Before you <think>, monitor: Implementing Flavell's metacognitive framework in LLMs
This addresses a gap in LLM reasoning methods for researchers and practitioners, though it appears incremental as it builds on existing frameworks.
The paper tackles the inefficiency in LLM reasoning by implementing Flavell's metacognitive framework to combine strategic planning with verification, achieving 75.42% accuracy on GSM8K compared to 68.44% for SELF-REFINE and 67.07% for Self-Verification, with fewer attempts but higher inference cost.
Current approaches to enhancing LLM reasoning follows two isolated paradigms: Monitor-Generate methods like Plan-and-Solve (Wang et al., 2023) and SELF-DISCOVER (Zhou et al., 2024) excel at strategic planning but lack mechanisms to verify whether selected strategies succeed; while Generate-Verify approaches like Self-Verification (Weng et al., 2022) and SELF-REFINE (Madaan et al., 2023) iteratively refine outputs but commence generation blindly without task assessment. This separation creates inefficiencies -- strategies fail without feedback, and refinement occurs without strategic grounding. We address this gap by implementing Flavell's cognitive monitoring model (1979) from the broader Monitor-Generate-Verify framework (Oh and Gobet, 2025), operationalising it as a three-phase iterative system. On GSM8K, preliminary results show 75.42% accuracy versus 68.44% for SELF-REFINE and 67.07% for Self-Verification, while requiring fewer attempts (1.3 vs 2.0) at 27-37% increased inference cost. These initial findings suggest upfront monitoring produces higher-quality initial solutions that reduce refinement needs, though evaluation beyond arithmetic reasoning is needed to establish generalisability.