Loop

You can set up an iterative improvement loop by specifying a target and iteration count, which runs multiple algorithm cycles to refine the target while tracking cumulative improvements and avoiding previously failed approaches logged in a dead-ends ledger.

Yes, you can enable autonomous iteration without human review by activating Autoresearch mode, which allows the algorithm to refine the target independently while tracking iteration count and cumulative improvements.

Yes, an algorithm harness is required to execute the observation and learning phases. The iterative improvement loop relies on this harness to process the target across multiple refinement cycles.

Use a dead-ends ledger to avoid repeating failed approaches during algorithm refinement. This ledger maintains a record of unsuccessful strategies, allowing future iterations to bypass previously failed methods.

Human review integration works by pausing between algorithm cycles to allow manual review and redirection of the target refinement. You can also disable this feature by enabling autonomous iteration mode.

Your iterative algorithm refinement may lack a dead-ends ledger to record failed approaches. Maintaining this ledger ensures future cycles avoid previously unsuccessful strategies and prevents repeated dead ends.