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Tuesday, May 19, 2026 - 48 papers
There's a quiet argument running through today's batch of papers, and it goes something like this: the hard problem isn't getting AI to do things, it's getting AI to do things reliably, repeatedly, and in ways you can actually audit afterward. Whether it's agents that accrue reusable skills from past runs, theorem provers that learn from their own failures, healthcare bots that collapse at 28% task completion, or a research assistant that still hallucinates data, the throughline is the same—raw capability keeps outpacing the scaffolding needed to trust it. Code, it turns out, keeps appearing as the answer to that scaffolding problem, which is either reassuring or just kicks the can down the road.
Monday, May 18, 2026 - 43 papers
The recurring finding across today's papers isn't that AI systems fail—it's that they fail in the specific place nobody was measuring. Models cite wrong evidence while getting answers right. Agents violate security boundaries mid-task while producing correct outputs. Robots struggle with generalization in exactly the conditions that weren't in the test set. The pattern is consistent enough to feel almost deliberate: we benchmark the endpoint, then act surprised when the path there was wrong. Several papers exist precisely because someone noticed the gap between "the model got it right" and "the model got it right for the right reasons." Here's what researchers were building—and auditing—this week.
AI Papers Daily. Summaries of the top Hugging Face papers, one email a day.
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