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Laude Institute
@LaudeInstitute
Laude Institute backs computer science researchers turning research into real-world impact. // @LaudeVentures
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Before their first release, the @terminalbench team called off a task-writing meetup, figuring there was no way to get more than ten people in a room to talk about the project. Tonight we squeezed ~150 into Laude Lab! The team covered the state of the bench and @harborframework, the process behind Terminal-Bench-Science, and how to keep pace as models get better, faster: continuous benchmarks, real-world evals, and long-horizon multi-agent challenges. @alexgshaw @ryan_marten @StevenDillmann @Mike_A_Merrill @andykonwinski
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A Laude / MIT collaboration we've been cooking. 👀
Introducing Headlong, an open source microharness for persistent agents: self-guided agents that think continuously. Most agent harnesses are reactive: you send a task, the agent completes it, and then it sits frozen until the next request. Cron jobs and heartbeats wake it up to run a checklist and put it back to sleep. A Headlong agent is never asleep. It keeps generating thoughts about whatever it decides is interesting, in a self-guided loop inspired by human inner monologue. Your message doesn't start a session. It's one more observation that lands in the agent's thought stream, and the agent decides if and when to reply. Headlong is built on the idea of persistent agency: continuous inner thought generation between external interactions. The agent sets its own interests and priorities, comes up with its own projects, and sometimes pings you unprompted with progress. To keep our prototype as simple and small as possible, we implemented Headlong as a microharness: a complete agent harness in under 10K lines of Bash, organized as a handful of small executables. It includes a loop that generates the next thought, shellm (a recursive language model written in Bash), a trajectory stored as a DAG of jsonl files, and context as a projection of that trajectory. We've been running one Headlong agent internally at Laude for several weeks. The whole team talks to it over Slack and Telegram, and every conversation lands in its single stream of thought. It works in its own fork of Headlong and we've pulled over 50 of its commits into main. One night, with nobody talking to it, it went back to check whether a recall process it had built was actually wired into its mind, found that it wasn't, diagnosed and fixed the bug, and verified the fix end to end. 48 minutes, no human asked for the fix or was in the loop at any point. Every step is a timestamped line in its log. Things broke too, and we wrote those up. Background thinking costs us $1 to $2 an hour, our agent stopped its own service three times by accident, and self-delegation died on day one. Details in the post. One line installs everything and starts an agent. Use a dedicated sandbox and spend-capped API key; it runs real shell commands and thinks around the clock. Headlong is research software, be careful! curl -fsSL | bash Launch post: Repo: Headlong is a @LaudeInstitute / MIT collaboration.
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We are thrilled to announce the inaugural Stanford Virtual Embryo Challenge at NeurIPS 2026 @StanfordAILab @NeurIPSConf , in collaboration with Laude Institute @LaudeInstitute , UCSD, and Harvard! Join us in building AI models that predict how life takes shape, across space, scale, time, and perturbation: We welcome participants from academia and industry, independent researchers, and even AI agent scientists!!! Our vision is inspired by and complementary to the virtual-cell efforts championed by @arcinstitute . But life is more than a collection of individual cells. Why Virtual Embryos? See below 🧵👇
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Dream team. Congratulations to Laude board member @JeffDean on founding @DiscoLoopAI with longtime collaborators @Sanjay_Ghemawat, @OriolVinyalsML, and @quocleix .
Announcing Discovery Loop! I am very excited to announce that, along with my longtime friends and collaborators @Sanjay_Ghemawat, @OriolVinyalsML and @quocleix, we are founding Discovery Loop (@DiscoLoopAI), a Public Benefit Corporation whose mission is to automate machine learning, science, and engineering to accelerate discoveries and progress. The four of us have worked together for 14 to 30 years, and have helped build some of the world’s most used products, infrastructure and AI models, and we’re excited to turn our attention to this ambitious endeavor. ♾ Learn more at:
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(1/9) I'm thrilled to share the open-source release of Mixture-of-Kittens (MoK), our MoE megakernel for NVL72s! MoK fuses all mixture-of-experts communication and computation into a single, fully deterministic kernel, and powers Composer training across tens of thousands of GPUs. Joint work with @nash_c_brown, @hmwildermuth, @tmwilliamlin168, and @ellev3n11
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The simpler the algorithm, the more willing I am to believe that it's a real breakthrough and not just overfitting to a niche domain or a few datasets (a sort of meta "bias vs variance" tradeoff in action). What got me excited about explorative modeling is that this algorithmic insight (1) is just "add a for loop here" (doesn't get much simpler than that), (2) shows gains across video, image, and text, (3) shows even larger advantage at larger scale, and (4) results in pretty substantial improvements ("X times better", not "X percent better"). Congrats to @AlexiGlad, @hengjinlp, and @du_yilun on exciting results! It's been fun watching this come together -- @LaudeInstitute Slingshot time & resources well spent. With more results to come...
