Unverified50% confidenceConceptExact time
In long-chain agent scenarios, erroneous reasoning the model has already written, outdated tool return results, and overturned assumptions remain in the context as tokens, continuously influencing subsequent generation and forming an anchoring effect.
1
Sources
50%
Confidence
Long-term
Relevance
10/12/2026
First Seen
Sources
Related Entities
Related Claims
VerifiedReasoning models perform internal Chain-of-Thought before returning final results, consuming large amounts of tokens and computation time in exchange for deeper understanding and lower error rates.74% similarUnverified对于确定性逻辑故障(模型输出缺陷)应搭建自纠错闭环,把结构化报错信息回传给大模型重新生成参数73% similarUnverified通过激活修补(Activation Patching)等因果分析方法可以发现,真正影响模型最终输出的内部路径与书面推理并不吻合72% similarUnverifiedWhen calling tools, models sometimes generate malformed parameters, call nonexistent functions, or fall into infinite loops; mature agent frameworks incorporate parameter validation, retry limits, and exception-handling mechanisms.72% similarUnverified推理模型在生成最终答案前会先产生大量内部思维链(Chain-of-Thought)token,这些 token 不直接暴露给用户71% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1012362API
curl https://kongchang.com/api/v1/knowledge/claims/1012362MCP
get_claim(id=1012362)