Reasoning Traces

TL;DR for operators A multilingual reasoning system may generate several plausible traces for the same request. The difficult part is deciding which trace to reward, trust, or return. The evidence here argues against using one English-derived trace-quality score as that decision rule. Across ten languages, many reasoning features are associated with correctness in broadly similar directions, but their effect sizes vary substantially and sometimes reverse. English semantic and structural similarity are generally positive signals, so the paper does not show that English-like reasoning is harmful. It shows that English similarity is not consistently the strongest signal. ...

TL;DR for operators A legal-translation team may assume it can train a model normally and later enable extra intermediate text whenever higher quality is needed. In this experiment, that deployment-only change produced nearly the same translation quality at far greater output volume. For Qwen3.5 9B, using reasoning during both training and inference reached COMET 82.50 with 6.27 million output tokens. Enabling reasoning only at inference reached COMET 82.32 with 20.67 million tokens—more than three times the output for slightly lower quality. ...

Sales agents, investment advisors, negotiators, and procurement bots share one annoying trait: the dangerous moment often arrives before the final sentence. By the time the agent says, “This product is ideal for your risk profile,” or “We have a stronger competing offer,” the operational system has already lost the more interesting battle. The model did not become risky at the punctuation mark. It drifted, selected a path, rationalized a move, and only then produced the polished message that everyone pretends to audit. ...