The most interesting question in AI evaluation is rarely the one that gets asked. We spend enormous energy measuring how well models perform, but almost none measuring how badly they fail, and even less on whether those failures are predictable. The question posted about specification ambiguity and correlated failure across model families cuts straight to that gap. It is a question about measurement, not explanation, and that distinction is what makes it worth taking seriously.
The premise is well established in practice, even if the formal literature is thin. Give different models the same underspecified task and they often converge on the same wrong answer. The poster is not asking why that happens. They are asking whether anyone has quantified the ambiguity of the task specification itself, and then tested whether that number predicts how often independent solvers fail identically. That is a sharper and more useful question than it first appears. If ambiguity is measurable and correlates with correlated failure, then we have a tool for auditing tasks before we run them, and for predicting where model families are likely to share blind spots.
What makes this worth our attention is the implied shape of the relationship. The poster asks directly whether the correlation is a smooth monotone increase or a threshold effect, where past some level of ambiguity the coincidence rate jumps sharply. That is not a minor methodological detail. A smooth curve would suggest that failures accumulate gradually and can be managed incrementally. A sharp threshold would mean that below a certain specification quality, models of entirely different architectures and training regimes will cluster on the same errors, and no amount of family diversity will save you. That is a different risk profile entirely, and it has direct implications for how we design evaluation suites and how we write prompts for production systems. This connects to adjacent work we have covered on Exploring Real-World Computer Vision, where deployment constraints often force underspecification in practice, and on Beyond MSE, where the choice of evaluation metric changes what we believe about model behavior.
The honest answer to the poster is that the measurement likely does not exist yet in a clean, published form. That is not a failure of the question, it is an opportunity. The field has been slow to treat ambiguity as a quantifiable property rather than a subjective inconvenience. But the absence of a metric does not mean the phenomenon is absent. It means we are flying blind in a way that the poster has been sharp enough to notice. We would tell anyone asking this question to start with a simple proxy: measure inter-annotator agreement on what the task is asking for, then compare that against the rate of identical errors across families. It is not perfect, but it would give you a first number.
The threshold question is the one to watch. If the relationship is smooth, we can chip away at ambiguity incrementally. If it is sharp, then there is a critical specification quality below which model diversity offers no safety in numbers, and the entire practice of ensemble evaluation needs to be rethought. That is a concrete consequence worth chasing. We would tell the poster to run the experiment, publish the raw numbers, and let the shape of that curve speak for itself. That curve, not another benchmark leaderboard, is what will tell us whether we are building toward robustness or just measuring our luck.