Introducing Voxel, a droid butler made by Tuesday Labs.

What to remember in the past to act—spatial, temporal or motion details? We often assume what’s useful, but that’s hard to know in advance. Meet IMPRINT 🧠: retain rich memory first, then learn to use it for control. Compact latents keep inference fast as history grows. 🧵 1/10

There’s lots of buzz around agentic harnesses for robots, particularly for long horizon tasks that require complex reasoning and memory. But what will it really take to turn a reasoning agent into a reactive, reliable and low-latency robot policy? In our new paper, workspace models, we design a new memory architecture that acts as a latent harness for stronger reasoning models. Here’s why we think this might be the way forward: (1/9)

Anthropic treats attacker access to open models as decisive while ignoring defender access to the same models. That is not a complete offense-defense analysis. Open weights also scale detection, hardening, attribution, and coordinated response. A coalition is the stable game-theoretic equilibrium which defends against such attacks, and is *enabled* by open models. Instead, they seem fixated on the single-step use case of an attacker removing guardrails from an open-weights model, but they seem to forget what naturally happens afterwards: once someone launches an attack with an open-weights model whose guardrails they've removed, there will be many more defenders enabled by open-weights models to guard against it.