---
title: "Who cites Adaptive Inference-Time — 22 citing papers"
description: "22 papers of the Elamur Research library cite «Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even…"
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---
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# 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»](https://md.research.elamur.ai/free/en/papers/manvi-2024-adaptive-inference-time-compute-llms/)

1. [**Do NOT Think That Much for 2+3=? On the Overthinking of o1-Like LLMs arXiv:2412.21187**Xingyu Chen, Jiahao Xu, Tian Liang, et al.*2024 · 192 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-think-that-much-2-3/)
2. [**Dynamic Early Exit in Reasoning Models arXiv:2504.15895**Chenxu Yang, Qingyi Si, Yongjie Duan, et al.*2025 · 74 citations*](https://md.research.elamur.ai/free/en/papers/yang-2025-dynamic-early-exit-reasoning-models/)
3. [**Deep Think with Confidence arXiv:2508.15260**Yichao Fu, Xuewei Wang, Yuandong Tian, et al.*2025 · 61 citations*](https://md.research.elamur.ai/free/en/papers/fu-2025-deep-think-confidence/)
4. [**Can 1B LLM Surpass 405B LLM? Rethinking Compute-Optimal Test-Time Scaling arXiv:2502.06703**Runze Liu, Junqi Gao, Jian Zhao, et al.*2025 · 41 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-1b-llm-surpass-405b-llm/)
5. [**Harnessing the Reasoning Economy: A Survey of Efficient Reasoning for Large Language Models arXiv:2503.24377**Rui Wang, Hongru Wang, Boyang Xue, et al.*2025 · 16 citations*](https://md.research.elamur.ai/free/en/papers/wang-2025-harnessing-reasoning-economy-survey-efficient/)
6. **[Meta-Reasoner: Dynamic Guidance for Optimized Inference-time Reasoning in Large Language Models](https://md.research.elamur.ai/free/en/papers/sui-2026-meta-reasoner-dynamic-guidance-optimized/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yuan Sui, Yufei He, Tri Cao, et al.*2026 · 13 citations*
7. **[Adaptive Test-Time Compute Allocation with Evolving In-Context Demonstrations](https://md.research.elamur.ai/free/en/papers/zuo-2026-adaptive-test-time-compute-allocation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Bowen Zuo, Dongruo Zhou, Yinglun Zhu*2026*
8. **[Think Hard Only When Needed: A Hybrid Best-of-N and Beam Search for Efficient Test-Time Compute](https://md.research.elamur.ai/free/en/papers/suh-2026-think-hard-only-when-needed/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hyewon Suh, Chaojian Li, Cheng-Jhih Shih, et al.*2026*

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

- [← back to](https://md.research.elamur.ai/free/en/papers/manvi-2024-adaptive-inference-time-compute-llms/) — Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation