Matthew Jörke

I’m excited to share that Bloom, where we ran a four week study on LLM health coaching, just won a Best Paper Award at CHI! 🏆 Paper: arxiv.org/abs/2510.05449 Website: stanfordhci.github.io/Bloom Interest form: forms.gle/JzEHgpLarJ6qc7… Come see my talk! programs.sigchi.org/chi/2026/progr… [1/11]

Can a language model learn, end-to-end, what to keep in its own KV cache and what to throw away? Can it learn to forget while it learns to reason? Deep learning's central lesson: capability emerges from end-to-end optimization, not heuristics/strong inductive biases. But for efficiency, we rely heavily on hand-designed approaches. 🗑️ Introducing Neural Garbage Collection (NGC): we train a language model to jointly reason and manage its own KV cache, using reinforcement learning with outcome-based task reward alone. No SFT, no proxy objectives, no summarization in natural language. New paper with @jubayer_hamid, Emily Fox, and @noahdgoodman!

@FerryLee_AIPOCH The app integrates with Apple's HealthKit API so while we used Apple Watches in our study, the platform itself is not tied to any particular wearable! If your wearable/smart device can read/write to HealthKit, Bloom can read that data too

@mjoerke Love the UI addition, it’s a great solution. At least now we don’t have to keep telling AI “check the context, I already told you that” 😅 Any plans to connect with other devices too, like smartwatches or smart scales?

I’m excited to share that Bloom, where we ran a four week study on LLM health coaching, just won a Best Paper Award at CHI! 🏆 Paper: arxiv.org/abs/2510.05449 Website: stanfordhci.github.io/Bloom Interest form: forms.gle/JzEHgpLarJ6qc7… Come see my talk! programs.sigchi.org/chi/2026/progr… [1/11]

If you’re at CHI, come see our presentation! programs.sigchi.org/chi/2026/progr… And if you’re interested in chatting more, drop me a line at joerke@stanford.edu. We’d love to hear from you! [11/11]

We’re actively working on releasing Bloom to the public. If you’d like to try it out, please fill out our interest form: forms.gle/JzEHgpLarJ6qc7… If you’re interested in building on Bloom, our code is open source github.com/StanfordHCI/Bl… [10/11]

✨I'm on the research scientist and postdoc job market! I'll be graduating from my PhD this academic year with a thesis that focuses on reinforcement learning and healthcare. ✨

@mbodhisattwa @oshaikh13 Thanks for sharing! Looking forward to reading this :)

@oshaikh13 We tried to look into this in our recent work: arxiv.org/pdf/2403.15737 Might be interesting! :)

Fire paper that grounds LLM generations in how experts ACTUALLY do motivational interviewing

Many thanks to my co-authors @sapkotashardul_ , Lyndsea Warkenthien, Niklas Vainio, @PSchmiedmayer, @EmmaBrunskill, @landay!!

In a counterfactual comparison to vanilla GPT4, GPTCoach is more consistent with motivational interviewing, asking more open-ended questions and giving advice with permission.

In a user study with 16 participants, we find that GPTCoach can adhere to motivational interviewing principles and contextualize a user's wearable data to their unique circumstances. Participants also appreciated its supportive and non-judgmental tone.

We built GPTCoach, a GPT4-based chatbot that implements an evidence-based health coaching program, uses counseling strategies from motivational interviewing, and can query and visualize a user’s health data from a wearable through tool use.

Through formative interviews with 22 participants, we learned that *all* health experts adopted a facilitative approach that did not give unsolicited advice. Notably, this contrasts with how current LLMs are trained to answer questions and give advice.

I'm excited to share our new paper exploring how LLM chatbots can be used to motivate physical activity behavior change! Check out our pre-print on arXiv: arxiv.org/abs/2405.06061