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We discovered a third pretraining axis beyond parameters and data: exploration. Scaling exploration monotonically improves existing models across images/video/language, and unlocks end-to-end generation. In the simplest case, it's just a for loop. Introducing Explorative Modeling. TLDR: - Gains from exploration grow with scale: 7%→36% as data scales, 13%→23% as parameters scale, and gains double at 3× the compute - Adding exploration to ~SOTA baselines improves data efficiency by 6.2×, FLOP efficiency by 4.1×, parameter efficiency by 47%, and hits a near-SOTA 1.43 unguided FID on ImageNet - Exploration lets you trade training compute for generalization, and scales how end-to-end your generative model is - End-to-end Explorative Models (XMs) match diffusion performance on control tasks with up to 256× less inference compute 🧵Thread:
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Andy Konwinski @andykonwinski spoke at CAIS months ago about why open AI infrastructure matters, drawing parallels to the early internet, where a few decisions at key moments shaped everything. The argument then is the argument now.
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Frontier-Bench picks up where Terminal-Bench left off. Why the new name? First, it evaluates capabilities at the frontier beyond agentic coding: finance, music, biology, hardware design, etc. Second, it evolves in lockstep with the frontier.
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We’re releasing Frontier-Bench: a benchmark that measures and evolves with the frontier of agent work. Built by the team behind Terminal-Bench and Harbor, Frontier-Bench is an on-going community effort. Frontier-Bench v0.1 contains 74 tasks on which the best agents score ~34%
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The frontier is continuously moving forward. Our benchmarks should be too. For software projects, we don't throw away the codebase and rewrite it for every major release. So why the hell do we do that with benchmarks? Going forward with Frontier-Bench, we don't!
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A living frontier needs a living benchmark. Congratulations to @alexgshaw, @ryan_marten, and the @terminalbench, @frontierbench, and @harborframework teams on this next era, and to our very first Slingshot for continuing to shape how the field measures progress. 👏
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We’re releasing Frontier-Bench: a benchmark that measures and evolves with the frontier of agent work. Built by the team behind Terminal-Bench and Harbor, Frontier-Bench is an on-going community effort. Frontier-Bench v0.1 contains 74 tasks on which the best agents score ~34%
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The bitter lesson rejects domain-specific structure. Problem decomposition & recombination (what RLMs make first-class) is about as general-purpose of a problem-solving tool as it comes. Reminds me of the march of processor speeds: CPUs got faster every year until ~2004, when clock speeds hit a heat wall around 3 GHz. Transistors kept getting cheaper, but the only way to keep getting gains was to decompose workloads into pieces and go parallel. As the tasks we give LLMs get longer (context length/time horizon) and more complex, scaling up to 100T parameters will probably help...but my money is on big-O-improving innovations like this improving generalization faster.
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Transformers struggle to generalize to tasks they were not explicitly trained on. Instead, we propose in 2026 that it is the job of the harness to generalize through composition. We observe a powerful property when training RLMs: for tasks with shared structure that look different, the root model naturally learns the same trajectory, meaning it views the two task trajectories as the same! In other words, the Transformer does not need additional generalization capabilities to transfer capabilities from one task to the other, the harness induces it. We find that well-designed harnesses form a quotient set over task trajectories, meaning their individual LLM calls can see structurally “similar” tasks as near-identical, token-for-token! Harnesses can effectively generalize for the Transformer during training, without relying on any intrinsic generalization capability from the model. For example, RLMs can see problems of different lengths as the same: we show that RLMs can train exclusively on short tasks, and fully generalize to similar but unseen tasks 8-32x longer because it produces near identical trajectories for both. Taking this further, we show that tasks across different domains (e.g. math solutions vs. essay writing) that share a decomposition strategy exhibit the same generalization effect. RLMs can train on the problem of finding which essays belong to the same author and improve performance on finding math problems that share similar solutions. The full blogpost, experiments, and discussion are in the thread below.
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Andy @andykonwinski and @swyx talked through why AI researchers may be uniquely well-positioned to wield power responsibly right now, why most of them are still thinking about impact only in terms of companies and citations - and how to change that.
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The real risk of AI's talent drain isn't just fewer researchers; it's the collapse of the mentorship loop that produced them. @profjoeyg spoke with @andykonwinski about why spending five years at the hip of one of the world's best researchers is a mechanism closed labs can't replicate.
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At CAIS, @profjoeyg told @andykonwinski he's noticing a shift with grad students: they're less interested in papers or startups, more interested in policy work and shaping what the world looks like when they inherit it. He sees them thinking about how to adapt technology and ideas to actually influence the world around them. He thinks if academia offers that path, they'll stay. If not, they'll go elsewhere.
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independent assessment of capabilities is critical to making the right decisions that shape our future the stakes are high, open-source benchmarks and empiricism has never been so important we need to evolve our practices to develop continuously improving benchmarks
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Dave Patterson beat Intel at chip design despite being outspent by orders of magnitude. He credits it in part to bringing industry partners into the lab every six months to focus on real problems and real feedback. He talked with @andykonwinski about why academia needs to remember how to do that again to lead on AI.
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the companies that figure out how to capture their value propositions into evals and envs will win. the ability to create high quality evaluations is a new type of strategic advantage, and it is more important than access to capital, economies of scale, network effects, or any other traditional competitive advantage
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We’re open sourcing WANDR. WANDR is an internal benchmark we built and used for building deep and wide research capabilities inside Perplexity Computer.