Autonomous discovery

The systems that work stand on auto-reset (a fresh environment per attempt), auto-improve (the loop drives its own upgrades) and auto-evaluate (outcomes judged by the environment, not by model confidence). Feedback must come from the environment, and selection matters as much as generation, the loop needs a mechanism for choosing among candidate ideas, not only for generating them.

The reported successes are mostly bounded, verifiable problems, exactly the class a register-and-gates methodology fits, and full autonomy over open-ended questions remains undemonstrated. The dissents are held, not dismissed.

Auto-reset is worktree discipline: every worker gets a fresh worktree off the mainline. Auto-improve is the worker desks. Auto-evaluate is the verify rows executed after merge by someone other than the implementer. The end-to-end research pipeline, out-of-band idea generation, agents arguing the candidates, harness-gated execution, human accept-or-reject at the end, is the shape the dreaming pass was designed to have. None of that runs today.

The open-source “lab” results, branch per idea, sandboxed writes, findings written down, are Assay’s core bet arrived at independently, sandbox rationale included, with loops carrying all three legs saturating their task. Three transfers are recorded: an ELO-style tournament as a selection step among competing brief plans (none exists today), iterating with the environment as the argument for verify rows against real CI, and revisiting brief granularity on the task-horizon cadence rather than treating it as fixed.

8 of 14 talks shown, the ones that reach the most concepts in this theme. Every sighting, per talk, is on the concept pages above.