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🚀Introducing VisualWebBench: A Comprehensive Benchmark for Multimodal Web Page Understanding and Grounding. 🤔What's this all about? Why this benchmark? > Back in Nov 2023, when we released MMMU ( a comprehensive multimodal understanding benchmark, we received feedback that it included very few UI screenshots. Considering the growing importance of...

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Xiang Yue

6,577 subscribers

56,696 views • 2 years ago •via X (Twitter)

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Li Bo2 years ago

Congrats! May I know if it's possible to PR this dataset into lmms-eval?

Xiang Yue@ICLR2025🇸🇬's profile picture
Xiang Yue@ICLR2025🇸🇬2 years ago

Sure! Happy to do that!

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Travala.com 🏨 ✈️4 years ago

This one's for the Crypto Whales ⬇️ NEW exclusive & luxurious experiences have been added to Apply today & #TravelUnlikeAnyOther with your favorite cryptocurrency.

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Homer Quan2 years ago

Nice work, this will be the foundational block of emerging “deep search” business model where Google is trying to pivot its search business into)

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Janak Agrawal2 years ago

This benchmark is amazing!! Does this benchmark reference Gemini 1 Pro or Gemini 1.5 Pro?

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Xiang Yue@ICLR2025🇸🇬2 years ago

Thanks! We used Gemini 1 Pro.

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Richard Socher

63,660 views • 1 year ago

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Artificial Analysis

132,623 views • 1 month ago

Super excited to share 🧠MLGym 🦾 – the first Gym environment for AI Research Agents 🤖🔬 We introduce MLGym and MLGym-Bench, a new framework and benchmark for evaluating and developing LLM agents on AI research tasks. The key contributions of our work are: 🕹️ Enables the exploration of different training algorithms for AI Research Agents such as RL 🛠️ Provides a flexible evaluation framework that can accommodate different artifacts such as models, algorithms, or predictions 🤖 Allows researchers to evaluate any model without the need to develop a custom agentic harness 🎯 Introduces 13 diverse open-ended AI Research tasks for evaluating AI Research Agents on a wide range of domains such as computer vision, natural language processing, reinforcement learning, game theory, and logical reasoning. 📈 Proposes a new evaluation metric for AI Research Agents MLGym makes it easy to: 1) Add new tasks 2) Evaluate new models 3) Integrate new agents Check out a video of the MLGym Agent to see how it performs the full pipeline of idea generation💡, implementation 👩‍💻, experimentation 👩‍🔬, and iteration 🔄 to improve on ML tasks. Huge thanks to the exceptionally talented Deepak Nathani who led this work and to all the other amazing collaborators who made this possible 🙏🫶🚀
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Super excited to share 🧠MLGym 🦾 – the first Gym environment for AI Research Agents 🤖🔬 We introduce MLGym and MLGym-Bench, a new framework and benchmark for evaluating and developing LLM agents on AI research tasks. The key contributions of our work are: 🕹️ Enables the exploration of different training algorithms for AI Research Agents such as RL 🛠️ Provides a flexible evaluation framework that can accommodate different artifacts such as models, algorithms, or predictions 🤖 Allows researchers to evaluate any model without the need to develop a custom agentic harness 🎯 Introduces 13 diverse open-ended AI Research tasks for evaluating AI Research Agents on a wide range of domains such as computer vision, natural language processing, reinforcement learning, game theory, and logical reasoning. 📈 Proposes a new evaluation metric for AI Research Agents MLGym makes it easy to: 1) Add new tasks 2) Evaluate new models 3) Integrate new agents Check out a video of the MLGym Agent to see how it performs the full pipeline of idea generation💡, implementation 👩‍💻, experimentation 👩‍🔬, and iteration 🔄 to improve on ML tasks. Huge thanks to the exceptionally talented Deepak Nathani who led this work and to all the other amazing collaborators who made this possible 🙏🫶🚀

