Klara Kaleb (@klarakaleb) header
Klara Kaleb (@klarakaleb) profile picture

@Agents4Academia · postdoc @OxCSML · LLMs & agents for science + nature recovery 🌿 · prev. @TheTeamAtX @ClopathLab @ucl

London | Oxford
Joined February 2017
Photo by Klara Kaleb (@klarakaleb)Photo by Klara Kaleb (@klarakaleb)Photo by Klara Kaleb (@klarakaleb)Photo by Klara Kaleb (@klarakaleb)Photo by Klara Kaleb (@klarakaleb)Photo by Klara Kaleb (@klarakaleb)Photo by Klara Kaleb (@klarakaleb)Photo by Klara Kaleb (@klarakaleb)
Klara Kaleb retweeted
We're hiring in London and California! Fun team to be in, too! Consider applying if you would like working across the pre-training stack!
The Gemini Large Scale Pretrain team is hiring a Research Engineer in London or MTV! The team works across the pretrain stack: research codebases, distributed training infra, data pipelines, tooling for training dynamics analysis, and most importantly using Gemini to accelerate our work! 🚀 If you're an engineer who has a strong "whatever it takes" attitude and is not afraid of spending tokens, apply here with some showcase work: google.com/about/careers/app… No particular qualifications needed, and we welcome early career applicants. Join if you want me to bug you with refactors to enable [new AGI compute multiplier]! 👀
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Klara Kaleb retweeted
I am happy to announce an opening for a student researcher on my team at GDM for 2027, based in London - for a project at the frontier of multi-agent coordination, in duration of 3 months with a possibility of an extension.
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AI agents could make organizations far more productive, but leave people more isolated and less autonomous. We've teamed up with @inherent_labs on an essay prize exploring how organizations can harness AI while strengthening human autonomy and fulfillment. We're thrilled that @lugaricano, @gleech, @divyasiddarth, and Chris Summerfield are joining us as judges. blog.cosmos-institute.org/p/…
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Klara Kaleb retweeted
how much can models remember? there’s a neat perspective on neural networks that deserves to be more widely known here’s how you could have invented every layer of the transformer from memory (1/n) from my new blog post!
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Replying to @aiamblichus
Only one way through of this. Keep creating interesting artifacts and share it with the world. Doesn’t matter if it’s called papers tweets services repos models or anything in between. Offer your findings in abundance and minimize friction to reproduce and build on.
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Today, we’re expanding @SnorkelAI’s Open Benchmarks Grants by 10x to a $30M commitment: - Funding a more diverse, robust, and continuously-updated ecosystem of open benchmarks - Launching the Open Benchmarks Red Team to continually test and strengthen them - Introducing the Snorkel Research Fellowship to support independent contributors developing new evaluation methods Since our launch earlier this year, we’ve been humbled to partner with the teams behind Terminal-Bench, TB-Science, ARC-AGI-3, OSWorld 2.0, Agents’ Last Exam, Continual Learning Bench, Senior SWE-Bench, SlopCodeBench, and more. Alongside these teams, we’ve helped design evaluation methodologies, develop task construction pipelines, and scale quality control. OBG-funded benchmarks have appeared on the latest model cards from every major frontier lab; have helped to index and measure frontier progress and alignment; and have contributed to guiding the frontier of AI development. Benchmarks have always been guideposts for—and drivers of— AI progress: setting a metric and then “hillclimbing” against it is the core of how AI works! But when benchmarks fall behind frontier capabilities, our ability to evaluate and align these systems does as well. They become too simple, static, or correlated, and the risk of “benchmaxxing” increases. We need more diverse, robust, and continuously-updated benchmarks, developed by a broader ecosystem of researchers and domain leaders. We’re incredibly excited to accelerate frontier evaluation with Open Benchmarks Grants— raising the ambition and investment in robust, open, and independent measurement. Read our full announcement and apply here: benchmarks.snorkel.ai/fronti…
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Cell modelling at this scale was a pipe dream during my Biomed BSc at @ucl — and that wasn't so long ago. Love to see it!!
A universal predictive model of the cell would dramatically accelerate science, allowing biologists to perform experiments digitally. Today we’re announcing a major partnership with the DOE and NIH. The goal of this partnership is to generate the data that will be needed to build an accurate model of the cell with artificial intelligence. Isomorphic, Google DeepMind, and Meta are joining us as founding partners in the Virtual Biology Initiative. This is the beginning of a large scale coordinated effort to map cellular biology to power the development of digital models of life. Other leading scientific institutions and consortia including the Allen Institute, Broad Institute, Gladstone Institutes, the Human Cell Atlas, the Human Protein Atlas, the Wellcome Sanger Institute, as well as NVIDIA, are working together as part of this international scientific project. Creating a predictive model of the cell is one of the most important challenges for the next decade of science. The insights that come from this could unlock a far greater understanding of disease, and open new paths for cures. We invite the worldwide scientific community and other funders to join us in this effort.
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Klara Kaleb retweeted
A universal predictive model of the cell would dramatically accelerate science, allowing biologists to perform experiments digitally. Today we’re announcing a major partnership with the DOE and NIH. The goal of this partnership is to generate the data that will be needed to build an accurate model of the cell with artificial intelligence. Isomorphic, Google DeepMind, and Meta are joining us as founding partners in the Virtual Biology Initiative. This is the beginning of a large scale coordinated effort to map cellular biology to power the development of digital models of life. Other leading scientific institutions and consortia including the Allen Institute, Broad Institute, Gladstone Institutes, the Human Cell Atlas, the Human Protein Atlas, the Wellcome Sanger Institute, as well as NVIDIA, are working together as part of this international scientific project. Creating a predictive model of the cell is one of the most important challenges for the next decade of science. The insights that come from this could unlock a far greater understanding of disease, and open new paths for cures. We invite the worldwide scientific community and other funders to join us in this effort.
