Expired75% confidenceOpinionTime unknown
Current reasoning models perform prominently in mathematics and programming, but there is still room for improvement in open-domain reasoning, causal reasoning, and other areas.
1
Sources
75%
Confidence
Medium-term (~90 days)
Relevance
6/1/2026
First Seen
This claim has expired. It is kept for the record and no longer describes the current state.
Valid until: 8/30/2026(expired)
Sources
LLM推理能力演进:从思维链到DeepSeek-R1全解析
githubatfortes
Related Entities
Related Claims
UnverifiedThe essence of reasoning-enhanced models is trading inference-time compute for accuracy73% similarUnverified深度推理模式的技术基础是链式思维(Chain-of-Thought)推理与推理时计算扩展(Inference-time Compute Scaling)73% similarVerifiedInference-time scaling improves model performance by increasing computation during the inference phase, and exhibits a power-law relationship similar to that of pre-training.73% similarUnverifiedTest-time compute scaling improves performance on complex reasoning tasks by investing more computation during the inference stage through methods such as extending the chain of thought, multi-path sampling with best-of-n selection, and self-verification reflection.72% similarUnverifiedAnthropic's Extended Thinking feature allows the model to perform longer and more in-depth reasoning, and is the key reason the Opus model excels in ambiguous scenarios72% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/29117API
curl https://kongchang.com/api/v1/knowledge/claims/29117MCP
get_claim(id=29117)