🔗 Who cites «Learning How Hard to Think: Input-Adaptive Allocation of LM Computation»

22 papers of our library cite it (of the 149,439 whose bibliographies we read) · 7 with a page here, the most cited first · ← back to «Learning How Hard to Think: Input-Adaptive Allocation of LM Computation»

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. Reasoning Models Know When They're Right: Probing Hidden States for Self-VerificationarXiv:2504.05419Anqi Zhang, Yulin Chen, Jane Pan, et al.2025 · 52 citations
3. A Survey of Frontiers in LLM Reasoning: Inference Scaling, Learning to Reason, and Agentic SystemsarXiv:2504.09037Zixuan Ke, Fangkai Jiao, Yifei Ming, et al.2025 · 39 citations
4. The Surprising Effectiveness of Test-Time Training for Few-Shot LearningarXiv:2411.07279Ekin Akyürek, Mehul Damani, Adam Zweiger, et al.2024 · 36 citations
5. GlimpRouter: Efficient Collaborative Inference by Glimpsing One Token of ThoughtsFindings of the Association for Computational LinguisticsWenhao Zeng, Xuteng Zhang, Yuling Shi, et al.2026 · 1 citation
6. Adaptive Test-Time Compute Allocation with Evolving In-Context DemonstrationsFindings of the Association for Computational LinguisticsBowen Zuo, Dongruo Zhou, Yinglun Zhu2026
7. 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

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

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