Unverified50% confidenceFactExact time
Large language models do not modify their parameters during inference; each call is an independent function computation.
1
Sources
50%
Confidence
Long-term
Relevance
9/7/2026
First Seen
Sources
Related Entities
Related Claims
VerifiedLarge language models are essentially stateless functions whose weights are fixed at inference time and do not learn over time.74% similarUnverifiedThe ordinary Function Calling mode does not have hard constraints; the model still generates output that conforms to the format on a best-effort basis.74% similarVerifiedEach inference call to a large model is an independent forward pass; the model weights are completely fixed during inference and there is no mechanism to preserve state across conversation sessions.71% similarUnverifiedThe inference speed of large language models is constrained by the nature of autoregressive generation, which can only produce one token at a time and depends on all preceding tokens, making true parallelization impossible.71% similarUnverifiedLarge language models perform poorly on math and logic problems requiring multi-step reasoning, as models tend to 'jump' directly to an answer rather than going through intermediate reasoning steps.70% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/873653API
curl https://kongchang.com/api/v1/knowledge/claims/873653MCP
get_claim(id=873653)