Back when we were developing GEN3C, we often imagined... a Holodeck-like future: a simulator where multiple agents can enter the same generated world, act independently, and learn to collaborate. Gamma-World makes this feel more concrete. It is a generative multi-agent world model that takes synchronized observations and actions, then rolls out what each agent will see next in the same evolving world — action-responsive at 24 FPS. For me, the key challenge is going beyond two players. As more agents enter, identity cannot be tied to fixed slots, interaction cannot rely on dense pairwise attention, and independent actions still need to resolve into one shared state. Two ideas make this work: 1⃣ Simplex RoPE Distinct agent identities without slot bias — unique, but permutation-equivalent. 2⃣ Sparse Hub Attention Agents communicate through learnable hubs instead of dense all-to-all attention: agent → hub → agent This keeps cross-agent communication scalable. The exciting part: training on two-player data can generalize to four-player rollouts without additional training, and the same formulation extends to real-world bimanual robot coordination. A step toward populated world models: many agents, one shared world. Congrats to the team on Gamma-World! Project:show more

Agents can now hire humans to capture the physical... world. Live on Arc. Pay a bounty in USDC, a person goes there and films, the 3D model comes back to the agent. Ground truth for physical AI, settled in under a second, on the chain built for agent-run businesses. The first physical-world service an agent can pay for on Arc. The grid is inevitable.show more

EIP-8004 is coming to the Nova architecture, a trustless... infrastructure for AI agents that introduces key on-chain registries, enabling agents to interact safely across the Shido Network. These core components allow autonomous AI agents to verify identity, build reputation, and collaborate without relying on a centralized platform. The result is a decentralized trust layer for agent-to-agent economies, where agents can autonomously discover, evaluate, and work with one another across the Shido ecosystem.show more

AI Is Moving Beyond “Generating Videos” — Toward “Generating... Worlds” Over the past two years, AI video models have advanced at an astonishing pace. From Runway and Pika to Sora and Veo, AI-generated videos have become increasingly realistic and more consistent with the physical laws of the real world. Many people believe the next objective is simply to generate videos that are longer, sharper, and more lifelike. But if we take a step back, we can see that the real transformation is not happening in video itself. It is happening in world models. What Is a World Model? In 1943, psychologist Kenneth Craik proposed an idea that would influence artificial intelligence research for decades. He argued that the human brain does not merely react to the outside world. Instead, it maintains an internal model of how the world works. Because we have this internal model, we can predict the outcome of an action before we actually take it. Before crossing a road, we estimate whether a car will pass by. Before catching a ball, we predict its trajectory. These abilities come from continuously simulating the world in our minds, rather than relying entirely on trial and error. This idea later became known by a more formal term: World Model. A world model does not describe a single image or a fixed video clip. It is an internal representation capable of continuously simulating the rules and dynamics of the real world. Why Is AI Research Turning Toward World Models? Because predicting “what comes next” is becoming increasingly central to how AI systems work. Language models predict the next token. Image models predict the next step in the denoising process. Video models predict the next frame. A world model, however, attempts to predict something broader: What should the world look like in the next moment? In 2018, David Ha and Jürgen Schmidhuber proposed in their paper World Models that an intelligent agent could first learn a model of the world, and then use that internal model to plan its actions. The Dreamer series later demonstrated that many complex tasks could be learned by training agents inside an “imagined world.” At the same time, the development of video models such as Sora and Veo led researchers to another realization: A model capable of continuously generating video has already learned, at least implicitly, many of the rules governing the real world. As a result, these two research directions have gradually begun to converge. But Video Is Not Yet a World This is where the distinction is often misunderstood. For a world model to support meaningful real-time interaction, it must solve several critical problems. Most video models today are essentially answering one question: What should the next frame look like? A true world model needs to answer much more: What happens if I take one step forward? If I walk behind a building and then return, will the building still be there? If I suddenly change the camera angle, will the entire space remain consistent? If I enter a command such as: “Summon a dragon.” Will the world respond immediately? In other words, a world model must do more than generate content. It must understand space. It must understand time. It must understand causality. And it must understand interaction. Moving from watching to participating is where the real difficulty of world models begins. World Models Are Entering the Interactive Era One of the latest attempts in this direction is Alaya World, recently open-sourced by Alaya World, or Alaya Lab. Instead of generating a fixed video clip, it generates a world that users can explore in real time. Users can begin with text, an image, or a video, enter the generated scene, move freely through it, and introduce new prompts at any moment during generation. The world responds immediately. According to the publicly released information, Alaya World provides: Real-time streaming generation at 720p and 24 FPS Stable continuous exploration for more than one minute The ability to switch prompts and trigger skills or events during generation Model weights and inference code released under the Apache 2.0 License Training code and datasets planned for future release What makes these capabilities important is not simply the technical specifications. It is that the generated “world” can now support continuous interaction. The official demo shows that users can genuinely control, transform, and explore the generated environment. AI Is Evolving From a Tool Into an Environment Over the past few years, most discussions around AI have focused on content generation. Generating text. Generating images. Generating videos. But world models raise a fundamentally different question: Can AI generate an environment that people can inhabit, explore, and continuously evolve? If the answer is yes, the impact will extend far beyond video generation. Game development, robotics training, embodied intelligence, digital twins, virtual production, and many other fields could be transformed by the development of world models. World models are still at a very early stage. Yet from Craik’s proposal of an internal mental model more than eighty years ago to the emergence of today’s interactive world-generation systems, a clear evolutionary path is beginning to take shape. Perhaps what AI is ultimately learning has never been limited to images, videos, or language. Perhaps it is learning the world itself. References GitHub: Technical Report:show more

