Cross-Model Void Convergence: GPT-5.2 and Claude Opus 4.6 Deterministic Silence

"Prompts sometimes return null"

I would be very cautious to attribute any of this to black box LLM weight matrices. Models like GPT and Opus are more than just a single model. These products rake your prompt over the coals a few times before responding now. Telling the model to return "nothing" is very likely to perform to expectation with these extra layers.

Anyway later they concede that it's not 100% deterministic, because

> Temperature 0 non-determinism. While all confirmatory results were 30/30, known floating-point non-determinism exists at temperature 0 in both APIs. One control concept (thunder) showed 1/30 void on GPT, demonstrating marginal non-determinism.

Actually FP non-determinism affects runs between different machines giving different output. But in the same machine, FP is fully deterministic. (it can be made to be cross-platform deterministic with some performance penalty in at least some machines)

What makes computers non-deterministic here is concurrency. Concurrent code can interleave differently at each run. However it is possible to build LLMs that are 100% deterministic [0] (you can make them deterministic if those interleavings have the same results), it's just that people generally don't do that.

[0] for example, fabrice bellard's ts_zip https://bellard.org/ts_zip/ uses a llm to compress text. It would not be able to decompress the text losslessly if it weren't fully deterministic