Abstract
Long-form thinking traces can substantially improve the multi-step reasoning performance of large language models (LLMs), but they introduce high inference-time overhead, with latency dominated by sequential decoding. We propose HyperThink, a text-to-parameter approach that amortizes this reasoning computation into a single query-conditioned parameter update: a lightweight hypernetwork reads the question and predicts updates to a small subset of the base LLM's parameters, while a vector-quantized decoder constrains them to a finite set of reusable patterns to improve robustness and transfer. Trained end-to-end on outputs from the base model itself, HyperThink eliminates long thinking traces at test time: after one hypernetwork forward pass, the adapted model generates a concise step-by-step solution and final answer without an intermediate trace, using far fewer tokens while retaining strong reasoning performance. Empirically, HyperThink improves the low-latency region of the accuracy-latency trade-off on mathematical and general reasoning tasks, with its strongest gains in the near-non-thinking regime.
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Cite this article
APA 7
Kim, D., Lu, J., Kim, C., Ren, M., & Hong, S. (2026). HyperThink: Text-to-Parameter Hypernetworks for Efficient Reasoning. https://omanscience.com/en/articles/hyperthink-text-to-parameter-hypernetworks-for-efficient-reasoning
MLA 9
Kim, Donggyun, et al. "HyperThink: Text-to-Parameter Hypernetworks for Efficient Reasoning." https://omanscience.com/en/articles/hyperthink-text-to-parameter-hypernetworks-for-efficient-reasoning.
Chicago (author–date)
Kim, Donggyun, Jack Lu, Chanwoo Kim, Mengye Ren, and Seunghoon Hong. 2026. "HyperThink: Text-to-Parameter Hypernetworks for Efficient Reasoning." https://omanscience.com/en/articles/hyperthink-text-to-parameter-hypernetworks-for-efficient-reasoning.
Harvard
Kim, D., Lu, J., Kim, C., Ren, M. and Hong, S. (2026) 'HyperThink: Text-to-Parameter Hypernetworks for Efficient Reasoning', Available at: https://omanscience.com/en/articles/hyperthink-text-to-parameter-hypernetworks-for-efficient-reasoning.
Vancouver
Kim D, Lu J, Kim C, Ren M, Hong S. HyperThink: Text-to-Parameter Hypernetworks for Efficient Reasoning. https://omanscience.com/en/articles/hyperthink-text-to-parameter-hypernetworks-for-efficient-reasoning
IEEE
D. Kim, J. Lu, C. Kim, M. Ren, and S. Hong, "HyperThink: Text-to-Parameter Hypernetworks for Efficient Reasoning," https://omanscience.com/en/articles/hyperthink-text-to-parameter-hypernetworks-for-efficient-reasoning.