Actions Speak Louder than Words: Measuring Cross-Lingual Policy Retention in Tool-Using Agents
Score
77首次将动作轨迹作为多语言评测对象并揭示五个可翻转结论的混淆因素，信息增量极高。
238 万次 rollout 的大规模实验、预注册预测与置换检验构成严密方法链，论述完整。
对多语言 Agent 评测与部署中的语言中转、提示词局限和评估正则陷阱给出了直接可用的警示与修正方案。
发布在 arXiv 最新一轮，直接回应当前多语言 Agent 评测缺乏过程审计的紧迫问题。
来自 arXiv 预印本，虽未经同行评审，但实验设计和预注册细节使其可信度较高。
Base 85 × T2 Source weight ×0.9 = 77
Curation line 70Selected
Why selected
Large-scale experiments combined with pre-registered ablations and permutation tests expose hidden evaluation traps with reproducible rigor, making the conclusions highly impactful.
AI commentary
「This is one of the rare papers that moves the crux of multilingual evaluation from answers to actions. For teams building agent evaluation or multilingual products, it offers a mirror: if you judge model ability only by text outputs, you may not even notice a 26x error. I especially recommend the finding that non-English tasks are routed through English—it implies current multilingual ability is essentially 'English policy plus a translation channel,' with real consequences for cost, latency, and failure boundaries.」
Quick overview
This arXiv study measures the action policy of tool-using agents instead of final answers. Across 8 models, 6 parallel benchmarks, 41 languages, and 2.38M rollouts, the authors remove five confounds that can flip conclusions. After normalization by each model's own reproducibility, four diverse frontier models converge under greedy decoding to 71–73% cross-lingual policy retention, with model identity explaining only 5.7% of variance. Below roughly 10B parameters, retention breaks down, and smaller-model rankings are largely a chance-floor artifact measured by permutation. The paper also shows agents route non-English tasks through English in a causally load-bearing pivot that cannot be abandoned via instructions. Finally, a single trace-extraction regex manufactured a 26-fold accuracy gain while readable outputs barely changed, warning that multilingual evaluation must inspect actions, not just answers.
- For the first time, action trajectories are the measured object across 8 models, 41 languages, and 2.38M rollouts, with five confounds systematically removed.
- After reproducibility normalization, frontier models retain only 71–73% of their action policy across languages, with model identity explaining just 5.7% of variance.
- Below roughly 10B parameters, cross-lingual policy retention collapses, and smaller-model rankings are largely driven by a chance floor.
- Non-English tasks are causally routed through English, and models do not abandon this pivot when instructed; one trace-extraction regex can fake a 26-fold accuracy gain.
Full text
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