multi-candidate-search-module

What problem does it solve?

It helps when a single model attempt is too fragile by making the search process explicit: it explores multiple candidates, prunes weak branches, and selects the best remaining option.

Core Features & Use Cases

• Explicit candidate budgeting: Allocates a meaningful default budget (K=5) or respects a user-specified limit, and treats branch failures as missing-budget events rather than normal pruning.
• Diversified candidate generation: Encourages variation across hypothesis, decomposition, evidence routes, tool usage plans, or risk preferences to avoid near-duplicates.
• Lightweight pruning and evidence-based selection: Removes duplicates, inconsistencies, unsupported candidates, and dominated options; selects using task-fit and evidence quality with pairwise comparison when scoring is noisy.
• Failure-aware escalation: Avoids forcing a bad winner when quality is insufficient, instead expanding budget, changing diversification axes, or triggering a new round with refined guidance.

Quick Start

Use the multi-candidate-search-module when you need to reliably solve a high-stakes or ambiguous task where a single-shot answer is likely to be brittle.