Parallel-Voice Proposer
Generate 2–3 candidate thoughts in parallel under named voices in one completion and have the same model select the canonical one — surfacing internal disagreement without paying for sequential persona calls.
Intent & Description
🎯 Intent
Single-pass generation produces confident-sounding output that masks real internal disagreement. Best-of-N is too expensive. Sequential personas are too slow. Parallel voices in one completion is the practical middle ground.
📋 Context
A single-agent loop on a workload where the model often outputs confident prose that hides real ambivalence or disagreement. Running an inner-committee of personas sequentially is too slow per tick; scoring N independent completions is too expensive per call.
💡 Solution
Prompt the model to produce 2–3 candidate next-thoughts in one completion, each prefixed with a voice tag (e.g. [voice: world-model], [voice: critic], [voice: prediction]). Then ask for a single selected:
Real-world Use Case
- Single-pass generation produces overconfident output that hides real internal disagreement.
- Sequential persona calls are too slow per tick for the cadence you need.
- An external reward model for best-of-N scoring is not available.
Source
Advantages
- Internal disagreement is preserved rather than collapsed into one confident output
- One completion is cheaper than sequential persona calls while still surfacing alternatives
- The loser log creates an audit substrate for retrospective analysis
Disadvantages
- Same model means correlated voices — true diversity is limited by shared training distribution
- Self-selection can rubber-stamp the first candidate without an explicit rotation strategy
- Keeping voices distinct adds prompt overhead per tick