LLM-native problem-solving method exploiting parallel candidate exploration:
- Generate — each participant proposes k distinct solution approaches (anonymised to avoid anchoring).
- Score — all participants score all candidates on feasibility/impact/risk (reuse matrix-scoring machinery).
- Prune — keep the top 2-3 (beam).
- Expand — each surviving candidate gets a deep-dive round: refine, identify obstacles, concretise.
- Repeat 2-4 until convergence or depth limit, then synthesise.
Genuinely iterative, so it depends on phase-machine loop support (#22). Together with NGT (#24) it would give the platform a real generative/exploratory arm to complement the evaluative methods.
LLM-native problem-solving method exploiting parallel candidate exploration:
Genuinely iterative, so it depends on phase-machine loop support (#22). Together with NGT (#24) it would give the platform a real generative/exploratory arm to complement the evaluative methods.