Prompt-Adapted Transport Control: Contextual Tokenwise LLM Behaviour Steering via Flow Matching

Many activation-steering methods compress a behavioural contrast into a fixed global direction, ignoring how the desired transformation varies across contextual token states. We introduce Prompt-Adapted Transport Control (PATC), which learns flows at two complementary resolutions. A token-level flow transports contextual prompt states toward the target-behaviour distribution; PATC selects, orients, and aggregates behaviour displacements aligned with the training behavior distribution, retains the components that best characterize how the behavior changes, and removes overlap with the source-activation subspace to obtain a prompt-adapted unit direction. Because local token transports vary in norm with context, a separate flow trained on mean-pooled response activations supplies a common magnitude from the terminal prompt state. Their product forms a fixed cache applied to all tokens throughout generation without further flow evaluation. We evaluate anger and anxiety at approximately matched achieved strength, isolating intervention quality from steering intensity; these dimensions also permit comparison with held-out COVID-Worry human reference profiles. Across evaluation, PATC consistently balances affect, lexical quality, task compliance, and human-profile fidelity.