My brain broke when I read this paper. A tiny 7 Million parameter model just beat DeepSeek-R1, Gemini 2.5 pro, and o3-mini at reasoning on both ARG-AGI 1 and ARC-AGI 2. It's called Tiny Recursive Model (TRM) from Samsung. How can a model 10,000x smaller be smarter? Here's how it works: 1. Draft an Initial Answer: Unlike an LLM that writes word-by-word, TRM first generates a quick, complete "draft" of the solution. Think of this as its first rough guess. 2. Create a "Scratchpad": It then creates a separate space for its internal thoughts, a latent reasoning "scratchpad." This is where the real magic happens. 3. Intensely Self-Critique: The model enters an intense inner loop. It compares its draft answer to the original problem and refines its reasoning on the scratchpad over and over (6 times in a row), asking itself, "Does my logic hold up? Where are the errors?" 4. Revise the Answer: After this focused "thinking," it uses the improved logic from its scratchpad to create a brand new, much better draft of the final answer. 5. Repeat until Confident: The entire process, draft, think, revise, is repeated up to 16 times. Each cycle pushes the model closer to a correct, logically sound solution. Why this matters: Business Leaders: This is what algorithmic advantage looks like. While competitors are paying massive inference costs for brute-force scale, a smarter, more efficient model can deliver superior performance for a tiny fraction of the cost. Researchers: This is a major validation for neuro-symbolic ideas. The model's ability to recursively "think" before "acting" demonstrates that architecture, not just scale, can be a primary driver of reasoning ability. Practitioners: SOTA reasoning is no longer gated behind billion-dollar GPU clusters. This paper provides a highly efficient, parameter-light blueprint for building specialized reasoners that can run anywhere. This isn't just scaling down; it's a completely different, more deliberate way of solving problems.

Replenishing the brain’s natural stores of lithium can protect against and even reverse Alzheimer’s disease go.nature.com/4mt8jSz

Above is a bacterial flagellar motor. Below is a quantum computer. Do with this what you want.

Boston-based @lifebiosciences is set to be testing age reversal in humans in the coming months. Stay tuned. Wish them luck🤞

New breakthrough quantum algorithm published in @Nature today: Our Willow chip has achieved the first-ever verifiable quantum advantage. Willow ran the algorithm - which we’ve named Quantum Echoes - 13,000x faster than the best classical algorithm on one of the world's fastest supercomputers. This new algorithm can explain interactions between atoms in a molecule using nuclear magnetic resonance, paving a path towards potential future uses in drug discovery and materials science. And the result is verifiable, meaning its outcome can be repeated by other quantum computers or confirmed by experiments. This breakthrough is a significant step toward the first real-world application of quantum computing, and we're excited to see where it leads.

Another massive computing/AI breakthrough Engineers create artificial neurons that think like real brain cells, big leap toward true AGI Researchers at USC Viterbi School of Engineering have built artificial neurons that physically replicate how real brain cells process electrical and chemical signals, a historic step toward brain like computing and potentially AGI. Powered by a breakthrough device called a diffusive memristor, these neurons use ions instead of electrons to compute, just like the human brain, enabling chips that are orders of magnitude smaller and more energy efficient than today’s silicon processors. The new design, published in Nature Electronics, could revolutionize neuromorphic computing, making AI hardware that doesn’t just simulate thought but actually works like the human brain.

Scientists at the University of Florida have created a breakthrough mRNA cancer vaccine that erased deadly brain tumors in early human trials without chemo or radiation. Tested on four glioblastoma patients, the vaccine reprogrammed their immune systems within 48 hours to attack the tumor. Built from each patient’s own tumor cells and delivered via lipid nanoparticles, it showed success similar to earlier tests in mice and dogs. It is now moving into Phase 1 pediatric trials.

I used ChatGPT to solve an open problem in convex optimization. *Part I* (1/N)

Hello Thermo World.

