Unverified50% confidenceBenchmarkExact time
模型有时会陷入过度思考，在特定任务中产生超过52倍的额外token
1
Sources
50%
Confidence
Long-term
Relevance
7/15/2026
First Seen
Sources
Related Claims
Unverified工具调用次数过多会导致上下文污染，使模型难以追踪任务目标73% similarUnverifiedOverly verbose, contradictory, or ambiguous system prompts cause the model's attention to become scattered, resulting in over-thinking or instruction-following failures; streamlining agents.md can improve the determinism of instruction following.73% similarUnverified研究表明，当任务需要超过 5-7 个相互依赖的推理步骤时，模型的错误率会显著上升72% similarUnverified过度膨胀的上下文会稀释模型注意力、抬高推理成本，甚至引入噪声导致幻觉71% similarUnverified模型规模越大，幻觉的表述往往越流畅、越具迷惑性，识别难度反而上升70% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/515796API
curl https://kongchang.com/api/v1/knowledge/claims/515796MCP
get_claim(id=515796)