Public articles linked to the same research event.
arXiv HyperThink proposes a text-to-parameter approach in which 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 those updates to a finite set of reusable patterns; trained end-to-end on outputs from the base model itself, it eliminates long thinking traces at test time, so that 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, and it 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.
HyperThink proposes a text-to-parameter approach in which 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 those updates to a finite set of reusable patterns; trained end-to-end on outputs from the base model itself, it eliminates long thinking traces at test time, so that 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, and it 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.
HyperThink proposes a text-to-parameter approach in which 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 those updates to a finite set of reusable patterns; trained end-to-end on outputs from the base model itself, it eliminates long thinking traces at test time, so that 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, and it 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.
HyperThink proposes a text-to-parameter approach in which 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 those updates to a finite set of reusable patterns; trained end-to-end on outputs from the base model itself, it eliminates long thinking traces at test time, so that 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, and it 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.