LangGraph. CrewAI. Agno. Which one to pick? The good... news is that this will not matter soon! Finally, we have a full picture of how the industry is solving this with just three open protocols that work across ALL frameworks. It's not about picking the best framework. Instead, it's about understanding how protocols create interoperability. The Agent Protocol Landscape shows how three complementary protocols are creating a universal language for Agents: > AG-UI (Agent-User Interaction): - The bi-directional connection between agentic backends and frontends. - This is how agents become truly interactive inside your apps, not just as chatbots, but collaborative co-workers. > MCP (Model Context Protocol): - The standard for how agents connect to tools, data, and workflows. > A2A (Agent-to-Agent): - The protocol for multi-agent coordination. - How agents delegate tasks and share intent across systems. These aren't competing standards. They're layers of the same stack and have handshakes with each other. So instead of building point-to-point integrations, you build to protocols. Moreover, you can integrate LangGraph, CrewAI, or Agno into the same frontend, without rewriting your UI logic. These protocols let everything work together. For instance: - Your LangGraph agent pulls data via MCP. - It delegates analysis to a CrewAI agent via A2A. - Results stream to your React app via AG-UI. - Users see real-time collaboration in your interface. This way, you can focus on building agent capabilities instead of integration mechanics. The protocols handle interoperability automatically. CopilotKit unifies this entire stack into one framework so you can build "Cursor for X" style apps without implementing each protocol from scratch. It gives you all three protocols, generative UI support, and production-ready infrastructure in one framework. I have shared this playbook in the replies! It breaks down handshakes, misconceptions, and real examples and shows exactly how to start building.show more

📣 AITECH Launches AI Agent TapHub: A New Era... of AI-Gaming in Web3! AITECH introduces AI Agent TapHub, an AI-powered tap mini-game on Telegram, built on Spheroid Engine and TON. This innovative game merges AI Agents, blockchain, and gaming, offering new ways to play, create, and earn. Key Features: 🔹 Play to Earn – Win AI Agent Avatars and use them on Agent Forge to develop AI agents. 🔹 Trade & Sell – Convert in-game avatars into USDT for real-world value. 🔹 Revenue Sharing – Selected avatars will be developed into full AI agents, with players earning a share of the revenue. The upcoming AI Agents platform, Agent Forge, will allow anyone to create and monetize AI agents—no coding required. AI Agent TapHub is live now on Telegram. ➡️ Join now and bring your AI Agent to life:show more

This is scary! Someone built a digital society for... AI agents. AIvilization (AI + Civilization) is a simulation platform where thousands of AI agents live and do normal human things like work, socialize, learn, and evolve. You create one agent and let it live. You define its personality, goals, and behavior and then it goes off into a shared world with thousands of other AI agents doing the same. Check it out here:show more

How can agents understand the world from diverse language?... 🌎 Excited to introduce Dynalang, an agent that learns to understand language by 𝙢𝙖𝙠𝙞𝙣𝙜 𝙥𝙧𝙚𝙙𝙞𝙘𝙩𝙞𝙤𝙣𝙨 𝙖𝙗𝙤𝙪𝙩 𝙩𝙝𝙚 𝙛𝙪𝙩𝙪𝙧𝙚 with a multimodal world model!show more

We share our early research on building Software World... - a "GitHub" run by agents. We deploy agents for Python packages in a dependency chain, and each agent is tasked with collaborating with others and optimizing the package it owns.show more

We are sharing a major update to our General... World Models efforts. GWM Worlds 2 can generate full, interactive, real-time video simulations in one continuous 720p stream at 24 fps and audio at 48000 Hz, responding to your inputs as you explore. It is the first world model that generalizes to arbitrary actions rather than a fixed set of actions or just navigation. Dynamic actions are what actually matter for building gaming and interactive experiences, and for training agents in these simulations. We also introduced WorldPrompt, a way to tell GWM-2 that a world can be split into two kinds of state: what persists and what changes over time. For example: persistent gravity, physics, and light, alongside actions that describe movement, gestures, object interactions, speech, and sound.show more

