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Egzakta AI Lab released KVARK Sparkle 1.0, an open-weight 27B model based on Qwen3.8-27B that cuts thinking tokens by up to 67% and doubles median response speed while maintaining comparable accuracy across five standard benchmarks.
UkisAI announces Swift GLM-5.3-Flash and Swift Ornith 9B, two reasoning-efficient models that cut thinking token usage significantly (-61.4% and -33.5% respectively) while preserving accuracy, using GSQ-RCO penalization of pathological overthinking tokens, RL-based adaptation, and on-policy distillation, with multiple quantization formats released.
INTACT 使用一个小型外部编码器在冻结的骨干模型上诱导潜在推理，通过蒸馏将连续软令牌表示映射为潜在推理前缀，在 GSM8K 和多项跨域基准上提升准确率的同时将端到端延迟降低约 19%。
Settle 论文提出一种训练模型在推理答案已稳定时停止生成的方法，通过答案稳定性标注监督现有 end-of-reasoning token 并加入 KL 正则，在 Qwen3-4B/MATH-500 上将 token 数量减少 40% 且精度仅下降 0.5 个百分点，扩展了推理停止方法的精度-计算量 Pareto 前沿。
Swift-Qwen3.8-27B is UkisAI's reasoning-efficient derivative of Qwen3.8-27B, reducing thinking tokens by 58.3% while maintaining near-identical performance.
A paper introduces an inference-time optimization for sparse MoE models by adjusting expert selection in late transformer layers, reducing reasoning tokens by 8.5% and latency by 10.9% without retraining, while maintaining accuracy.
Gemma-4-26B-a4B, with an updated chat template, outperforms Qwen3.6-MoE and Qwen3.5-MoE in fine-tuned instruct mode and reasoning efficiency.
Grug-35B-A3B is an open-source experimental model based on Qwen-3.6-35B-A3B that reduces reasoning token usage by approximately 69.8% while maintaining answer quality within 2% of the base model, resulting in faster generation and reduced context usage.
This paper introduces DyCon, a training-free framework that uses step-level embeddings to model evolving task difficulty and dynamically control reasoning depth in Large Reasoning Models, effectively reducing overthinking and improving efficiency without sacrificing accuracy.
ThoughtFold proposes a framework using introspective preference learning to reduce redundant explorations in Chain-of-Thought reasoning for Large Reasoning Models, achieving ~56% token reduction on DeepSeek-R1-Distill-Qwen-7B without accuracy loss.
This paper introduces the VPG-EA framework, which uses variational inference and posterior guidance to improve the reasoning efficiency of large language models by addressing the 'overthinking' phenomenon in chain-of-thought generation.
This paper introduces 'Hint Tuning,' a data-efficient method that reduces token usage in reasoning models by calibrating reasoning depth based on problem difficulty. It achieves significant token reduction (24–66%) on models like Qwen3-Thinking and DeepSeek-R1-Distill using only 1K self-annotated samples.