Cross-Domain Solution Matcher
A multi-stage DSPy pipeline for analogical problem solving—finding creative solutions by mapping challenges across completely different domains.
Abstract
This paper presents the Cross-Domain Solution Matcher (CDSM), a novel multi-stage DSPy pipeline that leverages large language models to find analogous solutions from disparate domains. By abstracting problems to their core challenges, mapping them to diverse fields, extracting domain-specific solutions, and synthesizing actionable recommendations, CDSM enables creative problem-solving through cross-domain analogical reasoning.
TL;DR: A 4-stage pipeline (analyze → map → extract → synthesize) that solves problems by finding creative solutions from completely different domains—like using immune system strategies to reduce customer churn.
Reasoning from First Principles
Problems that look different may share the same underlying structure.
Consider a glacier flowing down a mountain versus a queue of customers at a service counter. On the surface, they share nothing. Yet both involve flow constrained by capacity, buildup when input exceeds output, and dynamics influenced by environmental factors.
CDSM operationalizes this principle through explicit abstraction: the first stage extracts the "core challenge" independent of domain-specific details. This abstraction serves as a bridge, allowing the system to identify structurally similar problems in radically different fields.
4-Stage Pipeline Architecture
Each stage has a single, well-defined responsibility with explicit Chain-of-Thought reasoning
- •Core challenge identification
- •Key characteristics extraction
- •Desired outcome definition
- •Domain-agnostic abstraction
- •Divergent search across human knowledge
- •5-7 analogous domain identification
- •Reasoning for each domain selection
- •Structural similarity matching
- •Domain-specific solution mapping
- •Translation to original problem context
- •Implementation ideas generation
- •Fidelity to domain expertise
- •Common patterns identification
- •Unique domain insights preservation
- •Prioritized recommendations
- •Actionable next steps
Example: Customer Churn Reduction
Domains Mapped
1. Build an Early Warning System
From immune systems: Track engagement decay patterns, automate intervention triggers, personalize re-engagement messaging
2. Create Variable Reward Structures
From addiction psychology: Implement streak tracking, add surprise bonuses, use gamification judiciously
3. Foster User Communities
From social networks: Enable peer connections, facilitate collaboration, showcase user success publicly
Technical Specifications
- •Chain-of-Thought for All Modules: Every module uses explicit reasoning steps for better quality, interpretability, and debuggability
- •Multi-Stage Pipeline: Reflects natural cognitive process—understanding, recalling, retrieving, adapting
- •Structured Contracts: DSPy signatures define explicit interfaces for type safety and composability
- •High Token Budget: Prioritizes solution quality over API cost with room for rich reasoning chains
Extensions & Future Work
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