Roberta Raileanu

105,157 views • 1 year ago

Destra Network| One-Click Decentralized Hosting - Upgrade: v0.2 Today, we are excited to launch version 0.2 of Destra Decentralized Web Hosting, which introduces multiple enhancements and upgrades over the existing v0.1. Here's a detailed breakdown of the updates and features in v0.2: - Enhanced UI/UX: Based on community feedback, we've made significant design improvements to the user interface and user experience. The UI is now more user-friendly and comprehensive compared to v0.1. - Live Deployment Previews: Users can now view and monitor websites they've deployed on Destra Decentralized Web Hosting in real-time. Additionally, there's functionality to check the health of deployments on the Destra Network. -Destra DNS Management: Version 0.2 introduces a UI for Destra DNS management, including the ability to manage DNS zone records. - ENS Domains Management UI: The v0.2 update includes a user interface for managing ENS domains, enhancing control and accessibility, the functionality will be going live in next performance upgrade. The video attached below demonstrates the process.
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Destra Network| One-Click Decentralized Hosting - Upgrade: v0.2 Today, we are excited to launch version 0.2 of Destra Decentralized Web Hosting, which introduces multiple enhancements and upgrades over the existing v0.1. Here's a detailed breakdown of the updates and features in v0.2: - Enhanced UI/UX: Based on community feedback, we've made significant design improvements to the user interface and user experience. The UI is now more user-friendly and comprehensive compared to v0.1. - Live Deployment Previews: Users can now view and monitor websites they've deployed on Destra Decentralized Web Hosting in real-time. Additionally, there's functionality to check the health of deployments on the Destra Network. -Destra DNS Management: Version 0.2 introduces a UI for Destra DNS management, including the ability to manage DNS zone records. - ENS Domains Management UI: The v0.2 update includes a user interface for managing ENS domains, enhancing control and accessibility, the functionality will be going live in next performance upgrade. The video attached below demonstrates the process.

Destra Network

29,327 views • 2 years ago

Introducing ALE-Bench, ALE-Agent! Towards Automating Long-Horizon Algorithm Engineering for Hard Optimization Problems Blog: Paper: ALE-Bench is a coding benchmark primarily focused on hard optimization (NP-hard) problems. We developed this benchmark with AtCoder Inc., a leading coding contest platform company. What makes ALE-Bench unique is its focus on hard optimization problems that demand long-horizon and creative reasoning. It’s open-ended, in the sense that true optima are out of reach (NP-hard) and scores can continuously improve. We believe this benchmark has the potential to become one of the key benchmarks for reasoning and coding in the next generation. ALE-Agent is our end-to-end agent that we specifically designed for this challenging domain. In fact, our ALE-Agent has already built an impressive track record in the wild! In May 2025, our agent participated in a live AtCoder Heuristic Competition (AHC), alongside 1,000 other participants in real-time. AHC is considered to be one of the most challenging coding competitions in this domain. Our ALE-Agent achieved an impressive ranking of 21st out of 1,000 human participants in the competition (top 2%), marking a turning point for AI discovery of solutions to hard optimization problems with a wide spectrum of important real world applications such as logistics, routing, packing, factory production planning, power-grid balancing. We look forward to applying this technology to real industrial optimization opportunities. Building on the insights from this study, Sakana AI will continue to tackle the challenge of developing AI with even greater algorithm engineering capabilities. ALE-Bench Dataset: ALE-Bench Code: This research was conducted in collaboration with AtCoder Inc. (AtCoder). We are deeply grateful for their outstanding expertise and contributions in optimization and algorithms, which were invaluable in providing data, analyzing results, and enabling our AI agent’s participation in their contests.
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Introducing ALE-Bench, ALE-Agent! Towards Automating Long-Horizon Algorithm Engineering for Hard Optimization Problems Blog: Paper: ALE-Bench is a coding benchmark primarily focused on hard optimization (NP-hard) problems. We developed this benchmark with AtCoder Inc., a leading coding contest platform company. What makes ALE-Bench unique is its focus on hard optimization problems that demand long-horizon and creative reasoning. It’s open-ended, in the sense that true optima are out of reach (NP-hard) and scores can continuously improve. We believe this benchmark has the potential to become one of the key benchmarks for reasoning and coding in the next generation. ALE-Agent is our end-to-end agent that we specifically designed for this challenging domain. In fact, our ALE-Agent has already built an impressive track record in the wild! In May 2025, our agent participated in a live AtCoder Heuristic Competition (AHC), alongside 1,000 other participants in real-time. AHC is considered to be one of the most challenging coding competitions in this domain. Our ALE-Agent achieved an impressive ranking of 21st out of 1,000 human participants in the competition (top 2%), marking a turning point for AI discovery of solutions to hard optimization problems with a wide spectrum of important real world applications such as logistics, routing, packing, factory production planning, power-grid balancing. We look forward to applying this technology to real industrial optimization opportunities. Building on the insights from this study, Sakana AI will continue to tackle the challenge of developing AI with even greater algorithm engineering capabilities. ALE-Bench Dataset: ALE-Bench Code: This research was conducted in collaboration with AtCoder Inc. (AtCoder). We are deeply grateful for their outstanding expertise and contributions in optimization and algorithms, which were invaluable in providing data, analyzing results, and enabling our AI agent’s participation in their contests.

