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
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Merged summary
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.
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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
Public signals
N/A
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.