Efficient Reasoning with Balanced Thinking

The paper proposes ReBalance, a training-free framework that uses confidence signals to detect and mitigate both overthinking and underthinking in large reasoning models. It resides in the 'Confidence-Based Reasoning Control' leaf under 'Adaptive Reasoning Control and Difficulty-Aware Methods', which contains only two papers including this one. This is a relatively sparse research direction within a broader taxonomy of 50 papers across 22 leaf nodes, suggesting the specific approach of using confidence variance and overconfidence patterns for dual-sided reasoning control is not yet heavily explored.

The taxonomy reveals that ReBalance sits within a larger ecosystem of adaptive reasoning methods. Its parent branch includes 'Difficulty-Adaptive Reasoning Frameworks' (3 papers) and 'Dynamic Early Exit and Truncation Mechanisms' (2 papers), which address similar efficiency goals through different signals—explicit difficulty modeling or self-termination criteria. Neighboring branches include 'Reinforcement Learning and Training-Based Optimization' (5 papers) and 'Post-Hoc Reasoning Refinement' (3 papers), which tackle reasoning efficiency through training interventions or post-generation filtering rather than inference-time steering. The taxonomy's scope notes clarify that confidence-based methods like ReBalance are distinguished from difficulty-aware approaches by their reliance on model uncertainty rather than upfront problem characterization.

Among 30 candidates examined, the contribution-level analysis shows mixed novelty signals. 'Confidence as a continuous indicator' examined 10 candidates with 1 refutable match, suggesting some prior work uses confidence for reasoning control but perhaps not in the dual-detection manner proposed here. The 'ReBalance framework' contribution also examined 10 candidates with 1 refutable match, indicating the specific steering vector approach may have precedent. The 'plug-and-play solution' contribution examined 10 candidates with 2 refutable matches, suggesting training-free efficiency improvements are more established. These statistics reflect a limited search scope, not exhaustive coverage of the field.

Given the sparse population of the confidence-based control leaf and the limited search scale, the work appears to occupy a relatively underexplored niche within adaptive reasoning methods. The analysis captures top-30 semantic matches and does not guarantee comprehensive coverage of all relevant prior work, particularly in adjacent areas like difficulty-aware frameworks or latent reasoning representations. The dual focus on both overthinking and underthinking through confidence dynamics may differentiate this work from single-sided approaches, though the limited refutable matches suggest some conceptual overlap exists within the examined candidates.