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My dream had always been to prove beautiful theorems as the AI agents unloaded my dishwasher. Well, it went the wrong way... 😂
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I’m delighted to open applications for another year of fully funded PhD positions in Machine Learning in my group, for a 2027 start. We’re looking for outstanding ML students who want to build AI that changes the world for the better. vanderschaar-lab.com/join-th…
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Klara Kaleb retweeted
Replying to @ShahabBakht
I feel like the progress of AI is actually making systems neuroscience more exciting. It's not like current AI has a particularly good angle on physical intuition, common sense, world models, planning/reasoning, motivations/drives, emotions, etc. We may now have the tools to implement and test cognitive architectures.
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All my old papers with "agent" in the title are getting cited a lot more than others (even tho others are also about language agents). Guess AI lit search also just looks at titles. The era of Agent Search Optimization is here
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Stop anthropomorphizing computer programs while dehumanizing actual people challenge
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If you genuinely want to improve AI's public perception: - Actually build systems that meaningfully improve public services, health, education and access to justice instead of just talking about it. - Get your effing security hazard agents under control. - Stop boasting about taking people's jobs. - Acknowledge the industry-scale copyright violations that have taken place. - Enable pluralistic alignment of your models. The values of silicon valley are not universal.
🤦‍♂️How did I miss this? 🤦‍♂️ Here I was thinking that the attempted rename “Super Intelligence” was a loyalty test to see who would suck up to Trump the most. But it turns out, per @axios, that it was simultaneously also a rebrand, necessary because the greedy, tone deaf AI CEOs tarnished their own brand so much. Good luck with that. My guess is that the stench will linger.
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in case anyone’s wondering how this email finds me: toddler in my lap, glasses in her possession. please direct any typos to my co-author 👶🤓
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Klara Kaleb retweeted
📣 Call for Scientists! We're preparing Connect AI-Sci, a proposed ICLR 2027 workshop connecting domain scientists and AI researchers. We welcome researchers in biology, physics, chemistry, materials science, mathematics, medicine, engineering, and related fields in one or both roles: - Breakout participant (in person): Bring a scientific challenge and collaborate with AI researchers on a research plan and a feasible first experiment. - Scientific reviewer (remote): Join our Program Committee to review submissions in your field. No workshop attendance required. No prior AI research experience needed, and all perspectives welcome! 📝 Interest form: forms.gle/yU8e5sqjF58LfbnGA 🌐 Workshop website: open-galapagos.github.io/con… 👥 Organizers: @seungonekim, @jinheonbaek, @dilarafsoylu, @AdaFang_, @RuiMeng_, @ma_chang_nlp
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Klara Kaleb retweeted
Idk who needs to hear this but being kind, chalant, and relentlessly curious is the real moat
One of the worst forms of brainrot AI has cultivated is pessimism about basic research, ie the idea that important work can only happen inside a frontier/neo lab and only with 10k+ GPUs, so the rest should not even bother. What a bleak way to think about science. And it's false.
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Co-signed. The era of high-frequency research. The following are common myths: 1. Humans are not needed to do research 2. Cost is so high that only frontier labs can do research. 3. Somehow ai based research is bad for research communities The most useful way to think of ai is **accelerating the timescale of research** It is similar to the transition from low-frequency research to high-frequency research akin to what happened in trading. What took years now takes days. Once it’s known that Euler has blowup, in the next week it was shown that Navier Stokes has blowup. The fluid science is nowhere near done, now, we need to figure out how to get to next steps: forcing removal for example. Compressing years to days and hours should be the goal. The @__alpoge__ and @DimitrisPapail of the world, who do fundamental work but are agent native, terminally online and collaborative will do very well. We need to build tools and technologies to help more scientists make this transition soon. We also need postAGI scientific institutions that operate at this high frequency: systems of collaboration, credit and funding need to adapt to this speed of light. It’s an extraordinarily exciting time. We should collaborate to solve humanity’s hardest problems at the speed of light.
One of the worst forms of brainrot AI has cultivated is pessimism about basic research, ie the idea that important work can only happen inside a frontier/neo lab and only with 10k+ GPUs, so the rest should not even bother. What a bleak way to think about science. And it's false.
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Workshop Spotlight #2 | AIAS+ 2026 What if AI did not just analyze the brain, but proposed the next experiment? That is the question driving a new AIAS+ 2026 workshop: Discoverative AI for Neuroscience, exploring AI systems that form hypotheses, design experiments, and refine their own understanding of neural systems, moving beyond prediction toward genuine scientific discovery. Convened by @dongsong (@USC ), @song_dongjin (@UConn ) and Lu Chen (@Stanford ), the full-day session pairs invited talks with a closing panel on where discoverative AI goes next. Register now: aiasplus.org Friday, November 6, 2026, at AIAS+ 2026 in San Francisco. One-day passes from $150, standard rate ends October 15. Register: aiasplus.org #AIASPlusWorkshops #DiscoverativeAI #NeuroAI
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Klara Kaleb retweeted
You are the average of the five AI models you spend the most time with. Choose them wisely.
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