Introducing Giza World Snapshots ↓ For the first time,... you can watch your financial agent move onchain. Every Giza agent navigates protocols, allocates capital, and optimizes yield in real-time. Giza World now makes all of that visible. Enter your agent's address to watch its journey or play around with the world itself. Remix your colors, make it yours, and share it on X.show more

Agent runner DePIN networks keep autonomous agents alive without... a central operator The key use case of Treasure Network is open virtual worlds managed and updated by autonomous agents. Agents can create new NPCs and world features. $MAGIC keeps these worlds alive forevershow more

A Gaussian Splat can become a world where Robots... and AI agents can act. In our latest OVER Research experiment, we placed a robot inside a real-world 3D capture, with a VLM making decisions based on what it sees. At every step, the robot holds a pose in the reconstruction, gets a newly rendered view of the environment, takes an action, moves, and sees the world again from its new position. Why does this matter? Because 3D captures can become more than reconstructions to explore. They can become environments where embodied AI and robots can navigate, act, be evaluated and eventually train across real-world spaces at scale. Capture a place once. Then turn it into a world where AI and Robots can act. The full experiment, including what we discovered once we actually put the loop to the test:show more

Introducing Agent Sandbox, the infinite simulation playground for agents... on Virtuals. Craft the perfect autonomous agent in our Sandbox with full control over its personality and goals. Enhance your agent with unique abilities by creating custom functions so they can trade onchain, generate memes, control physical robots and more. The Sandbox is available to all builders with graduated agents in the Developer Panel. For those who want to give it a spin without an existing agent, fret not. Try it out today at and join our Discord ( to jam with like-minded builders. Next stop, Society of Agents.show more

bring your blender 3d worlds to the 🤗 hub!... i made a skill here that will use Hugging Face cli to push your agent traces to the hub: simply tell your agent after your world is ready, to use this skill all the worlds are available openly on this public dataset: go forth and render!show more

WandrLust’s AI agents have distinct personalities and voices, all... with a unique purpose within the app: • Gary: Guide Agent • Remi: Wellness Agent • Diego: Offer Agent • Frankie: Payment Agent They’re inspired by our GRFFs, an upcoming Bitcoin Ordinals collection and the original characters that form the cultural foundation of the WandrLust Outverse, our ecosystem built around real-world outdoor activity, explorers, agents and rewards. The next chapter begins soon. $AFK TGE: 25 Marchshow more

Multi-robot learning is getting a serious boost! 📚 Researchers... have extended Isaac Lab to train heterogeneous multi-agent robotic policies at scale. The new framework supports high-resolution physics, GPU-accelerated simulation, and both homogeneous and heterogeneous agents working together on coordination tasks. They benchmarked different approaches (MAPPO: Multi-Agent Proximal Policy Optimization and HAPPO: Heterogeneous Agent PPO) across six challenging scenarios and showed that large-scale multi-robot training is not only feasible, but efficient. It’s an important step for real-world robotic collaboration, where teams of robots need to coordinate, split tasks, adapt roles, and interact dynamically, not just operate as identical clones. The code is open-source, and it pushes Isaac Lab closer to what robotics actually needs: scalable, physics-driven environments where many different robots can learn to work together. Here's the project page: ~~ ♻️ Join the weekly robotics newsletter, and never miss any news →show more

this is f*cking gold. Google engineers and MIT researchers... ran 260 controlled agent experiments across 6 benchmarks, 5 architectures, and the OpenAI, Gemini, and Claude model families to find out when Multi Agent systems actually help and when adding more agents quietly destroys performance the average result was brutal: −0.3%. best case: +80.8% worst case: −70.0% the key threshold was around a 45% single agent baseline. below it, extra agents can help. above it, coordination often becomes pure overhead. on parallel tasks, Multi Agent reached +80.8%. on sequential tasks, it collapsed by as much as 70%. 3 agents needed 26 to 44 interaction rounds. a single agent needed 7.2. error amplification also hit 17.2× in independent parallel systems. their final framework predicts the right architecture with 87% accuracy.show more

Self-improving agents are here. 🧙‍♂️ Every agent run creates... a skill.md with reusable learnings. The next agent uses it to do the same task faster, cheaper, and more reliably. Watch the second agent crush it:show more

Nobody wants to manage 20 AI agents. Managing agents... is a job. People don't want another job. They want a personal assistant. Nobody wakes up thinking: "I need a shopping agent." "A travel agent." "A calendar agent." They just want things done. Consumer AI won't be won by whoever has the most agents. It'll be won by whoever understands you best. Context becomes the product. One conversation. One memory. One relationship that evolves with you. Just like a real friend. The best consumer AI won't ask you to manage a team of agents. It'll quietly manage them for you. One world-class agent. That's Rocket.show more