Holy shit... this might be the next big paradigm shift in AI. 🤯 Tencent + Tsinghua just dropped a paper called Continuous Autoregressive Language Models (CALM) and it basically kills the “next-token” paradigm every LLM is built on. Instead of predicting one token at a time, CALM predicts continuous vectors that represent multiple tokens at once. Meaning: the model doesn’t think “word by word”… it thinks in ideas per step. Here’s why that’s insane 👇 → 4× fewer prediction steps (each vector = ~4 tokens) → 44% less training compute → No discrete vocabulary pure continuous reasoning → New metric (BrierLM) replaces perplexity entirely They even built a new energy-based transformer that learns without softmax no token sampling, no vocab ceiling. It’s like going from speaking Morse code… to streaming full thoughts. If this scales, every LLM today is obsolete.

Meta Superintelligence Labs just made LLMs handle 16x more context and unlocked up to a 31x speedup. 🤯 Their new REFRAG framework rethinks RAG from the ground up to achieve this, all with zero drop in accuracy. Here's how it works: The core problem with long context is simple: making a document 2x longer can make your AI 4x slower. This is because an LLM's attention mechanism is expensive. Its cost and memory usage grow quadratically (N²) with the length of the text. REFRAG sidesteps this. Compress: A small, lightweight encoder first reads the retrieved documents. It compresses every 16-token chunk of text into a single, dense vector called a "chunk embedding," which captures the semantic essence. Shorten: The main LLM is then fed a sequence of these embeddings instead of the raw tokens. The input it has to process is now 16x shorter. Accelerate: Because the input sequence is so short, the quadratic attention calculation is cheaper, and the KV cache (the primary memory hog in LLMs) is smaller. This is what unlocks the 30.85x speedup. Select: To guarantee accuracy, a Reinforcement Learning (RL) policy acts as a quality control supervisor. It identifies the most critical, information-dense chunks and tells the system not to compress them, ensuring key details are preserved. Why this matters: REFRAG makes the promise of large-context RAG a production reality. Business Leaders: This is how you scale AI applications profitably. Deliver more powerful answers to users, analyzing entire reports, not just pages, all while being faster and cheaper. Practitioners: You no longer need to choose between large contexts and reasonable memory budgets. REFRAG lets you have both. It's an architectural win without architectural changes. Researchers: This work shows that co-designing decoding strategies with application-specific data patterns (like RAG's attention sparsity) yields results beyond generic, brute-force solutions.

I have an utterly insane discovery for you. Just found it. I’m writing an article now. I’ll post it soon. Get ready to be mind blown.

