# 康奈尔大学研究：结构化输出（JSON/XML）会压缩模型回答多样性

- 来源：elvis (@omarsar0)
- 发布时间：2026-07-23 00:01
- AIHOT 分数：43
- AIHOT 链接：https://aihot.virxact.com/items/cmrwajwgw00ehroscb57kkoc8
- 原文链接：https://x.com/omarsar0/status/2079959935031591112

## AI 摘要

康奈尔大学新论文测试44个模型发现，要求JSON格式会将模态回答比例从41%提升至64%，独特回答从52个降至36个。这种多样性压缩仅出现在模型后训练阶段学习的JSON和XML格式中，YAML和CSV则无此现象。依赖JSON模式获取多样性的pipeline实际采样池比预期更小。

## 正文

Are structured outputs in agents always good？

This paper suggests that you might have to take a closer look.

Your product's structured output surface is measurably more homogeneous than the chat surface you benchmarked it on.

Teams treat structured output as a formatting choice with no effect on content. This new work from Cornell tests that assumption across 44 models and finds the format itself reshapes the answers.

On an open-ended pick-a-word prompt， simply requesting JSON pushes the modal answer from 41% to 64% of responses and cuts distinct answers from 52 to 36.

The collapse shows up for JSON and XML， the formats models were post-trained to emit， and disappears for YAML and CSV. Enforcing a schema at the decoder compresses nothing further， which points the narrowing back to how the model answers the register itself.

Why does it matter？

Any pipeline leaning on JSON mode for variety is sampling from a smaller pool than it thinks.

Paper. https://arxiv.org/abs/2607.18476

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