## 4 Discussion and Future Directions
### Design Implications  
Although detailed engineering designs are beyond the scope of this hypothesis paper and remain a target for future work, understanding LRM behavior through dual-process theory still points to several principled design directions that could improve both the efficiency and capability of reasoning systems.  
**Explicit dual-process architectures**: Systems might benefit from architectures that explicitly implement System 1 and System 2 processing pathways. Simple problems could be routed to efficient pattern-matching components (System 1 analogs) while complex tasks engage deliberative reasoning mechanisms (System 2 analogs). In practice, the router could be a lightweight policy network that receives a compact feature vector (prompt length, syntactic depth, entropy of next-token distribution) from a frozen encoder and outputs a discrete routing decision. This dynamic routing, based on real-time assessment of task complexity, could prevent the inefficiencies observed when LRMs \"overthink\" simple problems while reserving computational resources for tasks that genuinely require deliberation. Complementary evidence comes from recent emotion-aware LLM work showing that hierarchical fusion layers coupled with lightweight attention gates can provide an ultra-fast 'affective sentinel' for routing [23; 24]. Such modules could supply the System 1 complexity signal that triggers the router envisioned here.  
**Adaptive resource management**: The counterintuitive reduction in reasoning tokens at high complexity suggests that current LRMs already implement rudimentary resource management strategies. Future systems could make these mechanisms explicit. For example, a metacognitive head could be constructed that tracks the marginal perplexity improvement per extra reasoning token. When the perplexity to token improvement falls below a learned threshold, the module truncates the Chain-of-Thought and returns the best current hypothesis. Thus, rather than viewing effort reduction as failure, systems could be designed to recognize when strategic disengagement represents optimal resource allocation. Embedding the same hierarchical fusion monitors inside the metacognitive head would let LRMs link token budget decisions to transient affective load, offering a concrete, testable implementation of the dual-process resource manager.  
**Capacity-aware training**: Training paradigms could incorporate principles from human cognitive psychology, including deliberate practice within capacity limits and strategic rest periods that mirror sleep-inspired consolidation. Multi-phase training with alternating periods of challenge and consolidation could improve both learning efficiency and robustness, preventing the cognitive overload that leads to systematic disengagement.  
**Evaluation beyond accuracy**: Current benchmarks that focus solely on final answer correctness miss important aspects of computational intelligence related to resource efficiency. New evaluation frameworks should consider effort allocation, strategic disengagement patterns, and the ability to adaptively match computational investment to problem tractability, recognizing that optimal performance sometimes involves choosing not to expend excessive resources on intractable problems.