alphaXiv(@askalphaxiv):"HyperThink: Text-to-Parameter Hypernetworks for Efficient Reasoning" Instead of actually generating CoT, this paper uses a lightweight hypernetwork to predict small and question-specific updates to the LLM's bias parameters, approximating the effect of thinking without generating the trace. Trained on the base model's own reasoning outputs, it improves accuracy over non-thinking on several reasoning tasks while keeping latency close to non-thinking. On some QA benchmarks, it matches thinking-mode performance with only 7-9% of the latency.

2026.10.09 05:44

"HyperThink: Text-to-Parameter Hypernetworks for Efficient Reasoning" Instead of actually generating CoT, this paper uses a lightweight hypernetwork to predict small and question-specific updates to the LLM's bias parameters, approximating the effect of thinking without generating the trace. Trained on the base model's own reasoning outputs, it improves accuracy over non-thinking on several reasoning tasks while keeping latency close to non-thinking. On some QA benchmarks, it matches thinking-mode performance with only 7-9% of the latency.