GradCuit: Credit-Assigned Gradient Flow Enables Robust and Interpretable Test-Time Latent Reasoning

GradCuit is a test-time latent-reasoning method that freezes model parameters and inserts optimizable continuous states at a selected Transformer layer between the prompt representation and generated continuation. Causal self-attention creates differentiable paths from continuation-token log-probabilities to preceding latent states, allowing sequence-level reward gradients to update those states directly. The supplied abstract reports evaluation across five instruction-tuned backbones, three reasoning benchmarks, and two answer formats, with 64.5% average accuracy: 6.6 percentage points above chain-of-thought prompting and 2.4 points above the strongest competing method.