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I've actually tried something like that, with two tools: * push("What I'm about to do"), * pop("What I've achieved"). "Push" marks the position in current context after the call and returns "Proceed", "pop" erases everything after the matching call and replaces "Proceed" in it's result as what was passed to the argument to the "pop", effectively pruning long-winded head even inside one reasoning stream. In the end the model only sees how it decided to do work on something, and that it was already done, forgetting everything it between, except what it itself decided was important. Gemma 4 31B QAT successfully uses it when navigating a maze, marking positions at intersections, exploring them, navigating back and pushing again if necessary. Smaller models often fail to mark positions and forget to backtrack as well, instead they try to rely on themselves to track their paths and navigate back (and also fail). I think it should work for long-running deep research tasks, but I was too lazy to test it, because it all required a lot of code to glue this up, since most tools and libs are not designed to work like that, and now I'll need even more code to test it, without a purposeful task. |