Senior Year Undergrad Research on Phase Qubits FINALLY DONE. > derived hamiltonian for a Josephson junction-based phase qubit > mapped it to a spin-1/2 system hamiltonian under magnetic fields > studied the quantum dynamics and evolution of the hamiltonian > derived spin-flip probabilities > explored qubit control via phase shift and applied mag fields for high fidelity

It’s kind of sad that π0.5 is still the only open-source VLA that feels like a consensus go-to after two years. We need more serious open-source efforts in this space. #OpenSource

Shazam for fragrances. Who’s building this?

thank you hopes, I've taken note of this.

More questions are deep learning questions than people realized. We just stopped asking deep learning questions and learned to work around them by tuning hyperparameters.

Machines of Loving Rhys

New episode with @polynoamial We talk about multi-agent, Navier-Stokes, and what the current explosion of maths progress tells us about what happens once you automate AI research. And we also discuss how we will know if the models are actually aligned before we kick off RSI. 0:00:00 – Multi-agent and Navier-Stokes 0:15:28 – How will AI firms work? 0:22:02 – What math progress tells us about recursive self improvement 0:40:22 – Hugging Face and alignment 1:01:18 – The internal/external model gap 1:08:34 – Chain of thought is degrading 1:14:12 – How will we know when alignment is solved?

We're sharing our new framework for tracking, investigating, and disclosing instances of model misalignment at OpenAI. The framework sets criteria and timelines for public disclosure, including when we haven’t yet fully explained or mitigated the behavior. More complex cases may require longer investigation or coordination with third parties. We’ll prioritize examples that reveal new misalignment mechanisms, meaningful changes in known behavior, or findings that challenge assumptions about safety or mitigation. Alongside the framework, we’re publishing six reports on instances of misaligned behavior we’ve observed during the training or evaluation of our models in the last six months. This is a starting point. We’ll refine the process through experience and public feedback, and share more reports on an ongoing basis. openai.com/index/model-misal…

Very interesting

What do you do when your robot actuators don’t have the feature you need? Well, our team found a race condition bug, and turned it into the feature they needed. A small side quest at Enigma 🧵 1/5

The interetsing question is whether domain-specific training actually changes the progress–compute scaling law, or if it mostly just shifts the curve left by improving sample/compute efficiency at a given capability level. If it’s majorly a constant-factor gain rather than a better scaling exponent, then sufficiently strong general models may (and will) eventually eat that advantage through scale. We have seen this earlier, even in robotics (open-x embodiment / RT-X model)

This is awesome from Periodic! But I can’t help but notice Astra here. Neon was explicitly midtrained + RL’d on proprietary lab data for this domain while Astra is a general-purpose model and still gets within ~2pp on FrontierXRD? ~40% higher cost, sure, but basically specialist-level XRD performance without the specialist training? This speaks much more to me wow

"superintelligence might kill everyone but delaying it would be cringe"

Yes, indeed

Machine of Loving Grace (Blackwell)

In light of some new robotics launches, some people can definitely benefit from reading this paper on best practices for policy evaluations: arxiv.org/pdf/2409.09491

Important work

The soft launch of all soft launches is Astra being good at robotics control.

In Dune, unlike in most scifi, the problem with AI wasn't machines rebelling against humans but rather the rise of an all-powerful class of AI technocrats. "Once men turned their thinking over to machines in the hope that this would set them free. But that only permitted other men with machines to enslave them." This could be the real existential risk of AI.

arxiv.org/abs/2401.05566 arxiv.org/abs/2511.18397 arxiv.org/abs/2412.14093