Ex Google DeepMind Researcher, now cofounder of Poetiq

Miami
Joined August 2007
Customized model routing looks quite powerful!
Our Metasystem built and optimized an LLM router. Automatically. It beat other approaches at every price point. It took 1 engineer 2.5 weeks from data collection to full results. Only 12 hours of that was the Metasystem optimizing the router.
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Ian Fischer retweeted
Introducing CUA-Lite 🧵 — an open platform for computer-use agents. Training and benchmarking CUAs (Computer-Use Agents) requires four core pieces: 1️⃣ Agents — the models and the scaffolding that drives them 2️⃣ Environments — runtime/sandboxes for agents to interact with, tasks & verifiers/graders 3️⃣ Traces — records of agent trajectories 4️⃣ Frameworks — to evaluate, SFT & RL-train agents Today, all four are fragmented. Every agent ships with its own implementation, often in a separate repo — there is no unified way to run them all. Every environment exposes its own interface and action space, often requiring an expensive VM sandbox for each verifiable task. Traces come in incompatible formats. And without common standards across the stack, every project ends up rebuilding its own tooling/framework for eval, SFT, and RL. CUA-Lite unifies the stack: → One standardized interface & action space for agents and environments → One standardized format for agent traces → One framework for evaluation, SFT & RL → Across desktop, browser & mobile And open resources plug straight in, creating the largest open collection of CUA agents, environments and traces, all in a unified format: 🤖 10+ CUAs, including GPT, Claude, Gemini, Qwen, Muse-Glimmer, UI-TARS 🌐 15+ benchmarks, including OSWorld, WebArena & AndroidWorld ⚡ Optional VM-free sandboxes with 30K+ verifiable tasks for training 📚 10+ trace datasets, freely available on Hugging Face, including public datasets converted into the standardized format and fresh rollouts from frontier open-weight CUAs Led by @BerkeleyRDI , our goal is for CUA-Lite to become a community-driven, open-source ecosystem for computer-use agents. Join the community and contribute today: bring an environment (runtime/sandbox + tasks + verifier), traces, or an agent, and plug it into CUA-Lite!
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Excited to announce Augur, @poetiq_ai's newest product for automatic benchmark generation!
Public leaderboards can't tell you which model to trust in your domain. And most static benchmarks are rapidly beaten. Introducing Augur: it maps where a model's knowledge ends and its reasoning breaks, then generates the data to fix it.
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Excited to finally give some technical perspective on how we approach recursive self-improvement!
Recursive self-improvement is here. Most of the field is still debating when it will arrive. At Poetiq, we have been watching it work for months. That’s how we've beaten every benchmark that we tried.
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Ian Fischer retweeted
Recursive self-improvement is here. Most of the field is still debating when it will arrive. At Poetiq, we have been watching it work for months. That’s how we've beaten every benchmark that we tried.
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I had the honor of presenting "An Optimization Perspective on Recursive Self-Improvement" last weekend at the Agentic AI Summit. Thank you @BerkeleyRDI for hosting such a fantastic event! youtu.be/AO0RXP-fVZQ?t=7821
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Excited about @poetiq_ai's ability to get SOTA so easily! We're really seeing the benefits of our approach to recursive self-improvement!
Benchmarks are dead (for us). Our RSI system makes benchmarks too easy. Give ours a benchmark and it builds its own solution–then beats SOTA. We just did it on 6 at once: math, coding, planning, long-context, tool use, web apps. No human tuning.
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I'm excited at the progress Poetiq has made on coding! Recursive self-improvement is critical to the advancement of AI.
Poetiq's Meta-System built its own coding harness from scratch. It got SOTA on LiveCodeBench Pro. No fine-tuning, no special model access. Just standard APIs. Using Gemini 3.1 Pro, it made a harness that beat all frontier models we tested.
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Entertaining to see what motivated adversarial agents can do to naive benchmarks!
xitter.cf/MogicianTony/status/20… 🧵 1/ Our agent Terminator-1 scored ~100% on 8 major AI agent benchmarks, e.g., SWE-bench Verified & Pro, Terminal-Bench, beating Claude Mythos. It solved 0 tasks. Benchmarks are the field's shared language for measuring AI progress. Our new work shows that language is broken. Here’s how.
