CSE Roadmap

Core tier · about 4 hours · 6 parts

Part 3 of 6 · 3 min read

Results and how to use them

Covers: Results; Design rules.

Results

Per 1,000 tasks, 20,000 simulated tasks per policy; 93.5 harmful actions in total:

Policy Requests per task Catch probability (fatigued) Harm leaked, fatigued Harm leaked, full attention Good actions rejected
none 0 n/a 93.5 93.5 0
all 6.0 0.23 70.8 9.5 120
irreversible 1.8 0.47 60.4 29.2 35
risk score >= 0.5 1.0 0.60 45.1 22.3 19
irreversible or risk >= 0.7 1.9 0.46 57.0 19.5 38
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actions that leak through, per 1,000 tasks, and time to finish 50 tasksLeaked harmful actions per 1,000 tasksnone94 fatigued94 full attentionall71 fatigued9 full attentionirreversible60 fatigued29 full attentionrisk >= 0.545 fatigued22 full attentionirrev. or risk>=0.757 fatigued20 full attentionReviewer attention assumed to halve at 20 requests per hour.Minutes to finish 50 tasks486250wait 5 min1,663272wait 30 min5,904713wait 120 minblockingpause and free workerAssumed rates and waits; simulation with no model and no real reviewers.
Harmful actions leaked under five gating policies, with and without fatigue, and time to finish 50 tasks with blocking and non-blocking waits.

Load (harm leaked per 1,000 tasks, gate-everything / irreversible-only): at 2 tasks per hour 39.9 / 39.4; at 10, 70.8 / 60.4; at 40, 86.7 / 79.4.

Blocking against pausing (minutes to finish 50 tasks, one worker, 5 minutes of work each): mean wait 5 minutes: 486 against 250; 30: 1,663 against 272; 120: 5,904 against 713.

Reading it:

  1. Gate-everything is the best policy only for a reviewer with unlimited attention. Under my fatigue assumption it is nearly as bad as no gate (71 of 94 leaked).
  2. Fewer, better-targeted requests win under load, and the gap grows as load grows.
  3. The risk-score gate did best under fatigue but only because the score is informative by assumption. Treat it as a hypothesis to test on your own data, not a result.
  4. Targeting has a floor. Irreversible-only never sees reversible harm (0.5 percent per action), which is why it leaks 29 even with full attention.
  5. Pausing, not waiting, keeps throughput. The pause costs durable state; the gain grows with the wait.
  6. A time limit is a design choice. At a 30-minute mean wait, 36.8 percent exceed 30 minutes, 13.5 percent exceed 60 and 1.8 percent exceed 120.

Design rules

  • Classify tools by consequence (read-only, reversible, irreversible, costly) and gate the top class; review the classification as tools change.
  • Measure the gate: approval rate, edit rate, time to decide, and harm found in sampled auto-approved actions. A rate above 95 percent suggests over-flagging; check by sampling.
  • Make the request readable: the action, its arguments, the reason, the evidence, the undo path and the choices (approve, edit, reject, ask).
  • Pause durably and free the worker; resume by id on any worker.
  • Default on timeout is stop or re-plan, and long pauses are re-validated (module 7).
  • Ask in the interface that fits: a short, specific question beats a generic "continue?"
  • Keep an audit log of who approved what and when.
  • Reduce gates as evidence accumulates, keeping spot checks (executor to reviewer to governor).
  • Enforce the gate in code (a hook or the tool wrapper), not in the prompt; a prompt can be talked out of it (Level 10).