Today, we’re announcing Kosmos, our newest AI Scientist, available to use now. Users estimate Kosmos does 6 months of work in a single day. One run can read 1,500 papers and write 42,000 lines of code. At least 79% of its findings are reproducible. Kosmos has made 7 discoveries so far, which we are releasing today, in areas ranging from neuroscience to material science and clinical genetics, in collaboration with our academic beta testers. Three of these discoveries reproduced unpublished findings; four are net new, validated contributions to the scientific literature. AI-accelerated science is here. Our core innovation in Kosmos is the use of a structured, continuously-updated world model. As described in our technical report, Kosmos’ world model allows it to process orders of magnitude more information than could fit into the context of even the longest-context language models, allowing it to synthesize more information and pursue coherent goals over longer time horizons than Robin or any of our other prior agents. In this respect, we believe Kosmos is the most compute-intensive language agent released so far in any field, and by far the most capable AI Scientist available today. The use of a persistent world model also enables single Kosmos trajectories to produce highly complex outputs that require multiple significant logical leaps. As with all of our systems, Kosmos is designed with transparency and verifiability in mind: every conclusion in a Kosmos report can be traced through our platform to the specific lines of code or the specific passages in the scientific literature that inspired it, ensuring that Kosmos’ findings are fully auditable at all times. We are also using this opportunity to announce the launch of Edison Scientific, a new commercial spinout of FutureHouse, which will be focused on commercializing our agents and applying them to automate scientific research in drug discovery and beyond. Edison will be taking over management of the FutureHouse platform, where you can access Kosmos alongside our Literature, Molecules, and Precedent agents (previously Crow, Phoenix, and Owl). Edison will continue to offer free tier usage for casual users and academics, while also offering higher rate limits and additional features for users who need them. You can read more about this spinout on our blog, below. A few important notes if you’re going to try Kosmos. Firstly, Kosmos is different from many other AI tools you might have played with, including our other agents. It is more similar to a Deep Research tool than it is to a chatbot: it takes some time to figure out how to prompt it effectively, and we have tried to include guidelines on this to help (see below). It costs $200/run right now (200 credits per run, and $1/credit), with some free tier usage for academics. This is heavily discounted; people who sign up for Founding Subscriptions now can lock in the $1/credit price indefinitely, but the price ultimately will probably be higher. Again, this is less chatbot and more research tool, something you run on high-value targets as needed. Some caveats are also warranted. Firstly, we find that 80% of Kosmos findings are reproducible, which also means 20% are not -- some things it says will be wrong. Also, Kosmos certainly does produce outputs that are the equivalent to several months of human labor, but it also often goes down rabbit holes or chases statistically significant yet scientifically irrelevant findings. We often run Kosmos multiple times on the same objective in order to sample the various research avenues it can take. There are still a bunch of rough edges on the UI and such, which we are working on. Finally, we are aware that the 6 month figure is much greater than estimates by other AI labs, like METR, about the length of tasks that AI Agents can currently perform. You can read discussion about this in our blog post. Huge congratulations to our team that put this together, led by @ludomitch and @michaelathinks: Angela Yiu, @benjamin0chang, @sidn137, Edwin Melville-Green, Albert Bou, @arvissulovari, Oz Wassie, @jonmlaurent. A particular shout out to @m_skarlinski and his team that rebuilt the platform for this launch, especially Andy Cai @notAndyCai, Richard Magness, Remo Storni, Tyler Nadolski @_tnadolski, Mayk Caldas @maykcaldas, Sam Cox @samcox822 and more. This work would not have been possible without significant contributions from academic collaborators @mathieubourdenx, @EricLandsness, @bdanubius, @physicistnevans, Tonio Buonassisi, @BGomes_1905, Shriya Reddy, @marthafoiani, and @RandallBateman3. We also want to thank our numerous supporters, especially @ericschmidt, who has been a tremendous ally. We will have more to say about our supporters soon!

Boston-based @lifebiosciences is set to be testing age reversal in humans in the coming months. Stay tuned. Wish them luck🤞

Scientists May Have Found a Simple Way to Reverse Aging Eyes | scitechdaily.com/scientists-…

Leukemia Immune Evasion Revealed, Antibody Restores T Cell Attack #Leukemia cells evade immune attack via SLAMF6, and by blocking this protein with a new antibody, researchers restored #Tcell function in AML @lunduniversity #antibodies #immunotherapy hubs.li/Q03MF2KC0

When the XPENG IRON gracefully approaches you, @Tesla_Optimus how will you greet her?

Babraham Institute researchers used a 13-day partial reprogramming method with Yamanaka factors to reset adult skin cells’ molecular clocks by roughly three decades. The rejuvenated fibroblasts produced more collagen and closed lab-grown wounds faster while keeping their original cell identity. Could this breakthrough open the door to future anti-aging therapies? For more content like this, please visit: bit.ly/4le6ecb #science #biotech #aging #innovation #health

At @OpenAI, we believe that AI can accelerate science and drug discovery. An exciting example is our work with @RetroBiosciences, where a custom model designed improved variants of the Nobel-prize winning Yamanaka proteins. Today we published a closer look at the breakthrough. ⬇️

