Unverified50% confidenceFactExact time
The determinism of LLM inference has preconditions: the inference backend must not introduce extra randomness, such as changes in floating-point accumulation order caused by dynamic batching, non-deterministic GPU operators, or tokenizer version differences.
1
Sources
50%
Confidence
Long-term
Relevance
10/18/2026
First Seen
Sources
With Temperature Set to 0, Regex Beat the Prompt
rss10/18/2026
Related Entities
Related Claims
UnverifiedBecause LLM outputs are probabilistic, one cannot expect the model to proactively acknowledge errors; validation must have deterministic guarantees added at the system level.77% similarUnverifiedThe Adjudicated Query pattern externalizes judgment logic to a deterministic rules engine, thereby avoiding the problems of approximate matching in vector retrieval and the non-reproducibility of probabilistic sampling in LLMs.76% similarUnverifiedLLMs generate tokens based on probability distributions and cannot guarantee 100% adherence to complex multi-condition instructions; when there are too many rules in a prompt, the model may forget some constraints due to context competition.75% similarVerifiedThe essence of LLMs is statistical probability-based sequence prediction rather than true logical reasoning, which creates inherent limitations when handling long-context dependencies, global architectural consistency, and complex edge cases.74% similarVerifiedAlthough LLMs do not directly output normalized probability vectors, decision confidence can be approximately estimated through various engineering methods.73% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1059135API
curl https://kongchang.com/api/v1/knowledge/claims/1059135MCP
get_claim(id=1059135)