Really excited for the release of 𝜏³-bench, which brings interactive agent evals ever closer to real-world use cases across two dimensions: 1. 𝜏-knowledge evaluates agents that need to operate over noisy knowledge bases to figure out the correct policies/tools to use while serving a user 2. 𝜏-voice tests voice agents in interactive customer service style settings. If you are developing embedding or voice models for AI agents, 𝜏³ is a great testbed for you to see how your models would perform in a realistic downstream use case. Blog: sierra.ai/blog/bench-advanci… Tweets from @BenShi34 and @keshav_57: nitter.net/benshi34/status/203436… nitter.net/keshav_57/status/20346…
RESEARCH
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LeWorldModel: Teaching AI to Simulate and Understand World
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LeWorldModel: Teaching AI to Simulate and Understand the World
— Satya Mallick (@LearnOpenCV) 3 avril 2026
In this episode of Artificial Intelligence: Papers and Concepts, we explore LeWorldModel, a new approach to building AI systems that can model and simulate real-world environments. Instead of reacting to inputs… pic.twitter.com/Jlf3hbSIfvLeWorldModel: Teaching AI to Simulate and Understand the World In this episode of Artificial Intelligence: Papers and Concepts, we explore LeWorldModel, a new approach to building AI systems that can model and simulate real-world environments. Instead of reacting to inputs step-by-step, world models aim to learn underlying dynamics—allowing AI to predict outcomes, plan actions, and reason about future scenarios. We break down why traditional models struggle with long-term reasoning and planning, how world models enable a deeper understanding of cause and effect, and what this means for applications like robotics, gaming, and autonomous systems. If you’re interested in world models, reinforcement learning, or the future of AI systems that can think ahead and simulate reality, this episode explains why LeWorldModel represents an important step toward more general and intelligent AI. Resources: Paper Link: arxiv.org/pdf/2603.19312v1 Interested in Computer Vision and AI consulting and product development services? Email us at contact@bigvision.ai or visit us at bigvision.ai
→ View original post on X — @learnopencv, 2026-04-03 14:30 UTC
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Google DeepMind Adopts Apache 2.0 License for Open Models
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Love that Google DeepMind is following OpenAI’s suit w/ using Apache 2.0 license for their open weights models – congrats! but, can we please stop using Arena Elo as the de facto measure of performance?
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AI Coding Agents Reach Inflection Point in November 2025
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My biggest takeaways from @simonw
: 1. November 2025 was an inflection point for AI coding. GPT 5.1 and Claude Opus 4.5 crossed a threshold where coding agents went from “mostly works” to “almost always does what you want it to do.” Software engineers who tinkered over the -
Gemma 4 Demonstrates Impressive Capabilities for Compact Models
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Gemma 4 has incredible capabilities for such small models. Follow @googlegemma for the latest information.
→ View original post on X — @demishassabis, 2026-04-03 14:01 UTC
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Gemma 4 Outperforms Models 10x Its Size
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Gemma 4 outperforms models over 10x their size! (note the x-axis is log scale!) [Translated from EN to English]
→ View original post on X — @demishassabis, 2026-04-03 14:01 UTC
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Stanford functional analysis with Paul Cohen educational experience
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I took Stanford functional analysis with Paul Cohen. One of the best educational experiences of my life.
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YOLOv11: The Next Leap in Real-Time Object Detection
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🐍 YOLOv11: The Next Leap in Real-Time Detection
— Satya Mallick (@LearnOpenCV) 3 avril 2026
For nearly a decade, the YOLO family kept pushing real-time object detection forward. In 2024, YOLOv11 arrived faster, more accurate, and easier to deploy. 🚀
With improved multi-scale fusion, streamlined inference, and models… pic.twitter.com/4T0rz9yv6x🐍 YOLOv11: The Next Leap in Real-Time Detection For nearly a decade, the YOLO family kept pushing real-time object detection forward. In 2024, YOLOv11 arrived faster, more accurate, and easier to deploy. 🚀 With improved multi-scale fusion, streamlined inference, and models sized for both edge devices and maximum accuracy, YOLOv11 stayed true to the YOLO philosophy: fast enough for real-time, accurate enough for production, simple enough to deploy everywhere. ⚡ #YOLOv11 #ComputerVision #DeepLearning #AI #ObjectDetection #MachineLearning #AIResearch #DataScience 🤖
→ View original post on X — @learnopencv, 2026-04-03 13:26 UTC
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Claude Code Skill Converts ArXiv Papers to Working Code
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I made a Claude Code skill that turns any arxiv paper into working code.
— pdawg (@prathamgrv) 3 avril 2026
Every line traces back to the paper section it came from & any implementation detail the paper skips will be flagged, and not assumed.
open sourcing it –https://t.co/sSio4JfpIo pic.twitter.com/5XqlGgQsqCI made a Claude Code skill that turns any arxiv paper into working code. Every line traces back to the paper section it came from & any implementation detail the paper skips will be flagged, and not assumed. open sourcing it – github.com/PrathamLearnsToCo…