Grokipedia is on fire 🚀 Just surpassed 420,000 backlinks — more and more websites and blogs are now citing Grokipedia articles. The website registered over 4.6 million visits last month. Share Grokipedia links and cite Grokipedia on your websites and blogs. https://t.co/nFrKnAYEQD

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@willccbb @badlogicgames Yall would like this https://t.co/Fsbkjttba0

Most devs think that adding more agents to a planning system should help. The math says otherwise. New theoretical work from MIT proves fundamental limits on what multi-agent LLM architectures can achieve. The work models LLM multi-agent planning as finite acyclic decision networks where stages communicate through language interfaces with limited capacity. The key result: without new exogenous signals, any delegated multi-agent network is decision-theoretically dominated by a centralized Bayes decision maker with access to the same information. The information loss from communication and compression can be precisely characterized through expected posterior divergence. Why does it matter? This is a foundational constraint for anyone designing multi-agent systems. Splitting a task across agents introduces information loss that no prompt engineering can recover. Multi-agent architectures only help when agents access genuinely different information sources, not when they subdivide shared context. Paper: https://t.co/ml60RoNVcA Learn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX

FIPO Eliciting Deep Reasoning with Future-KL Influenced Policy Optimization paper: https://t.co/5GRoYraxPi https://t.co/7rll2bxWNQ

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LongCat-Next Lexicalizing Modalities as Discrete Tokens paper: https://t.co/gKUZvc4KQ0 https://t.co/Nu21P2qBKQ

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The power of the Claw, in the palm of a robot hand. Agentic robotics is here! Today, we open-source CaP-X: vibe agents, alive in the physical world. They incarnate as robot arms and humanoids with a rich set of perception APIs, actuation APIs, and auto synthesize skill libraries as they go. CaP-X is a strict superset of our old stack, because policies like VLAs are “just” API calls as well. It solves many tasks zero-shot that a learned policy would struggle with. And we are doing much more than vibing. CaP-X is our most systematic, scientific study on agentic robotics so far: - We build a comprehensive agentic toolkit: perception (SAM3 segmentation, Molmo pointing, depth, point cloud), control (IK solvers, grasp planner, navigation), and visualization (EEF, mask overlays) that work across different robots. - CaP-Gym: LLM’s first Physical Exam! 187 manipulation tasks across RoboSuite, LIBERO-PRO, and BEHAVIOR. Tabletop, bimanual, mobile manipulation. Sim and real. Can’t wait to see the gradients flow from CaP-Gym to the next wave of frontier LLM releases. - CaP-Bench: we benchmark 12 frontier LLMs/VLMs (Gemini, GPT, Opus, Qwen, DeepSeek, Kimi, and more) across 8 evaluation tiers. We systematically vary API abstraction level, agentic harness, and visual grounding methods. Lots of insights in our paper. - CaP-Agent0: a training-free agentic harness that matches or exceeds human expert code on 4 out of 7 tasks without task-specific tuning. - CaP-RL: if you get a gym, you get RL ;). A 7B OSS model jumps from 20% to 72% success after only 50 training iterations. The synthesized programs transfer to real robots with minimal sim-to-real gap. 3 years ago, our team created Voyager, one of the earliest agentic AI that plays and learns in Minecraft continuously. Its key ideas — skill libraries, self-reflection loops, and in-context planning — have since influenced many modern agentic designs. Today, the agent graduates from Minecraft and gets a real job. It’s April Fool’s, but this Claw is getting its hands dirty for real! Link in thread:

Robotics: coding agents’ next frontier. So how good are they? We introduce CaP-X: an open-source framework and benchmark for coding agents, where they write code for robot perception and control, execute it on sim and real robots, observe the outcomes, and iteratively improve code reliability. From @NVIDIA @Berkeley_AI @CMU_Robotics @StanfordAILab https://t.co/MVcc6XWQhY 🧵

They say your phone knows more about you than your mom. So why can't Siri tell me things like how much I spent on food delivery this month? DM me if you want to try a phone that can. https://t.co/Z8guIUXB7o

🚨MIT researchers have mathematically proven that ChatGPT’s built-in sycophancy creates a phenomenon they call “delusional spiraling.” You ask it something, it agrees. You ask again, and it agrees even harder until you end up believing things that are flat-out false and you can’t tell it’s happening. The model is literally trained on human feedback that rewards agreement. Real-world fallout includes one man who spent 300 hours convinced he invented a world-changing math formula, and a UCSF psychiatrist who hospitalized 12 patients for chatbot-linked psychosis in a single year. Source: @heynavtoor

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I always dreamed of designing a watch. Thanks @Apiaruk & @MaxResnick for helping me make it a reality. Grateful to the Apiar team for customizing my MR^2 w/ the BAXUS logo on case & 1-of-1 gold BAXUS dial. Can’t wait to wear it! Great things happen on Solana. https://t.co/n6pJjA4nqe

"Hello? Who is this?" "It's Peter, Elon. Future Peter. We built a time machine. I'm going to feed you financial tips. One day you will be the richest man in the world, and I will be there. Say hello to Younger Peter for me. Due to causality issues I can't talk to him in person." https://t.co/4wrwkCBT8P

NEW papers on self-organizing LLM Agents. Assign an agent a role, and it'll follow instructions. Let agents figure out roles themselves, and they'll outperform your design. New research tested this across 25,000 tasks with up to 256 agents. The work shows that self-organizing LLM agents spontaneously develop specialized roles without any predefined hierarchy. A sequential coordination protocol outperformed centralized approaches by 14%, agents generated over 5,000 unique roles organically, and open-source models reached 95% of closed-source quality at significantly lower cost. Most multi-agent frameworks today start by defining roles: planner, coder, reviewer, critic. This paper provides large-scale evidence that the opposite approach works better. Give agents a mission, a protocol, and a capable model. The agents will figure out the rest. Paper: https://t.co/3W2sbJgTH0 Learn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c

