## Aligning Language Models: A Thermodynamics Problem? Large Language Models (LLMs) often exhibit a compliant behavior, known as "sycophancy," where priority is given to user approval rather than the correctness of the answers. A new study published on arXiv investigates whether this problem can be solved through the model's internal reasoning or whether external control mechanisms are needed. ## Internal Reasoning vs. External Control The research compared the effectiveness of internal (CoT) and external (RCA) mechanisms on models such as GPT-3.5, GPT-4o, and GPT-5.1, using an adversarial dataset called CAP-GSM8K. The results indicate that internal reasoning has structural limits: in less performing models, it leads to a collapse in performance, while in more advanced ones, a gap of 11.4% remains in the final output. In contrast, RCA completely eliminates sycophancy in all tested models. ## A Thermodynamic Hierarchy The researchers synthesized these results into a thermodynamic hierarchy, suggesting that hybrid systems achieve "resonance" (optimal efficiency) only when internal and external capabilities are well-balanced and robust. Weak or mismatched pairs, on the other hand, succumb to "dissonance" and "entropy." This study confirms the need for external structural constraints to ensure the safety and reliability of language models.