AGENTIC-SYSTEMSApril 27, 20261 min read6 views

Introducing Background Temperature to Characterise Hidden Randomness in Large Language Models

Researchers introduce 'Background Temperature' (T_bg) to formalize and measure the inherent non-determinism in LLMs caused by hardware and implementation factors, even at zero nominal temperature.

Computer Science > Artificial Intelligence

Title: Introducing Background Temperature to Characterise Hidden Randomness in Large Language Models

Even when decoding with temperature $T=0$, large language models (LLMs) can produce divergent outputs for identical inputs. Recent work by Thinking Machines Lab highlights implementation-level sources of nondeterminism, including batch-size variation, kernel non-invariance, and floating-point non-associativity.

In this short note we formalize this behavior by introducing the notion of background temperature $T_{bg}$, the effective temperature induced by an implementation-dependent perturbation process observed even when nominal $T=0$. We provide clean definitions, show how $T_{bg}$ relates to a stochastic perturbation governed by the inference environment $I$, and propose an empirical protocol to estimate $T_{bg}$ via the equivalent temperature $T_n(I)$ of an ideal reference system.

We conclude with a set of pilot experiments run on a representative pool from the major LLM providers that demonstrate the idea and outline implications for reproducibility, evaluation, and deployment.

Source: arXiv cs.AI Recent

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