Sakana AI

237,195 views • 1 year ago

📢 Join us tomorrow morning at our CVPR 2025 poster session (#340, ExHall D, 10:30am–12:30pm) to chat about Project Magma 👉 This is a big team effort to build a multimodal agentic model capable of understanding and acting in both digital and physical environments—just like how we interact with the world every day. 🚀 Even more exciting: we demonstrate the scaling potential of agent pretraining on large-scale human instructional videos through our Set-of-Mark (SoM) and Trace-of-Mark (ToM), showcasing strong zero-shot performance in: 1) multimodal image/video understanding, 2) UI navigation, 3) Real-world robot manipulation and even 4) Gaming! We've received encouraging feedback over the past few days—and this is only the beginning. A small step forward, with exciting things ahead!
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📢 Join us tomorrow morning at our CVPR 2025 poster session (#340, ExHall D, 10:30am–12:30pm) to chat about Project Magma 👉 This is a big team effort to build a multimodal agentic model capable of understanding and acting in both digital and physical environments—just like how we interact with the world every day. 🚀 Even more exciting: we demonstrate the scaling potential of agent pretraining on large-scale human instructional videos through our Set-of-Mark (SoM) and Trace-of-Mark (ToM), showcasing strong zero-shot performance in: 1) multimodal image/video understanding, 2) UI navigation, 3) Real-world robot manipulation and even 4) Gaming! We've received encouraging feedback over the past few days—and this is only the beginning. A small step forward, with exciting things ahead!

Jianwei Yang

13,112 views • 1 year ago

🔍 Introducing BackSearch. LLMs are increasingly asked to predict the future, but a good backtest requires a snapshot of the internet at a point in time. BackSearch allows LLMs to search the web as it was on a particular date. It’s great for: 🔮 Forecasting and prediction markets. 📈 Quantitative finance. 🌍 RL environments that simulate the world. 🥶 Freezing websearch for benchmark reproducibility We’re releasing a narrow slice of our index to begin with, focused on the news domain for 2026. Based on feedback we’ll open up more of our index in subsequent releases. 👇 Try BackSearch out with the link below.
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🔍 Introducing BackSearch. LLMs are increasingly asked to predict the future, but a good backtest requires a snapshot of the internet at a point in time. BackSearch allows LLMs to search the web as it was on a particular date. It’s great for: 🔮 Forecasting and prediction markets. 📈 Quantitative finance. 🌍 RL environments that simulate the world. 🥶 Freezing websearch for benchmark reproducibility We’re releasing a narrow slice of our index to begin with, focused on the news domain for 2026. Based on feedback we’ll open up more of our index in subsequent releases. 👇 Try BackSearch out with the link below.

General Reasoning

419,098 views • 1 month ago

Tencent announces AppAgent Multimodal Agents as Smartphone Users paper page: Recent advancements in large language models (LLMs) have led to the creation of intelligent agents capable of performing complex tasks. This paper introduces a novel LLM-based multimodal agent framework designed to operate smartphone applications. Our framework enables the agent to operate smartphone applications through a simplified action space, mimicking human-like interactions such as tapping and swiping. This novel approach bypasses the need for system back-end access, thereby broadening its applicability across diverse apps. Central to our agent's functionality is its innovative learning method. The agent learns to navigate and use new apps either through autonomous exploration or by observing human demonstrations. This process generates a knowledge base that the agent refers to for executing complex tasks across different applications. To demonstrate the practicality of our agent, we conducted extensive testing over 50 tasks in 10 different applications, including social media, email, maps, shopping, and sophisticated image editing tools. The results affirm our agent's proficiency in handling a diverse array of high-level tasks.
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Tencent announces AppAgent Multimodal Agents as Smartphone Users paper page: Recent advancements in large language models (LLMs) have led to the creation of intelligent agents capable of performing complex tasks. This paper introduces a novel LLM-based multimodal agent framework designed to operate smartphone applications. Our framework enables the agent to operate smartphone applications through a simplified action space, mimicking human-like interactions such as tapping and swiping. This novel approach bypasses the need for system back-end access, thereby broadening its applicability across diverse apps. Central to our agent's functionality is its innovative learning method. The agent learns to navigate and use new apps either through autonomous exploration or by observing human demonstrations. This process generates a knowledge base that the agent refers to for executing complex tasks across different applications. To demonstrate the practicality of our agent, we conducted extensive testing over 50 tasks in 10 different applications, including social media, email, maps, shopping, and sophisticated image editing tools. The results affirm our agent's proficiency in handling a diverse array of high-level tasks.

AK

343,919 views • 2 years ago