Expired65% confidenceFactExact time
Large 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.
3
Sources
65%
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
Andrew Ng's New Course Explained: A Practical Guide to Using OpenAI's O1 Reasoning Model
bilibili吴恩达智能体6/13/2026
Related Claims
Unverified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83% similarUnverifiedProof text output by conversational large language models is generated based on statistical patterns and cannot guarantee that every step is logically valid81% similarUnverifiedIn 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.80% similarUnverifiedTraditional language models tend to exhibit logical drift or error accumulation in reasoning chains exceeding 5–7 steps.79% similarUnverified思维链推理的效果随模型规模呈现涌现性,在较小模型上几乎无效,在足够大的模型上才能显著提升推理准确率79% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/47320API
curl https://kongchang.com/api/v1/knowledge/claims/47320MCP
get_claim(id=47320)