Unverified50% confidenceTradeoffExact time
Core decisions in ADAS must be predictable, verifiable, and reproducible, whereas the outputs of LLMs are inherently stochastic.
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Long-term
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9/24/2026
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UnverifiedBecause LLM outputs are probabilistic, one cannot expect the model to proactively acknowledge errors; validation must have deterministic guarantees added at the system level.67% similarUnverifiedLLMs' probability judgments drift under the influence of irrelevant factors such as how evidence is framed and the order in which it is presented.66% similarUnverifiedTraditional autoregressive models typically require additional processing or approximation methods to obtain reliable probability distributions, giving diffusion models a structural advantage in this regard.65% similarUnverifiedDue to the stochasticity of large model outputs, evaluation practices commonly employ methods such as model scoring (LLM-as-Judge), human annotation, or reference-based semantic similarity.65% similar
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