:10,468 --> 00:09:22,278 By offering a capable, efficient, and open-source foundation, UI-Venus-2 aims to advance the field toward more verifiable and self-reflective agents for real-world applications. 105 00:09:22,372 --> 00:09:24,552 Evan: And that's the end of the Method section. 106 00:09:24,552 --> 00:09:30,302 Stay tuned as we move on to the experimental results and evaluations in our next segment. 107 00:09:31,563 --> 00:09:36,353 Evan: Alright Ashley, let's move on to the experiments and results section. 108 00:09:36,353 --> 00:09:40,743 How did UI-Venus-2 perform across different benchmarks? 109 00:09:40,803 --> 00:09:49,023 Ashley: UI-Venus-2 was evaluated across several benchmarks, covering mobile, desktop, web environments, and CAPTCHA-solving tasks. 110 00:09:49,023 --> 00:09:51,203 Let's dive into them one by one. 111 00:09:51,327 --> 00:09:52,547 Evan: Sounds good. 112 00:09:52,547 --> 00:09:54,587 Let's start with mobile use. 113 00:09:54,587 --> 00:09:57,047 How did UI-Venus-2 perform there? 114 00:09:57,099 --> 00:10:00,799 Ashley: UI-Venus-2 excelled in mobile usage scenarios. 115 00:10:00,799 --> 00:10:15,969 On the MobileGym benchmark, which provides a controlled, browser-based simulation environment, UI-Venus-2-27B achieved a success rate of 60.5%, while the smaller UI-Venus-2-9B model reached 52.7%. 116 00:10:15,969 --> 00:10:22,999 These figures outperform the strongest baseline, Seed2.0 Pro, which stands at 52.0%. 117 00:10:23,043 --> 00:10:24,293 Evan: Impressive. 118 00:10:24,293 --> 00:10:28,203 What about a more realistic setting like VenusBench-Mobile? 119 00:10:28,251 --> 00:10:42,591 Ashley: VenusBench-Mobile, which focuses on realistic, user-centric evaluations, showed UI-Venus-2-27B with a success rate of 48.7% and UI-Venus-2-9B at 46.5%. 120 00:10:42,591 --> 00:10:51,611 Both models significantly outperformed the strongest prior baseline, Claude-Opus-4.6, which had a success rate of 36.5%. 121 00:10:51,735 --> 00:10:54,795 Evan: What about Android-specific benchmarks? 122 00:10:54,843 --> 00:11:08,053 Ashley: On AndroidWorld, which spans a variety of real-world Android applications, UI-Venus-2-27B achieved 84.0%, and UI-Venus-2-9B hit 80.2%. 123 00:11:08,053 --> 00:11:17,043 This edges out the previous best, UI-Venus-1.5-30B-A3B, which had a success rate of 77.6%. 124 00:11:17,091 --> 00:11:18,211 Evan: Nice. 125 00:11:18,211 --> 00:11:23,011 How did they perform on more challenging mobile benchmarks like MobileWorld? 126 00:11:23,067 --> 00:11:28,947 Ashley: MobileWorld is indeed more challenging due to its long-horizon and cross-application task structure. 127 00:11:28,947 --> 00:11:40,617 Here, UI-Venus-2-27B achieved a success rate of 76.1% in the standard 50-step setting, while UI-Venus-2-9B achieved 65.8%. 128 00:11:40,617 --> 00:11:48,387 Under the extended 100-step setting, the performances increased to 82.9% and 75.2%, respectively. 129 00:11:48,387 --> 00:11:56,687 Although they trailed behind the top performer, Qwen-UI-Agent-27B, at 82.1%, they still performed admirably. 130 00:11:56,739 --> 00:11:57,669 Evan: Got it. 131 00:11:57,669 --> 00:12:02,859 What about benchmarks that require personalized assistance and memory retention? 132 00:12:02,907 --> 00:12:17,187 Ashley: For tests like KnowUBench, which evaluates personalized mobile assistance, UI-Venus-2-27B achieved a success rate of 59.7%, while UI-Venus-2-9B scored 56.5%. 133 00:12:17,187 --> 00:12:23,057 Both surpassed the previous best, Seed2.0 Pro, at 51.6%. 134 00:12:23,057 --> 00:12:35,327 For memory-focused benchmarks like MemGUI, UI-Venus-2-27B achieved the highest score of 70.3%, with UI-Venus-2-9B following at 62.6%. 135 00:12:35,379 --> 00:12:37,079 Evan: That’s quite comprehensive. 136 00:12:37,079 --> 00:12:39,799 Let's switch gears to desktop environments. 137 00:12:39,799 --> 00:12:42,079 How did UI-Venus-2 fare there? 138 00:12:42,123 --> 00:12:47,503 Ashley: In desktop environments, UI-Venus-2 also showed robust performance. 