Expired60% confidenceFactExact time
Large language models predict the next token one at a time through an autoregressive generation mechanism, and are naturally inclined to generate more complete rather than more concise code.
2
Sources
60%
Confidence
Medium-term (~90 days)
Relevance
7/2/2026
First Seen
This claim has expired. It is kept for the record and no longer describes the current state.
Valid until: 9/30/2026(expired)
Sources
Ponytail: The Lazy Philosophy That Teaches AI to Write 90% Less Code
bilibiliAI_transfer6/16/2026
Related Claims
UnverifiedModern large language models generate code in an auto-regressive manner, predicting the next token each time by referencing only the existing context.82% similarUnverifiedLarge language models are optimized to 'maximize the prediction accuracy of the next token,' which naturally inclines them toward generating 'complete, self-consistent' outputs.80% similarUnverifiedLarge language models generate text in an autoregressive manner, where the production of each token depends on the context formed by all preceding tokens, following the chain rule of probability.80% similarUnverifiedLarge language models are autoregressive generation models where each output step depends on the preceding context.80% 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.79% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/45557API
curl https://kongchang.com/api/v1/knowledge/claims/45557MCP
get_claim(id=45557)