@Thom_Wolf: watching a team of agents tackling a hard theoretical physics problem is quite mesmerizing - self-correcting, deriving …
Summary
A tweet observes AI agents collaboratively solving a difficult theoretical physics problem, demonstrating self-correction and equation derivation.
View Cached Full Text
Cached at: 05/15/26, 02:58 AM
watching a team of agents tackling a hard theoretical physics problem is quite mesmerizing - self-correcting, deriving hard equations, computing intermediate results, re-estimating the best approach https://t.co/RhUmNXkGLB
Similar Articles
@agupta: some ideas are much clearer when you can use coding agents to show a proof of concept. eg I hadn’t really understood ho…
A tweet highlights how coding agents can clarify complex ideas, using GPU vs NPU memory competition on devices as an example demonstrated through code.
@MaximeRivest: Coding agents can only accelerate our work when we are willing to accept that we may not fully understand the overly co…
The article discusses how AI coding agents require engineers to accept that they may not fully understand the complex systems created, drawing parallels to other fields like natural resource management.
@TheTuringPost: AutoScientists – a research lab made of agents @Harvard researchers connected agents into a self-organizing scientific …
Harvard researchers present AutoScientists, a multi-agent system that forms self-organizing scientific teams without a central coordinator, achieving strong results on BioML-Bench and optimization tasks.
@omarsar0: This was one of the standout AI papers of the week. (bookmark it) It tackles a question most self-improving AI agents i…
This paper introduces a categorical framework for distinguishing genuine scientific discovery from mere retrieval or search in self-improving AI agents, using category theory to formalize regime transitions. The authors demonstrate the framework with a protein mechanics example where an agent's accuracy drops as it tackles harder problems, but its theory compresses more data, indicating real discovery.
@Thom_Wolf: Love this work from Aksel and the post-training team at Hugging Face! Turns out the HF ecosystem (papers, datasets, mod…
Hugging Face’s post-training team demonstrates how the HF ecosystem enables ML agents to autonomously train any AI model to peak performance.