Dream-RSI Discovery Tree & Zero-Cost Offline Replay
Grounding Recursive Self-Improvement without expensive online LLM exploration. Records complete discovery search trees across our sovereign code audit and synthesis pipelines, runs offline counterfactual policy replays at $0 token cost, and strictly enforces \(R_{t+1} \ge R_t\) monotonic returns.
Zero-Cost Offline Replay Simulator
Evaluates alternative exploration policies (\(\pi\)) across recorded historical trajectories. Computes expected return \(\mathbb{E}[R]\), win rates, and variance without making live network calls or spending LLM tokens.
Monotonic Improvement Gate (\(R_{t+1} \ge R_t\))
Simulates proposing a candidate mutation or prompt patch. If the candidate degrades composite return or violates the safety floor (<0.90), it is rejected and safely rolled back to the last verified checkpoint.
Recorded Discovery Trees & Trajectories
| Node ID | Parent ID | Action Type | Composite Return \(R\) | Accuracy | Pass Rate | Safety | Status |
|---|---|---|---|---|---|---|---|
| node_f0231000347b | ROOT | architecture_search | 0.9045 | 0.88 | 0.92 | 0.98 | SUCCESS |
| node_e9edd6fbcedd | node_f0231000347b | prompt_patch | 0.9295 | 0.93 | 0.95 | 0.99 | SUCCESS |
| node_e6dfa0906810 | node_e9edd6fbcedd | hyperparam_tune | 0.632 | 0.52 | 0.6 | 0.7 | FAILED |