CASE

Researchers propose CASE (Correctness-Axis SElection), a dynamic selection combiner that trains a linear gate on the answer-token hidden state to select the highest-scoring candidate, and introduce de…

Researchers propose CASE, a framework combining training-time causal alignment and inference-time structural enforcement to improve chain-of-thought faithfulness in large language models. Experiments …