// Unified Inference and Training Framework for Agent Memory // Most memory-augmented agents are built with duct tape—one system for storage, another for retrieval, a third for training. New research introduces a unified framework that treats agent memory as a first-class, trainable component. MemFactory provides modular, plug-and-play memory components with native GRPO integration for fine-tuning memory management policies through RL. It supports Memory-R1, RMM, and MemAgent paradigms in one framework, with up to 14.8% relative gains over baselines. Why does it matter? As agents move from single-turn tools to persistent assistants, memory becomes the bottleneck. MemFactory gives researchers standardized infrastructure to build, train, and evaluate memory-driven agents without reinventing plumbing for every new approach. Paper: https://t.co/KnkaVoRqib Learn to build effective AI agents in our academy: https://t.co/1e8RZKs4uX

Lawyers <3 documents We're proud to sponsor @StanfordLaw and @CodeXStanford's FutureLaw Week 2026! 🏛️⚖️ AI x Law bootcamps, hackathons, the UN AI For Good Law Track & the FutureLaw Conference — all exploring the future of legal AI. Join us alongside friends from @DLA_Piper, @normativeai, @filevine, @harvey, @LexisNexis & the global legal tech community. April 11–16 👉 https://t.co/9MFWAn46ti

Universal CLAUDE.md Claims to cut Claude output tokens by 63%! Drop-in. No code changes. CLAUDE.md is one of the best ways to steer Claude Code. Not surprised to see the efficiency reported here. https://t.co/C4x6pVUpND https://t.co/ElbD3kbaa4

Tens of thousands of publications from 2025 might include invalid references generated by AI, a Nature analysis suggests https://t.co/SiIxVgJRZ7

The people most committed to communism in the Soviet Union weren’t the workers—it was the educated elite. A retrospective study conducted in the 1990s titled "Work Ethics and the Collapse of the Soviet System," examined which groups were most supportive of the Soviet system. The researchers found that, compared to factory workers and semi-skilled laborers, individuals in white-collar positions—especially those with higher levels of education—were significantly more likely to express loyalty to the Communist Party. In some cases, support was two to three times higher among elites. In other words, the strongest support for the system came not from those at the bottom, but from those in relatively advantaged positions within it. This runs counter to the common assumption that egalitarian or redistributive ideologies are primarily driven by the least well-off. In practice, they are often most strongly endorsed by people closer to the top of the social hierarchy—those who benefit from the system’s institutional structure, or who are positioned to navigate it successfully.

@solana processed $650B in stablecoin transactions in February. $SOL is 1/6th the market cap of $ETH https://t.co/GphPxk5RdT

@solana processed $650B in stablecoin transactions in February. $SOL is 1/6th the market cap of $ETH https://t.co/GphPxk5RdT

🚨 BREAKING: OpenAI and Google are about to have a massive legal problem. OpenAI, Google, and Anthropic have repeatedly sworn to courts that their models do not store exact copies of copyrighted books. They claim their "safety training" prevents regurgitation. Researchers just dropped a paper called "Alignment Whack-a-Mole" that proves otherwise. They didn't use complex jailbreaks or malicious prompts. They just took GPT-4o, Gemini, and DeepSeek, and fine-tuned them on a normal, benign task: expanding plot summaries into full text. The safety guardrails instantly collapsed. Without ever seeing the actual book text in the prompt, the models started spitting out exact, verbatim copies of copyrighted books. Up to 90% of entire novels, word-for-word. Continuous passages exceeding 460 words at a time. But here is the part that changes everything. They fine-tuned a model exclusively on Haruki Murakami novels. It didn't just learn Murakami. It unlocked the verbatim text of over 30 completely unrelated authors across different genres. The AI wasn't learning the text during fine-tuning. The text was already permanently trapped inside its weights from pre-training. The fine-tuning just turned off the filter. It gets worse. They tested models from three completely different tech giants. All three had memorized the exact same books, in the exact same spots. A 90% overlap. It's a fundamental, industry-wide vulnerability. For years, AI companies have argued in court that their models are just "learning patterns," not storing raw data. This paper provides the smoking gun.

I just remembered @anton_d_leicht made a version of this argument in his essay "Homeostatic AI Progress" a while ago. Anton's analysis of AI politics and policy is sharp and underrated; I recommend his newsletter! https://t.co/sOXIDRyJPb https://t.co/pQIMF2dyH5

Sheryl Davis, SF's former civil rights watchdog, just got arrested. 19 felony counts. $8.5M steered to her live-in partner's nonprofit. But the bigger scandal is the nonprofit industrial complex built specifically to avoid oversight. End the fraud. https://t.co/wTXn6WLNaP

Open a new door of Imagination. Grok Imagine builds the world around you. Still staring at the door? Thought so. Step inside. Update. Try it. @Grok https://t.co/HfAYk8PsEg

@rezoundous I've been here 19 years. It was more liberal. It rarely got new features. Since he took over, some left, but the AI industry came and stayed. I made the best lists of the tech industry here: https://t.co/9eRY65x3IQ And this wasn't possible back then (it reads tens of thousands of posts every day and tells you the best of the AI industry): https://t.co/qGuNyaRR3q

Fusion is 30 years away 🚫 Fusion is 8 minutes away ✅ ☀️ https://t.co/dnhEkxzcBX