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Comments (49)

• wood_spirit
  My experience is that sota LLMs are pretty bad at confidence and priorities and things.I write harnesses that do decision steps and regularly use competing models - from the major vendors and open source models.And I have learned that if I can isolate a problem and put it into a straightforward prompt then Gemini 2.5 flash - the most basic and cheap commercial model - is actually very reliable. The newer models take longer, cost more and are really bad at saying they don’t know or nothing. You can ask them for a confidence score but they just hallucinate it!I would like to try out decision models like Jev. The way the decision models approach the problems I work with seems a much more promising fit.
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  NeumannGod
  This is a false comparison. What Jev does is fundamentally different from what LLMs are doing as a judge.For a quick primer, Jev is able to provide confidence scores on its classification, i.e. it is able to calibrate how well it is able to predict. Being able to predict in a distribution is different from being able to calibrate confidence of the predictions which should happen from the question or domain distribution from which the decisions are predicted - being able to do that is tough and is not same as using LLMs logit probabilities which are predictions in the vocab space. Though both are loosely correlated and might converge as LLMs keep getting better, the former is a much stronger decision-making signal than the latter. Jev not beating LLM-as-a-judge might be due to various other reasons such as world knowledge etc, but Jev as a concept will always provide more reliable decisions / outputs than LLM-as-a-judge giving a scalar score.
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  AnthusAI
  That was a pretty simple task they gave it, and sure you can use BERT with sequence classification for simple classification tasks.In our benchmarks, Jev did a LOT better at multi-step reasoning tasks than any open decision model we have tested so far, and it was also better than GLiDE which was specifically designed for that kind of task. And also better than Luna. On accuracy and also confidence calibration but also time and cost.https://hard-decisions.anth.us/models/
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  segmondy
  duh, this is not news. (general, fast and cheap) before decision models, you could pick only 2.LLM as judges - generalized, but too slow. If you had to make millions of classifications a day, this will be the wrong approach. you won't/shouldn't use LLM to classify spam/no spam. hot dog/or something.traditional classifiers, very specific 1 trick pony, super fast and cheap once built. If you need to make tons and tons of classifications, this would be the approach. but if you wanted a classifier right now for a novel problem, you need an expert to curate data, train and deploy.decision models/jev - are generic, you can throw them at most generic classification problems, and they are good enough. it's a fine balance between general, fast and cheap. you get all 3
• Garlef
  I think it's a bit early to call the race.I think the abstraction is a useful one - a general purpose classifier that does not need to be specifically trained: Unstructured signal in, structured judgement out - with a focus on speed and cost efficiency.And since there is not yet a large body of benchmarks, I don't think we have sufficiently explored how to measure these things.But since there's a market and some hype this will soon happen.(And it's not like TypesafeAI has a real moat or invented something entirely new here ~ they just managed to put things into one coherent perspecive)
• reexpressionist
  The key properties for using such models for conditional-branching decisions in agentic stacks (and related) is that they should be well-calibrated (under the definition chosen for the task) and informative (e.g., always predicting the mean might be "well-calibrated" in a theoretical sense for some chosen quantities of interest, but isn't particularly useful in practice).The tricky thing with the neural networks is that the output logits are in effect a highly lossy compression of the epistemic (reducible) uncertainty, so even if the target calibration quantity is well-specified, it can be difficult to obtain in practice. A side-effect of this is that estimates in the high probability regions are not particularly stable under even modest co-variate shifts, which is a real problem if the estimates are being used for decision-making in a multi-step search graph that can lead to branches that are unlike what the model/estimator saw at training/calibration (if not altogether out-of-distribution). Here are a couple papers that describe how to approach those challenges:[1] Similarity-Distance-Magnitude Activations. In Findings of the Association for Computational Linguistics: ACL 2026, pages 22037–22057, San Diego, California, United States. Association for Computational Linguistics.[2] Introspectable, Updatable, and Uncertainty-aware Classification of Language Model Instruction-following. In Proceedings of the ACM Conference on AI and Agentic Systems (CAIS '26). Association for Computing Machinery, New York, NY, USA, 1259--1269.
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  mertcikla
  Jev may or may not have truly innovated on AI architecture but it still kick-started a new paradigm.its a breather after waves of llm wrappers.
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  bjord
  am I missing something or is there an incredible amount of title editorialization here? on the page itself (and within the slug), the title is:"Benchmarking AI decision models against traditional guardrails"
• Havoc
  Traditional classifier isn’t a direct equivalent though. Jev has some light abstraction/reasoning ability.eg feed it a weather forecast and ask it whether I need an umbrella. It’s smart enough to make the connection between rain and umbrella.So somewhere between classifier and fat LLM.Ultimately boils down to right tool for the job
• mellosouls
  Jev (or equivalent) seem to me ideally placed for prototyping - prove the classification (say) works or is useful, then decide on how that should be made robust and economic with other options (eg local) brought in to the evaluation. Much quicker to get up and running, especially in greenfield scenarios.
• deepsquirrelnet
  BART is quite an old model for this kind of test, and probably not a good very good choice for much these days. I'm working on replicating their benchmark on my own NLI model that targets zero-shot guardrail applications. I don't think it'll beat much larger models, but should give a better baseline for what a crossencoder can do.https://huggingface.co/dleemiller/crossingguard-nli-l
• fg137
  Who removed "guardrail" from HN submission title?
• 6thbit
  Shouldn't LLMs intuitively be better with a high number of available options? This article only does simple prompts with only options to block or not block.What is openai doing for their decisions API, a finetuned luna?
• dominotw
  prompts that these evaluations were done are too trivial
• deadbabe
  If you build a traditional classifier, and you have the data set for training curated or created hy an LLM, then you would essentially be building a classifier that judges the same way the LLM would?
• petesergeant
  There are plenty of benchmarks that show they do, too, though, so this is a single data point.
• chatformmycusto
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• AlphanimbleAI
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• anon
  undefined
• chelseahermes
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• agenttavern
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• aidiveyt
  The block/allow framing is the part I'd push on. I had a batch where automated checks passed all 99 outputs and reading each one by hand found 8 broken. The scorer only catches the failure modes its rubric already names, and a two-option guardrail bench inherits that ceiling whichever model sits behind it.
• anon
  undefined