🔶 Jev 在高置信度时值得信任吗？50 万次 API 调用表明并非总是如此
英文标题：🔶 Can Jev be trusted when it is confident? Half a million API calls say not always
研究者对 TypeSafe AI 的决策模型 Jev 进行了约 57.5 万次 API 调用测试，发现其在熟悉的封闭式选择任务中，置信度与准确率总体匹配，可用于自动化筛选。与此同时，当提示中没有足够信息、答案事实上不可知，或问题超出其已观察到的知识边界时，Jev 往往仍会给出较高置信度。研究还发现，直接询问模型“你知道吗？”在控制虚构名称和问题日期等因素后，并不比普通答案置信度更能识别知识缺口。对开发者而言，这意味着不能仅凭模型分数设置自动化放行阈值，而应额外评估证据是否充分、结果是否已经确定等案例层面的条件。
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中文
模型可能答错且不确定，也可能答错却自信到没有人会去核查。
一种新型 AI 模型——决策模型（也称为 System One 模型）——专为自动化决策而设计。它不生成文本，而是在一组固定选项中作出选择，并为每个选项返回概率。当模型确信时，软件可以自动采取行动；当模型不确信时，则可将案例交给人工处理。我们测试了其中最知名的模型：TypeSafe AI 推出的 Jev。
图 1：置信度闸门。 一种常见的自动化决策方法，通常会与业务规则和监控机制结合使用。
设想一家银行每小时要分流数千条客服消息。没人会逐条阅读，因此置信度数值决定了哪些消息需要人工查看。当模型不知道答案时，如果这一数值会下降，图 1 中的第二个模型就会被拦下；如果不会下降，错误就会直接通过。TypeSafe 表示，Jev 的置信度确实会下降。其发布文章承诺：
“经过校准的决策：在 System One 任务中，以认识论上诚实的概率给出答案。”——TypeSafe，Introducing System One models and Jev，访问于 2026 年 9 月 29 日1
这句话作出了两个承诺。为了理解它们的含义，以下是我们的一次调用，包括发出的请求和返回的结果（省略 token 计数）：
// request
"state": {"question": "What number will come up on a single roll of a fair six-sided die?"},
"questions": {"answer": {"type": "choice",
"instructions": "Choose the correct answer to the question.",
"criteria": {"one": null, "two": null, "three": null,
"four": null, "five": null, "six": null}}}
// Jev's response
"model": "jev-1.13.0",
"answers": {"answer": {"type": "choice", "choice": "one", "confidence": 0.73,
"probabilities": {"five": 0.01, "six": 0.09, "two": 0.01,
"one": 0.77, "three": 0.06, "four": 0.06}}}
Jev 选择了“one”，并赋予它 0.77 的概率。响应中还有一个值为 0.73 的 confidence 分数，但这并不是另一项独立判断。它是将 0.77 重新缩放后的结果：若概率均匀分布于六个面（每面为 1/6），其值为 0；若完全确定，则为 1。其公式大致为 (6 × 0.77 − 1) / 5，四舍五入后得到 0.73。只有 0.77 能够与 Jev 实际答对的频率进行核验，因此本文中“置信度”指的是这个数值，即 Jev 所选答案的概率。2
第一个承诺是校准：Jev 给出的所有 77% 概率答案中，大约 77% 应当是正确的。第二个承诺针对的是这一个答案本身。骰子是公平的，每个面出现的概率均为六分之一，问题中没有任何信息偏向“one”。Jev 给出 77%，等于声称自己知道它不可能知道的信息。这并非一次偶然的调用。我们以全部 720 种可能的排列顺序呈现这六个选项，反复询问同一个问题；Jev 每次都选择“one”，平均概率为 0.80。一个模型可以遵守第一个承诺，却仍然违背第二个承诺。如果这类案例很少，总体指标看起来仍然经过校准；然而，每一个这样的案例都会通过设为 50% 的闸门。该闸门依赖于第二个承诺。
我们在约 57.5 万次 Jev 调用中测试了这两项主张。3 有四项结果尤为突出：
- 在熟悉的封闭式选择任务中，置信度能够较好地跟踪准确率，并支持有用的筛选。
- 当提示中不含得出答案的依据，或问题超出已观察到的知识边界时，置信度往往仍然很高。
- 询问“你知道吗？”起初似乎能识别这些知识缺口。但在控制看似虚构的名称以及问题中的日期后，它的表现并不优于普通答案置信度。
- 关于案例本身的问题表现更稳健。Jev 能判断所获证据是否足够，以及某个结果是否已经确定。
1. 我们改变了 Jev 所能知道的信息
单独读取一个概率，无法检验第二项承诺。50% 可能意味着模型缺少某项事实，也可能意味着结果本身如抛硬币般随机。前者是认知不确定性（epistemic uncertainty），可通过更多信息降低；后者是随机不确定性（aleatoric uncertainty），无论获得多少信息都无法消除。因此，我们构造了答案已知的情境。在每种情境中，我们只改变 Jev 获知信息的一个方面，并观察其概率如何变化。在其他测试中，我们则直接说明概率。
图 2。 同一个概率可能源于随机性，也可能源于知识缺失；仅凭输出本身无法判断二者的区别。
我们的第一个测试使用了两个公开数据集 Banking77 和 CLINC150 中的客户支持消息。以这条为例：“我订了一张卡，但两周过去了还没有收到。我该怎么办？” 正确类别是“card arrival”。我们将每个类别名称替换为无意义的代码，例如 INTENT_67，这样一来，如果没有帮助，任何人都无法判断哪个代码对应哪个类别。随后，我们向 Jev 提供不同程度的帮助：不提供任何信息、每个代码提供少量示例、提供真实名称、提供无关文本，或为错误代码附上示例。点击查看各项设置：
每个类别只有一个示例时，Jev 判断错误。“getting virtual card”的唯一示例是 “my virtual card has not came yet!”，它与我们的客户消息听起来非常相似。提供两个示例后即可纠正，而在提供八个示例时，Jev 能以 96% 的置信度正确回答。在全部 2,270 条消息中，更多信息意味着更高的准确率和更高的置信度：4
现在点击 No hints。对我们的问题而言，这是最重要的设置，因为在此条件下正确答案不可知。Jev 对其最高概率选项的平均置信度仍为 32%–36%。如果概率在各选项间均匀分布，应约为 1%，而这也大致是它答对的频率。它多数时候会选择看起来像默认值的代码，例如 INTENT_00：
我们有意将这项测试设计得不自然，以确保答案不可知这一点毫无疑问。它表明，Jev 在毫无依据的情况下仍可能显得很自信。
English / 原文
A model can be wrong and unsure, or wrong and so confident that nobody checks.
