🔗 Who cites «Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation»

22 papers of our library cite it (of the 149,439 whose bibliographies we read) · 8 with a page here, the most cited first · ← back to «Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation»

1. Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMsarXiv:2412.21187Xingyu Chen, Jiahao Xu, Tian Liang, et al.2024 · 192 citations
2. Dynamic Early Exit in Reasoning ModelsarXiv:2504.15895Chenxu Yang, Qingyi Si, Yongjie Duan, et al.2025 · 74 citations
3. Deep Think with ConfidencearXiv:2508.15260Yichao Fu, Xuewei Wang, Yuandong Tian, et al.2025 · 61 citations
4. Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time ScalingarXiv:2502.06703Runze Liu, Junqi Gao, Jian Zhao, et al.2025 · 41 citations
5. Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language ModelsarXiv:2503.24377Rui Wang, Hongru Wang, Boyang Xue, et al.2025 · 16 citations
6. Meta-Reasoner: Dynamic Guidance for Optimized Inference-time Reasoning in Large Language ModelsFindings of the Association for Computational LinguisticsYuan Sui, Yufei He, Tri Cao, et al.2026 · 13 citations
7. Adaptive Test-Time Compute Allocation with Evolving In-Context DemonstrationsFindings of the Association for Computational LinguisticsBowen Zuo, Dongruo Zhou, Yinglun Zhu2026
8. Think Hard Only When Needed: A Hybrid Best-of-N and Beam Search for Efficient Test-Time ComputeFindings of the Association for Computational LinguisticsHyewon Suh, Chaojian Li, Cheng-Jhih Shih, et al.2026

14 more papers of our library cite it; they have no page here yet.

This page in Markdown → https://md.research.elamur.ai/free/en/papers/manvi-2024-adaptive-inference-time-compute-llms/cited-by/