139 00:12:47,503 --> 00:13:01,383 On OSWorld-Verified, which evaluates real computer interactions, UI-Venus-2-27B scored 80.5%, close to the leading model Claude-Opus-4.8 at 83.4%. 140 00:13:01,383 --> 00:13:06,503 The smaller 9B model achieved a respectable score of 70.8%. 141 00:13:06,555 --> 00:13:09,935 Evan: What about more complex desktop tasks? 142 00:13:09,987 --> 00:13:25,677 Ashley: For DeskCraft, which focuses on long-horizon professional workflows and collaboration, UI-Venus-2-27B led with a success rate of 55.5%, outperforming the best baseline Kimi-K2.6 at 41.4%. 143 00:13:25,677 --> 00:13:30,567 The 9B model also did well, scoring 48.0%. 144 00:13:30,627 --> 00:13:34,827 Evan: And in long-horizon scenarios like OSWorld 2.0? 145 00:13:34,875 --> 00:13:42,025 Ashley: For OSWorld 2.0, UI-Venus-2's performance was measured by Binary Accuracy and Partial Score. 146 00:13:42,025 --> 00:13:50,275 The UI-Venus-2-27B achieved a Binary Accuracy of 2.8% and a Partial Score of 13.2%. 147 00:13:50,275 --> 00:13:57,295 The 9B model scored 0.0% for Binary Accuracy and 7.5% for Partial Score. 148 00:13:57,295 --> 00:14:00,935 These results highlight areas for possible future improvement. 149 00:14:01,037 --> 00:14:01,967 Evan: Indeed. 150 00:14:01,967 --> 00:14:04,337 Now, moving to web navigation. 151 00:14:04,337 --> 00:14:06,887 How did UI-Venus-2 perform there? 152 00:14:06,939 --> 00:14:22,719 Ashley: On WebVoyager, which evaluates end-to-end web navigation, UI-Venus-2-27B scored 93.4%, the highest among all evaluated models, including the previous leader Fara1.5 at 89.3%. 153 00:14:22,719 --> 00:14:27,739 The 9B variant also did very well, achieving 90.8%. 154 00:14:27,795 --> 00:14:29,475 Evan: That's excellent. 155 00:14:29,475 --> 00:14:36,515 What about benchmarks like Online-Mind2Web that test realistic, dynamic online environments? 156 00:14:36,579 --> 00:14:43,829 Ashley: In Online-Mind2Web, UI-Venus-2-27B again led the performance with 78.3%. 157 00:14:43,829 --> 00:14:49,019 The 9B version was not far behind, scoring 74.0%. 158 00:14:49,019 --> 00:14:56,599 Both outperformed the previous best, Fara1.5-27B, which had a score of 72.3%. 159 00:14:56,643 --> 00:15:00,223 Evan: How did they handle replica environments like REAL? 160 00:15:00,267 --> 00:15:11,007 Ashley: For REAL, which provides deterministic high-fidelity simulations, UI-Venus-2-27B achieved a new state-of-the-art score of 80.2%. 161 00:15:11,007 --> 00:15:20,767 The 9B model attained a strong score of 76.9%, both surpassing the previous best of 74.4% by a fair margin. 162 00:15:20,811 --> 00:15:25,201 Evan: Long-horizon benchmarks like Odysseys must have been challenging. 163 00:15:25,201 --> 00:15:26,431 How did they fare? 164 00:15:26,475 --> 00:15:40,325 Ashley: In Odysseys, a benchmark for long-horizon, multi-step workflows, UI-Venus-2-27B achieved top scores with 80.4 in averaged rubric score and 66.3 in perfect rubric score. 165 00:15:40,325 --> 00:15:43,975 This was distinctly higher than the previous best baseline. 166 00:15:44,019 --> 00:15:47,239 Evan: What about GUI grounding capabilities? 167 00:15:47,283 --> 00:15:51,153 Ashley: For GUI grounding, they evaluated on multiple benchmarks. 168 00:15:51,153 --> 00:16:00,603 For example, on VenusBench-GD, UI-Venus-2-27B achieved 80.1% accuracy, surpassing the previous best. 169 00:16:00,603 --> 00:16:07,443 Similar strong performances were noted on ScreenSpot-Pro, OSWorld-G-R, and UI-Vision benchmarks. 170 00:16:07,551 --> 00:16:10,471 Evan: And how did they handle CAPTCHA solving? 171 00:16:10,515 --> 00:16:23,855 Ashley: On CAPTCHA benchmarks, UI-Venus-2-27B achieved a top score of 79.9% Pass@1 on VenusBench-CAPTCHA, and the 9B model scored 78.1%. 172 00:16:23,855 --> 00:16:31,135 Both models significantly outperformed the previous leaders like Qwen3.6 at 53.0%. 173 00:16:31,135 --> 00:16:37,835 Similar top performances were observed across MCA-Bench, Spatial-CAPTCHA-Bench, and NextGen-CAPTCHAs. 