Reasoning Routing

Reasoning Routing addresses the inefficiency of applying expensive multi-step reasoning to all queries regardless of complexity. Simple questions ('What's the weather?') don't benefit from reasoning chains, while complex problems ('Compare three approaches to X considering Y constraints') require systematic decomposition. Mavaia's ReasoningRouter analyzes query characteristics to determine whether specialized reasoning modules (Python brain modules for analytical, creative, strategic, diagnostic, or comparative reasoning) should activate. This conditional approach achieves +12.3% accuracy improvement for complex queries while avoiding unnecessary latency for simple requests. The system operates through threshold-based activation using query length, keyword patterns, and multi-step indicators to predict reasoning requirements.

Reasoning Routing implements three-stage decision process. First, Complexity Analysis examines query characteristics: length (>200 characters increases activation probability), keyword presence (research, analyze, compare, classify, evaluate trigger activation), multi-step indicators (numbered lists, 'first... then...', 'step by step' phrases). Second, Activation Threshold Evaluation combines complexity signals into confidence score requiring >0.70 threshold for activation (manually tuned based on empirical evaluation). Third, Module Selection routes activated queries to appropriate Python brain module based on intent type: analytical (data analysis), creative (brainstorming), strategic (planning), diagnostic (troubleshooting), comparative (evaluation). The system logs all routing decisions enabling post-hoc accuracy assessment and threshold refinement.

Reasoning routing evaluation across 5,000 queries showed 15% conditional activation rate. Accuracy improvement: queries routed to reasoning modules achieved +12.3% accuracy versus baseline direct inference. False activation rate: 8% of activated queries didn't genuinely benefit from reasoning (unnecessary latency). Missed activation rate: 5% of complex queries not activated, missing potential accuracy improvement. Module selection accuracy: 89% of activated queries routed to appropriate reasoning type. Latency characteristics: reasoning-activated queries 3-6 seconds total (2-5s reasoning overhead plus 1s base), non-activated queries 1.8s average. User experience: reasoning activation transparent to users with progress indicators for longer operations.

Reasoning Routing demonstrates that conditional activation effectively balances capability and efficiency. The 15% activation rate proves most queries don't require expensive reasoning - simple questions benefit from fast inference without multi-step decomposition. The +12.3% accuracy improvement validates reasoning modules provide genuine value when activated. The 8% false activation and 5% missed activation represent acceptable error rates for threshold-based heuristics. The 3-6 second latency for reasoning-activated queries is significant but acceptable given the accuracy benefit for genuinely complex problems. The system's logged routing decisions enable continuous threshold refinement as more data accumulates. The approach contrasts with continuous reasoning systems (DeepSeek R1, Google Deep Think) that apply reasoning to all queries regardless of complexity.

Current limitations include: (1) Threshold-based activation (>200 chars, keyword presence) misses some complex short queries and activates some simple long queries, (2) Manual threshold tuning (0.70 confidence) may not generalize across all users and query types, (3) Module selection limited to five reasoning types, missing hybrid queries requiring multiple reasoning approaches, (4) The system doesn't explicitly model query ambiguity where reasoning helps clarify requirements, (5) Routing decision doesn't consider user urgency signals that might justify skipping reasoning for time-sensitive queries, (6) Python brain latency (2-5s) creates noticeable delay even for users who understand reasoning provides value.

Reasoning Routing provides conditional activation of specialized reasoning modules based on query complexity assessment. The 15% activation rate and +12.3% accuracy improvement validate that selective reasoning application efficiently allocates computational resources. The framework demonstrates that reasoning systems don't need to operate continuously - threshold-based conditional activation provides appropriate trade-offs between capability and efficiency. Future work will focus on learned activation thresholds rather than manual tuning, reduced Python brain latency, hybrid reasoning for queries requiring multiple approaches, ambiguity-based activation, and user urgency signal integration for adaptive reasoning depth.