🔧 GradCuit: How to Make LLMs Reason Better at Test Time Without Changing a Single Weight

TL;DR


GradCuit (arXiv:2608.02585) inserts optimizable latent vectors at an intermediate Transformer layer and uses causal self-attention as a differentiable "circuit" to flow reward-weighted gradients directly to those latents at test time — no parameter updates, no token resampling, just smarter internal reasoning. Result: 64.5% average...