NVIDIA研究长程智能体错误率问题
NVIDIA这篇论文揭示了长程智能体在处理任务时准确率下降的具体数据，对构建可靠AI助手很有参考价值。
NVIDIA发布论文研究长程智能体在128K上下文下的错误率问题。研究显示，当上下文从4K增加到128K时，七个开源模型的准确率下降62.8%。当输入格式变化时，准确率下降36.5%；当每步操作难度增加时，准确率下降39.9%。该研究测量了智能体在处理长表格或账本时失去位置的原因。
Banger paper from NVIDIA on long running agents.
A model can accept 128K tokens of context and still make more mistakes the longer it works through a task.
If your agent loses its place partway through a long table or ledger, this work measures what causes it.
The setup:
Long-Transduction asks a model to keep reading, updating and outputting state-dependent results over thousands of outputs, and varies three factors separately.
Results:
Across seven open-weight models, accuracy drops 62.8% when context grows from 4K to 128K, 36.5% when only the input format changes, and 39.9% when the per-step operation gets harder.
Paper: https://t.co/kjWSHzoegq
