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Explicit context compilation boosts LLM performance on in-context learning tasks, lifting accuracy from 15.4% to 21.4% on complex benchmarks.
Even the best LLMs fail to produce fully feasible travel plans more than half the time, revealing a significant gap in their ability to understand user needs.
Current dense predictors can deviate by over 3x from accurate predictions when faced with unsupported edge cues, revealing a fundamental flaw in their design.
Current AI agent evaluations are like testing a car only on a straight track; HAAF offers a holistic "wind tunnel" to reveal hidden risks in complex, real-world scenarios.