174 00:16:37,929 --> 00:16:47,499 Evan: All in all, UI-Venus-2 seems to demonstrate robust performance across the board. 175 00:16:47,547 --> 00:16:48,677 Ashley: Indeed, Evan. 176 00:16:48,677 --> 00:16:51,627 And that's the end of the Experiments and Results section. 177 00:16:51,627 --> 00:16:55,367 Stay with us as we delve into more insights in the next segment. 178 00:16:56,621 --> 00:17:00,411 Evan: Alright, Ashley, let's move on to the Related Work section. 179 00:17:00,411 --> 00:17:05,081 How does UI-Venus-2 fit into the landscape of existing GUI agents? 180 00:17:05,141 --> 00:17:10,601 Ashley: To understand the context, it's important to note that the field of GUI agents has seen a lot of growth. 181 00:17:10,601 --> 00:17:19,601 Early works primarily focused on platform-specific APIs or handcrafted workflows, which demanded extensive labor and were not easily generalizable. 182 00:17:19,601 --> 00:17:27,661 In recent years, multimodal GUI agents have emerged as an interface that can bridge high-level user intent and concrete digital execution. 183 00:17:27,795 --> 00:17:28,935 Evan: I see. 184 00:17:28,935 --> 00:17:34,025 What were some of the significant advancements leading up to UI-Venus-2? 185 00:17:34,085 --> 00:17:44,175 Ashley: A key shift was the development of Multimodal Large Language Models, or MLLMs, which leverage both visual and textual data to enhance their capabilities. 186 00:17:44,175 --> 00:17:45,855 Researchers like Bai et al. 187 00:17:45,855 --> 00:17:48,045 in 2025 and Liu et al. 188 00:17:48,045 --> 00:17:51,665 in 2024 have contributed significantly in this area. 189 00:17:51,665 --> 00:18:00,425 These models are trained to perceive rendered interfaces and interact through actions like clicking, typing, and scrolling, rather than just text-based commands. 190 00:18:00,555 --> 00:18:05,225 Evan: How has the approach to agent evaluation evolved over time? 191 00:18:05,285 --> 00:18:09,165 Ashley: Initially, most evaluations were benchmark-oriented. 192 00:18:09,165 --> 00:18:18,505 However, recent user-centric evaluations like VenusBench-Mobile in 2026 and OSWorld in 2024 have exposed gaps. 193 00:18:18,505 --> 00:18:26,435 These evaluations showed that while agents might perform well on benchmarks, they often struggle in realistic, dynamic environments. 194 00:18:26,435 --> 00:18:34,545 This gap drove the need for more comprehensive environment coverage, scalable task construction, and reliable reward verification. 195 00:18:34,589 --> 00:18:39,469 Evan: And how does UI-Venus-2 address these needs? 196 00:18:39,533 --> 00:18:46,843 Ashley: UI-Venus-2 builds upon the foundations laid by its predecessors, especially UI-Venus-1.5. 197 00:18:46,843 --> 00:18:52,953 The earlier iterations pioneered a unified, end-to-end framework for GUI grounding and navigation. 198 00:18:52,953 --> 00:18:57,793 They improved the agents through large-scale GUI mid-training and reinforcement learning. 199 00:18:57,793 --> 00:19:04,633 UI-Venus-2 takes this further by systematically scaling environments, tasks, and feedback mechanisms. 200 00:19:04,685 --> 00:19:09,225 Evan: Can you elaborate on the scaling of environments? 201 00:19:09,269 --> 00:19:18,539 Ashley: To expand environmental coverage, UI-Venus-2 includes over 170 multilingual mobile apps and native desktop operating systems. 202 00:19:18,539 --> 00:19:25,269 This broadens the range of scenarios the agent can handle, ensuring it performs reliably in diverse settings. 203 00:19:25,385 --> 00:19:26,595 Evan: Interesting. 204 00:19:26,595 --> 00:19:30,025 What about task construction and data verification? 205 00:19:30,077 --> 00:19:37,597 Ashley: Task construction has become more sophisticated, employing a deep-research pipeline to generate functionally grounded instructions. 206 00:19:37,597 --> 00:19:43,017 This ensures the tasks are not just plausible but executable under varying interface states. 207 00:19:43,017 --> 00:19:54,497 For data verification, they introduced stronger evaluators that assess task completion using visual keypoints and multi-model voting, enhancing the reliability of reinforcement learning signals. 