CoSMo Framework Improves Reasoning Efficiency in Large Language Models Through Split-Merge Optimization
Researchers have proposed CoSMo, a framework that reduces structural redundancy in large reasoning model outputs by dynamically merging redundant segments and splitting logical gaps in reasoning chains. Long reasoning chains in current AI models impose significant computational costs and latency, motivating the search for more efficient alternatives. CoSMo demonstrates that reasoning efficiency and accuracy need not trade off, achieving a 3.3-point accuracy improvement alongside a 28.7% reduction in segment usage.
A research paper submitted to arXiv introduces CoSMo (Consistency-Guided Split-Merge Optimization), a framework targeting the verbose reasoning chains produced by Large Reasoning Models (LRMs). Rather than simply capping token output, CoSMo employs a split-merge algorithm that identifies and collapses redundant reasoning segments while also detecting and filling logical gaps to preserve coherence. The framework pairs this structural refinement with structure-aligned reinforcement learning, using a novel segment-level budget signal to guide training toward efficient reasoning patterns. Evaluated across multiple benchmarks and model backbones, CoSMo outperforms reasoning efficiency baselines by 3.3 accuracy points on average while cutting segment usage by 28.7%. The work addresses a practical bottleneck in deploying reasoning-capable AI systems, where longer chains increase inference latency and hardware costs. The paper reached its camera-ready version in June 2026 after initial submission in February 2026.
What's missing
It is unclear how CoSMo performs on tasks requiring genuinely long reasoning chains where compression might hurt correctness, nor whether the segment-level budget hyperparameter requires significant tuning per task or model. Computational overhead introduced by the split-merge algorithm itself is not discussed in the abstract.
What different sources said
- arXiv cs.CLCenter
Short Chains, Deep Thoughts: Balancing Reasoning Efficiency and Intra-Segment Capability via Split-Merge Optimization
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