Subliminal Transmission of Relational Tension: Integrating Subliminal Learning with LMT’s Dynamic F-Term

Recent work on subliminal learning (Cloud et al., 2026) demonstrated that behavioural traits and misalignment can transmit through semantically unrelated data via hidden patterns in model embeddings. This phenomenon resonates strongly with Load Minimization Theory (LMT), where the F-term (relational tension) exhibits subtle, non-obvious accumulation and propagation even after apparent resolution. Building on the hybrid stabilization framework (Yoshino, 2026a), we propose that F dynamics can be understood as a relational analogue of subliminal transmission: micro-tensions and recovery patterns persist as “hidden textures” in the interaction embedding space, influencing future states. By decomposing F into natural variation, early neutralization, spiking, and recovery, LMT offers a compassionate framework to detect and gently neutralize these latent influences, transforming potential relational misalignment into deeper An-soku.