208 00:19:54,557 --> 00:19:57,817 Evan: It's clear they integrated several advanced techniques. 209 00:19:57,817 --> 00:20:01,077 How does their work compare to other state-of-the-art models? 210 00:20:01,133 --> 00:20:07,783 Ashley: UI-Venus-2 compares favorably against various baselines, including Qwen, Claude, and Seed models. 211 00:20:07,783 --> 00:20:15,183 For instance, it consistently outperformed these in mobile-use, desktop-use, web-navigation, and CAPTCHA-solving tasks. 212 00:20:15,183 --> 00:20:24,953 By integrating capabilities from different domains and ensuring robust task completion verification, UI-Venus-2 sets a new benchmark for general-purpose GUI agents. 213 00:20:25,073 --> 00:20:31,793 Evan: It sounds like UI-Venus-2's comprehensive approach has set it apart from its predecessors and peers. 214 00:20:31,853 --> 00:20:33,013 Ashley: Exactly. 215 00:20:33,013 --> 00:20:44,333 The combination of extensive environment coverage, sophisticated task construction, and reliable data verification has allowed UI-Venus-2 to advance the field significantly. 216 00:20:44,441 --> 00:20:47,481 Evan: And that's the end of the Related Work section. 217 00:20:47,481 --> 00:20:51,501 Stay tuned as we move on to more insights in the next segment. 218 00:20:52,758 --> 00:21:00,518 Evan: Alright Ashley, let's wrap up by summarizing the key contributions and takeaways from the UI-Venus-2 paper. 219 00:21:00,582 --> 00:21:01,682 Ashley: Sure, Evan. 220 00:21:01,682 --> 00:21:08,262 To start, UI-Venus-2 represents a significant advancement in the development of general-purpose GUI agents. 221 00:21:08,262 --> 00:21:15,642 It builds on previous efforts by expanding not just the environment coverage but also the depth of task construction and verification. 222 00:21:15,702 --> 00:21:16,472 Evan: Right. 223 00:21:16,472 --> 00:21:25,542 They managed to scale the environment coverage to over 170 multilingual mobile apps and native desktop applications, which is impressive. 224 00:21:25,590 --> 00:21:26,660 Ashley: Exactly. 225 00:21:26,660 --> 00:21:34,820 They also developed a deep-research pipeline to generate grounded instructions for tasks, ensuring high accuracy and executability. 226 00:21:34,820 --> 00:21:39,510 This approach helps the agent perform reliably even as interface states change. 227 00:21:39,618 --> 00:21:48,898 Evan: And for verification, they introduced trace- and sample-level evaluators using visual keypoints and multi-model voting. 228 00:21:48,898 --> 00:21:52,918 This brings more reliability to reinforcement learning signals. 229 00:21:52,974 --> 00:21:56,834 Ashley: Yes, and don't forget their impact on various benchmarks. 230 00:21:56,834 --> 00:22:06,814 UI-Venus-2 showed robust performance across mobile, web, desktop, and CAPTCHA-solving tasks, often outperforming the existing state-of-the-art models. 231 00:22:06,870 --> 00:22:07,980 Evan: Precisely. 232 00:22:07,980 --> 00:22:14,710 It's this comprehensive, well-rounded approach that makes UI-Venus-2 stand out in the field. 233 00:22:14,766 --> 00:22:27,326 Ashley: In summary, by offering a capable, efficient, and open-source foundation, UI-Venus-2 advances the development of more generalizable, verifiable, and self-reflective GUI agents. 234 00:22:27,450 --> 00:22:30,170 Evan: And that's a wrap for today's episode. 235 00:22:30,170 --> 00:22:32,970 Thanks for tuning in to Daily Paper Cast. 236 00:22:33,030 --> 00:22:37,330 Ashley: We hope you found our discussion on UI-Venus-2 insightful. 237 00:22:37,330 --> 00:22:41,770 Be sure to join us again for more deep dives into cutting-edge research. 238 00:22:41,884 --> 00:22:47,054 Evan: And remember, you can find the full paper and more details in our show notes. 239 00:22:47,054 --> 00:22:51,014 Until next time, stay curious and keep exploring! 240 00:22:51,078 --> 00:22:54,098 Ashley: Take care, and see you in the next episode.