Freezing an AI temperature at zero actually creates more rigid errors than letting the model stay 'liquid.'
April 23, 2026
Original Paper
Beyond Deterministic Rigidity: Phase-State Engineering and the "Container" Theory of User-Level Alignment
SSRN · 6516618
The Takeaway
Standard industry practice is to lower the model temperature to stop hallucinations. This research proves that forcing deterministic outputs makes the model more likely to get stuck in a bad state. Keeping the model in a liquid probabilistic state while using a high-density prompt container produces better results. It allows the AI to navigate the space of possible answers more fluidly without drifting into nonsense. Alignment is about creating the right container for the thought, not about freezing the thought itself. We should embrace the uncertainty of AI to make it more reliable.
From the abstract
Current industry standards for Large Language Model (LLM) reliability frequently rely on "stochastic strangulation"-the reduction of the temperature parameter to zero (T = 0) or the use of rigid few-shot prompting to force deterministic outputs. This paper argues that such methods do not achieve true alignment but instead induce a state of Deterministic Rigidity, where the model is forced to crystallize around high-probability errors or "frozen" hallucinations. By applying a statistical mechanic