I asked Pi to implement a skill. It was written in TS. Then I ask it to use the skill in two different sessions, none of them can get it working. One has to wrap it with JS and call it. The other has to take the curl commands out of the skill and call them directly. Which is quite smart BTW. But what is the point of making a convoluted TS skill at all?
Completely unrelated. Recursive Language Models are just "what if we replaced putting all the long text into the context window with a REPL which lets you read parts of the context through tool calls and launch partitioned subagents", ie divide-and-conquer applied to attention space.
My first thought was also that this is also reminiscent of RLMs - they are ought to solve the same problem as far as my understanding goes. Authors say "Self-improving AI systems aim to reduce reliance on human engineering by learning to improve their own learning and problem-solving processes" which is what RLM is trying to solve so my understanding is that this work shares the same goal but takes a different approach. E.g. instead of using REPL-like environment with multiple (or even single) agents, which is what RLMs are doing, they suggest using agents that can modify themselves. I didn't read the paper so I don't know how this really works but it caught my attention so if you could share more insights I would appreciate it.
They also tend to imply symbolic recursion which seems to be the biggest deal out of everything by a wide margin.
When you can nest 10+ agents deep and guarantee you will get back home without losing any data in any of the stack frames, the ability to chunk through complex problems goes up dramatically.
https://gist.github.com/ontouchstart/40db1244c0ff523f830df1c...