The user wants me to rewrite a headline about GradCuit, a method for making LLMs reason better at test time without changing weights.

Short summary

GradCuit inserts optimizable latent vectors at an intermediate Transformer layer, using causal self-attention to create a differentiable path from output tokens back to latents—no weight updates needed. This solves the credit assignment problem that plagued prior latent methods like LatentSeek, achieving 64.5% average accuracy and beating Chain-of-Thought by 6.6 percentage points. The post includes a PyTorch implementation sketch of the core mechanism.

• •GradCuit optimizes latent vectors at an intermediate Transformer layer at test time, keeping model weights frozen
• •Causal attention creates a fully differentiable gradient path, solving the credit assignment problem in latent reasoning
• •Achieves 64.5% average accuracy across 5 models and 3 benchmarks, beating CoT by 6.6pp and LatentSeek by 2.4pp

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