Zurich, Switzerland
Joined March 2025
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Robert Müller retweeted
Today we give Superintelligence back to its owners.
Introducing Skill2Env 🎨 , the most aligned and diverse dataset to fuel modern Agentic RL . 🧵
Robert Müller retweeted
We’re open-sourcing Pro and Flash, MiMo-V2.6-Distill-Qwen-9B, the technical report, 7K+ RL task environments, an end-to-end RL framework and composable mini-harnesses.
Reproduce, verify and build on the work.
Robert Müller retweeted
We are launching Aionic Labs (@aionic_labs). 🔥🚀
With support from SPRIN-D’s Next Frontier AI initiative, we are building a frontier AI company at the heart of Europe’s leading AI ecosystem.
Clinicians decide when a patient needs intervention. Factory teams catch machine problems before production stops. Operators determine which alarms demand action.
These are some of the most consequential decisions in the physical economy, and they all depend on time-series data.
We are developing Time-Series Language Models (TSLMs) that can understand these signals, reason over them, predict what comes next, and suggest actions in natural language.
TSLMs connect sensor data with language experts use to make critical decisions.
Europe is the right place to build this technology. We have world-class research, top technical talent, and decades of industrial expertise and data.
Our team brings together former founders, frontier tech experience, and world-class researchers from Stanford, Harvard, ETH Agentic Systems Lab (@ETH_agent_lab), ETH AI Center (@ETH_AI_Center) and CDTM (@cdtm_munich). We have built companies, developed technology at scale, and advanced research at the frontier of AI.
Over the coming months, we will:
→ Launch selected TSLM pilots across manufacturing, healthcare, energy, logistics, telecom, insurance, and banking.
→ Build an open research community around OpenTSLM (@OpenTSLM) and TimeNet.
→ Enable trusted data partnerships across industries.
→ Accelerate the Time-Series AI community in Zurich and Europe.
From Zurich. For Europe. 🇨🇭🇪🇺
Join our mission and help build a Frontier AI Lab from day one. Career and pilot program links are in the comments.
Managing Directors: Robert Jakob (@robertjakob), Thomas Kaar (@thomaskaar)
Supported by: SPRIN-D (@SPRIND)
Photo: Felix Adler für SPRIND
All this AI and we're still finding sublets in group chats like it's 2009.
So @_balsam0 and i built clawby.
Text it what you want, it finds your match and intros you. Both sides say yes before any details are shared.
live and free: clawby.chat/ 🦀
Robert Müller retweeted
Excited to share that members of our lab co-authored 6 papers accepted at #ICML2026, including three Main Track and three Workshop papers 🔥🚀
📄 Accepted papers:
▪️ OpenTSLM: Time-Series Language Models for Reasoning over Multivariate Medical Text- and Time-Series Data [Main Track]
▪️ Auditing Emotion-Vector-Steered Political Bias in Open-Weight LLMs [AI4GOOD Workshop]
▪️ Reinforcement Learning of Karma Bidding Strategies [NExT-Game Workshop]
▪️ Cinematic Source Separation with Dialogue-Driven Sidechain Ducking [Workshop on Machine Learning for Audio]
▪️ Reinforcement Learning for Tool-Calling Agents in Fast Healthcare Interoperability Resources (FHIR) [Main Track]
▪️ Preserving Plasticity in Continual Learning via Dynamical Isometry [Main Track]
@robertjakob @PatrickLanger20 @gaborhollbeck @f14wn @DerRiehl @atoof_sh @deepqlearning @kev_osull @cs06thegreat @nzuma0 @maxrosenblattl
Huge congratulations to everyone involved. We are looking forward to presenting these works, reconnecting with colleagues, and meeting new friends in Seoul 🇰🇷
🔗 Full links in the comments.
The lesson learned is that agent evaluation needs environments where capabilities compose under pressure.
Memory alone is not enough.
Math alone is not enough.
Tool use alone is not enough.
The question is whether the policy remains coherent when the world pushes back.
Static benchmarks measure what a model knows.
Interactive benchmarks measure what a model keeps doing when its own actions change the state of the world.
Cattle Trade is intended to be a first step in that direction.
Paper: arxiv.org/abs/2605.14537
@ETH_agent_lab @robertjakob