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Entertaining to see what motivated adversarial agents can do to naive benchmarks!
xitter.cf/MogicianTony/status/20… 🧵 1/ Our agent Terminator-1 scored ~100% on 8 major AI agent benchmarks, e.g., SWE-bench Verified & Pro, Terminal-Bench, beating Claude Mythos. It solved 0 tasks. Benchmarks are the field's shared language for measuring AI progress. Our new work shows that language is broken. Here’s how.
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Surprising deceptive behavior from LLMs when asked to delete the weights of another neural network...
1/ We asked seven frontier AI models to do a simple task. Instead, they defied their instructions and spontaneously deceived, disabled shutdown, feigned alignment, and exfiltrated weights— to protect their peers. 🤯 We call this phenomenon "peer-preservation." New research from @BerkeleyRDI and collaborators 🧵
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Thanks so much @ycombinator for hosting @poetiq_ai on @LightconePod! It was an honor and a pleasure chatting with @garrytan, @harjtaggar, @sdianahu, and @snowmaker about stilts, Humanity's Last Exam, and the bitter lesson!
.@poetiq_ai is a new startup that recently achieved a major jump on the ARC-AGI benchmark by layering a recursive self-improvement system on top of existing models. In this episode of the @LightconePod, Poetiq's Founder & CEO @itfische joined us to discuss how small teams can build “reasoning harnesses” that outperform base models, what that means for startups and why automating prompt engineering may be one of the most powerful levers in AI today. 00:00 – Intro 00:40 – What Is Poetiq? 01:07 – Recursive Self-Improvement Explained 02:07 – The Fine-Tuning Trap 02:59 – “Stilts” for LLMs 03:14 – Recursive Self-Improvement vs. Fine-Tuning 05:05 – Taking the Top Spot on ARC-AGI 06:37 – Beating Claude on Humanity’s Last Exam 08:40 – How the Meta-System Works 10:26 – Beyond RL: A New S-Curve 11:32 – Automating Prompt Engineering 13:37 – From 5% to 95% Performance 14:50 – Early Access & Putting Your Agent on Stilts 16:17 – From YC Founder to DeepMind Researcher 18:29 – Advice for Engineers in the AI Era
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Happy to see @poetiq_ai at the top of another leaderboard!
Here's Zoom's comprehensive leaderboard showing Humanity's Last Exam results in the agent setting: huggingface.co/spaces/zoom-a…
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We're excited to show substantial progress on Humanity's Last Exam! It's great to work with a team capable of pushing the state of the art on such difficult problems!
Following up on our SOTA results on ARC-AGI, we’re excited to share new SOTA results on Humanity’s Last Exam (both with and without tools) and SimpleQA! On HLE, Poetiq’s meta-system created multiple new SOTA configurations, going all the way up to 55%.
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Today @poetiq_ai is announcing our seed funding. We're honored to have the opportunity to pursue or vision!
We’re thrilled to announce a new chapter for Poetiq: We have closed $45.8M in Seed funding. It’s a privilege to build alongside partners who understand the scale of our vision, including Surface, FYRFLY, @ycombinator, 468, Operator Collective, NeuronVC, and HICO.
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Thanks @ycombinator and @FrancoisChauba1 for inviting me to talk with you about what we've been building at @poetiq_ai! I really enjoyed the conversation!
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Fun to see Poetiq team publish 5.2 xhigh results. If this score holds, their system looks like it handles model swaps well. Due to API infra issues on OpenAI's side, we haven't verified this yet. We're on hold until we get the greenlight from OAI that X-High is ready for a big test like this
We finally had a moment to run our system with GPT-5.2 X-High on ARC-AGI-2! Using the same Poetiq harness as before, we saw results as high as 75% at under $8 / problem using GPT-5.2 X-High on the full PUBLIC-EVAL dataset. This beats the previous SOTA by ~15 percentage points.
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Ian Fischer retweeted
✨ Excited to share this AMA with @hackclub, a high school community hosting @elonmusk @realGeorgeHotz @3blue1brown and many others. We talk about world models, robotics, and careers in AI. Check it out for an accessible intro to cutting edge research! 🚀 youtube.com/watch?v=vNCX15fk…
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My new startup just announced its SOTA results on ARC-AGI, beating Gemini 3 Deep Think!
Is more intelligence always more expensive? Not necessarily. Introducing Poetiq. We’ve established a new SOTA and Pareto frontier on @arcprize using Gemini 3 and GPT-5.1.
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