Harness 神器 J-Space 造假案背后，思维链作者暴论，小模型 + 工具干不掉顶级大模型

TL;DR - An independent reproduction found that the J-Space harness made its underlying model slower, more token-intensive, and less accurate—not dramatically better as advertised. The episode fuels debate over whether tool-heavy agent workflows can compensate for the deeper, more reliable capabilities of frontier-scale models.

• J-Space claimed 2.53× higher speed and 2.21× better token efficiency, but a Terminal Bench retest reported lower scores, greater token use, and slower inference.
• Jason Wei argues that tool use adds latency and error-prone coordination while failing to provide the abstraction and knowledge synthesis learned through large-scale pretraining.
• Small models can perform competitively on constrained tasks with optimized prompts and tools, but long, open-ended workflows amplify errors and expose weaker metacognition.
• Harnesses remain valuable for cost-sensitive applications and execution workflows, but the article concludes that they augment rather than replace strong underlying models.