# "我没做微决策"：在协作中衡量、引导与揭示目标层面的AI贡献

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-05-20 08:00
- AIHOT 分数：64
- AIHOT 链接：https://aihot.virxact.com/items/cmpgy1ffy0jmxsljwdv6kl48l
- 原文链接：https://arxiv.org/abs/2605.21363

## AI 摘要

本研究提出了CoTrace目标级归因框架，用于分解协作目标并追踪AI的贡献。对638份真实对话的分析发现，大语言模型在目标塑造中的直接贡献为11%-26%，但在引入具体实践需求方面作用显著，并存在多种间接影响。控制实验表明交互设计会影响AI的目标行为。用户研究显示，向用户展示目标级分析后，其对AI贡献的感知评分在5分制中变化了近2分，揭示了用户对自身AI协作成果存在系统性的校准偏差。

## 正文

As large language models (LLMs) increasingly shape how users form, refine, and extend their goals, attributing contributions in human-AI collaboration becomes critical for users calibrating their own reliance and for evaluators assessing AI-assisted work. Yet existing methods focus on final artifacts, missing the process through which goals themselves are jointly shaped. We introduce a goal-level attribution framework, CoTrace, that decomposes explicit goals into verifiable requirements and traces both direct contributions and indirect influences across dialogue turns. Applying CoTrace to 638 real-world collaboration logs, we find that while models account for only 11-26% of goal-shaping contribution, they contribute substantially more on introducing lower-level concrete requirements, and make various kinds of indirect contributions. Through controlled simulations, we show that interaction design choices significantly affect model goal-shaping behavior. In a user study, exposing participants to goal-level analyses shifts their perceived contributions by nearly 2 points on a 5-point scale, revealing systematic miscalibration in how users understand their own AI-assisted work.