给AI纠错，不用重写，改一个字就行
给AI的答案纠错，过去要么整段重写，要么只能从头再来。这个工具把纠错拆到最小单位：你读到AI回答里第一个不对劲的词，点一下，从候选词里挑一个对的，或者直接打字改掉，系统自动把后面的内容全部重生成。改一个字，AI接着往下写，直到你满意。一个小规模对照实验里，标注时间中位数降了52%。它现在主要是给训练AI的人用的，不是给你明天在聊天框里用的。
📄 原文摘要(英文)
We present onPanda, an interactive tool for efficiently annotating LLM alignment data and agent trajectories. onPanda adopts token-level correction as its core interaction: while reading a model response, the annotator locates the first inappropriate token and either picks a substitute from the model's candidate tokens or types the correct text via free-form editing. The system then truncates everything after that position and continues generation from the corrected prefix, repeating this locate-correct-continue loop until a satisfactory response is obtained. This mechanism lets annotators precisely steer model outputs at low cost: a small controlled study suggests that onPanda reduces median annotation time by 52% over manual post-editing. Since the vast majority of tokens in the final response are generated by the model itself, the resulting data largely preserves the model's sampling distribution and is well suited for constructing on-policy SFT and preference data. Furthermore, the token-level corrections recorded during annotation provide fine-grained supervision with precise positions and naturally paired positive--negative samples. onPanda also connects to external tools and harnesses, enabling interactive trajectory annotation in realistic environments. In addition, we release Panda-CVL, a dataset annotated with onPanda, together with a benchmark for token-level correction.