Driving Plato's Chariot: A Dual-Objective Teacher-Student Prototype for Reason-Affect Allocation in Dialogue

<title>Abstract</title> <p>Conversational systems may need to preserve factual reliability while responding sensitively to affective context. This paper audits and reframes a compact teacher-student prototype that makes this tension explicit through two bounded outputs inspired by Plato's chariot allegory. The recorded experiment maps each dialogue message to three features: length density, negative-sentiment probability from an SST-2 classifier, and a heuristic urgency flag. A dual-headed teacher produces reason- and affect-allocation proxies under a scalar reward, while a 194-parameter student learns the teacher's two-dimensional output by mean-squared error. The notebook reports 11,936 message records drawn from the first 1,000 conversations of a 10,000-row generated dialogue corpus. Student-teacher training MSE decreased from 0.003040 at epoch 1 to 0.000719 at epoch 10 and ended at 0.001481 at epoch 25. In contrast, cumulative teacher reward worsened from − 23,194.12 to -29,219.67, and the notebook-labeled factual-accuracy proxy declined from 59.3% to 50.9%. These results establish implementation and in-sample compression feasibility, but they do not demonstrate improved truthfulness, empathy, or reinforcement-learning convergence. The contribution is therefore a transparent proof-of-concept, a corrected interpretation of its measurements, and a concrete redesign path for a valid multi-objective dialogue-control study.</p>