Copasetic Flow

 I'm still seeing Sonnet fan work out beyond its model capablities using subagents. You can see in the window below that the Gas Town polecat has had to stop and compact its context before proceeding. The good news is that when these subagent work fans don't turn into storms, I'm seeing more thorough reseearch findings returned. The bad news is that with Sonnet's limited context, those findings occasionally overwhelm the polecat that started it all.  I found a preprint from MIT for using what the authors call recurive language models or RLMs. The general idea is basically what I'm seeing with subagents—split the task, split the context—but they have clever ideas about how to manage the output stages to avoid the exact overflows shown above. I'm still harboring my hunch that this all started with the advent of Sonnet 5. Anthropic's release notes include "Sonnet 5 is much more agentic than its predecessors. Testers described how it finishes complex tas...

 Working with Fable last week, I noticed that it can get very excited about fanning out tasks, (to the point of crashing my WSL session.) I experimented with prompting agents to split out tasks on their own, but haven't found a successful to do this yet. Consequnetly, I reverted back to my original prompt which does not explicitly call for task splits. Then, this morning, running on Sonnet 4.6, another task storm sprung up. I wonder if the new 50% higher token limit  till August is Anthropics way of buying themselves some head  room while they experiment with models creating subagents?  Are other  people seeing the same thing? It's particularly worrisome that Sonnet 4.6 has started a subagent storm becuase it really doesn't ahve the context to deal with the results.

 I'm still tracking down what makes some agents find a Nikola Tesla research finding and why others do not. Today, that's led me into investigating Claude CLI's forking harness. A few notes from me. Forking looks pretty spectacular! The agent kicks off a subagent that automatically has a copy of the parent's context. The subagent doesn't add to the parent's context until it's done. So, it seems to make things cheaper, at least for my passenger manifest research. The agent that used forking made the Tesla association. The other two agents with the same inputs and the same model did not use forking and did not find the Tesla assocation. This is important. It seems that agents that don't fork lack the persistence to look for more than one "really good" finding. They make that one good finding, and then kind of take any results for the rest of the passengs as good enough. Each forked subagent is looking for its own "really good" finding ...