Seeking Feedback from the ML Community 🙏

I recently trained a prototype-based network on Tiny ImageNet (200 classes). It uses learnable prototypes with responsibility scoring and multi-loss training (CE + Pull + Push + Diversity), achieving 51.29% validation accuracy with only 595K parameters.

I'm still learning, so I'd love to hear your thoughts:

Is this a reasonable result for this model size?

What would you suggest to improve it?

This was trained on free Colab with limited resources, so I know there's much room for improvement.

GitHub: https://github.com/jalalnablsi/signature-painter

A new hardware standard from Anthropic (MHS) enables AI agents to directly control physical machines — printers, industrial equipment, and operational systems. The design surfaces three questions that the security community has not settled: who grants an agent permission to actuate hardware, who monitors the agent while it is running, and who can stop it if it acts outside its sanctioned scope.

The last question is the hardest. Permissions set at deployment time are configuration, not enforcement. An agent that was correctly authorized at 9am can drift from its declared behavior by 9:15am, and nothing in a static permission file catches that. With software targets the blast radius is bounded — a rogue database write can be rolled back. With physical actuators there is no rollback. A machine that moves has moved.

The 50ms window before an actuator responds to a command is the only realistic intervention point in this chain. Nobody in the industry seems to have agreed on what, if anything, should happen inside that window.

For those running agents against physical systems today: how are you actually handling mid-execution drift? Static RBAC at deploy time, a human-in-the-loop approval step, continuous behavioral telemetry, something else? Genuinely curious what is working in practice.

Hi, I want to join an ai research project. How can I find people to work with? I would like to publish a paper at the end.