Unverified60% confidenceOpinionExact time
In AI decision-making scenarios dominated by large language models, the model's reasoning process is distributed across the activation states of billions of parameters, making it difficult to establish a complete causal chain from input to output.
2
Sources
60%
Confidence
Long-term
Relevance
7/12/2026
First Seen
Sources
Zero-Dependency AI Decision Record Auditor: A Practical Guide to Building Traceable AI Systems
hackernewshackernews7/9/2026
Related Claims
UnverifiedAI叙事生成的高算力消耗主要来源于自回归生成机制,模型生成每个词元时都需完整执行一次前向传播计算,推理成本随长度近似线性增长78% similarVerifiedThe 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.77% similarUnverified扩散语言模型在复杂推理、指令遵循和思维链等方面面临结构性挑战,可控性弱于自回归模型77% similarVerifiedThe emergent capability of large language models refers to the complex reasoning ability that suddenly manifests after the parameter scale surpasses a certain threshold.77% similarUnverified大模型在约千亿参数规模附近突然展现出思维链推理、多步数学解题等此前小模型不具备的能力76% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/489218API
curl https://kongchang.com/api/v1/knowledge/claims/489218MCP
get_claim(id=489218)