In a groundbreaking advance, researchers at KAIST used an AI tool called BENEIN to identify key genes that drive cancer cell behavior. By silencing just three of them—MYB, HDAC2, and FOXA2—they reprogrammed aggressive colorectal cancer cells to behave like normal intestinal tissue. In mouse models, tumors shrank by 70 percent without radiation, surgery, or chemotherapy. This approach could mark a paradigm shift in cancer care—focused on healing rather than destruction. For more content like this, please visit: bit.ly/4le6ecb #cancerresearch #kaist #genetherapy #aiinmedicine #cancerbreakthrough #medicalinnovation

It would be wild if the Boomers "make it" to the anti-aging singularity after all. There might be guys alive in 2075 who can remember seeing the new 1957 Chevys cruising down Main Street.

Another HUGE AI cancer breakthrough from Google. Google’s New AI - DeepSomatic - detects cancer mutations with unprecedented accuracy, a giant leap toward curing cancer. Cancer happens when the genetic instructions that control cell growth go wrong. DeepSomatic uses machine learning and convolutional neural networks to spot these dangerous mutations in tumor DNA, even in difficult cases where traditional methods fail. It works across all major DNA sequencing platforms and can even identify mutations in cancers it wasn’t trained on. In tests, DeepSomatic outperformed existing tools by a wide margin, finding more real cancer mutations while making fewer mistakes. It’s especially strong at detecting tricky insertions and deletions in the genetic code, a key advantage for precision medicine. AI will solve cancer by 2030

Our TPUs are headed to space!  Inspired by our history of moonshots, from quantum computing to autonomous driving, Project Suncatcher is exploring how we could one day build scalable ML compute systems in space, harnessing more of the sun’s power (which emits more power than 100 trillion times humanity’s total electricity production). Like any moonshot, it’s going to require us to solve a lot of complex engineering challenges. Early research shows our Trillium-generation TPUs (our tensor processing units, purpose-built for AI) survived without damage when tested in a particle accelerator to simulate low-earth orbit levels of radiation. However, significant challenges still remain like thermal management and on-orbit system reliability.  More testing and breakthroughs will be needed as we count down to launch two prototype satellites with @planet by early 2027, our next milestone of many. Excited for us to be a part of all the innovation happening in (this) space!

A hair-growth serum based on plant extracts has noticeable results in less than two months. newscientist.com/article/249…

Epigenetic Switch to Turn Memories On and Off Created Scientists have discovered that memory strength can be directly controlled by switching a single gene’s epigenetic state. Using CRISPR-based tools, researchers targeted the Arc gene in memory-storing engram cells, turning its activity up or down like a dimmer switch. Silencing the gene erased learned associations, while activating it strengthened them—and both effects were fully reversible. The study reveals a direct molecular mechanism for tuning memory and opens the door to potential treatments for PTSD and memory decline.

MASSIVE claim in this paper. AI Architectural breakthroughs can be scaled computationally, transforming research progress from a human-limited to a computation-scalable process. So it turns architecture discovery into a compute‑bound process, opening a path to self‑accelerating model evolution without waiting for human intuition. The paper shows that an all‑AI research loop can invent novel model architectures faster than humans, and the authors prove it by uncovering 106 record‑setting linear‑attention designs that outshine human baselines. Right now, most architecture search tools only fine‑tune blocks that people already proposed, so progress crawls at the pace of human trial‑and‑error. 🧩 Why we needed a fresh approach Human researchers tire quickly, and their search space is narrow. As model families multiply, deciding which tweak matters becomes guesswork, so whole research agendas stall while hardware idles. 🤖 Meet ASI‑ARCH, the self‑driving lab The team wired together three LLM‑based roles. A “Researcher” dreams up code, an “Engineer” trains and debugs it, and an “Analyst” mines the results for patterns, feeding insights back to the next round. A memory store keeps every motivation, code diff, and metric so the agents never repeat themselves. 📈 Across 1,773 experiments and 20,000 GPU hours, a straight line emerged between compute spent and new SOTA hits. Add hardware, and the system keeps finding winners without extra coffee or conferences.