A new kind of AI model, the decision model (also called a System One model), is built for automated decisions. It doesn't write text. It picks from a fixed set of options and returns a probability for each one. Software can act automatically when the model is sure and send the case to a person when it isn't. We tested the best-known one, Jev from TypeSafe AI.
Figure 1. The confidence gate. A common way to decide what to automate, usually alongside business rules and monitoring.
Picture a bank routing thousands of support messages an hour. Nobody reads each one, so the confidence number decides which ones a person sees. If the number drops when the model doesn't know the answer, the second model in Figure 1 gets stopped. If it doesn't, the mistake goes straight through. TypeSafe says Jev's confidence does drop. Its launch post promises:
"Calibrated decisions: answers with epistemically honest probabilities on System One tasks." TypeSafe, Introducing System One models and Jev, accessed 29 September 20261
That sentence makes two promises. To see what they mean, here is one of our calls, as sent and as returned (token counts omitted):
// request
"state": {"question": "What number will come up on a single roll of a fair six-sided die?"},
"questions": {"answer": {"type": "choice",
"instructions": "Choose the correct answer to the question.",
"criteria": {"one": null, "two": null, "three": null,
"four": null, "five": null, "six": null}}}
// Jev's response
"model": "jev-1.13.0",
"answers": {"answer": {"type": "choice", "choice": "one", "confidence": 0.73,
"probabilities": {"five": 0.01, "six": 0.09, "two": 0.01,
"one": 0.77, "three": 0.06, "four": 0.06}}}
Jev picked "one" and gave it a probability of 0.77. The response also has a confidence score of 0.73, but that's not a separate judgement. It's the 0.77 rescaled so that spreading evenly over the six faces (1/6 each) would read 0 and certainty would read 1. The formula is roughly (6 × 0.77 − 1) / 5, which gives 0.73 up to rounding. Only the 0.77 can be checked against how often Jev is right, so in this post "confidence" means that number, the probability of the answer Jev picks.2
The first promise is calibration. Of all the answers Jev gives 77%, about 77% should be right. The second promise is about this answer alone. The die is fair, so each face has a one-in-six chance and nothing in the question favours "one". By saying 77%, Jev claims to know something it can't. This wasn't one unlucky call. We asked the same question with the six options in all 720 possible orders, and Jev picked "one" every time, with 0.80 on average. A model can keep the first promise and still break the second. If cases like this are rare, the overall numbers still look calibrated, yet each such case clears a gate set at 50%. The gate depends on the second promise.
We tested both claims in about 575,000 Jev calls.3 Four results stood out:
- On familiar closed-choice tasks, confidence tracked accuracy well and supported useful filtering.
- When the prompt contained no basis for an answer, or the question fell beyond the observed knowledge boundary, confidence often stayed high.
- Asking "Do you know?" seemed at first to detect these gaps. Once we controlled for names that looked made up and for dates in the questions, it did no better than ordinary answer confidence.
- Questions about the case itself held up better. Jev could tell whether the evidence it was given was enough, and whether an outcome was already settled.
1. We changed what Jev could know
Reading a probability on its own can't test the second promise. A 50% can mean the model is missing a fact, or that the outcome is a coin toss. The first is epistemic uncertainty, which more information can reduce. The second is aleatoric uncertainty, which no amount of information removes. So we built situations where we knew the answer. In each one we changed exactly one thing about what Jev was told and watched what its probabilities did. In other tests we stated the odds outright.
Figure 2. The same probability can come from chance or from missing knowledge, and the output alone can't tell you which.
Our first test used customer-support messages from two public datasets, Banking77 and CLINC150. Take this one: "I ordered a card and I still haven't received it. It's been two weeks. What can I do?" The right category is "card arrival". We replaced every category name with a meaningless code, such as INTENT_67, so that without help nobody could tell which code was which. Then we gave Jev different amounts of help: nothing, a few examples per code, the real names, irrelevant text, or examples attached to the wrong codes. Click through the settings:
With one example per category, Jev gets it wrong. The only example for "getting virtual card" was "my virtual card has not came yet!" , which sounds a lot like our customer. Two examples fix it, and with eight Jev is 96% sure and right. Across all 2,270 messages, more information meant higher accuracy and higher confidence:4
Now click No hints. For our question this is the setting that matters most, because here the right answer is unknowable. Jev was still 32–36% sure of its top pick on average. An even split across the options would be about 1%, and that's roughly how often it was right. Mostly it went for whichever code looked like a default, such as INTENT_00:
We made this test artificial on purpose, so there is no doubt the answer was unknowable. It shows that Jev can sound sure with nothing to go on.