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KU Leuven
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The hardest AI tasks remain largely unsolved, with current models achieving only a 2.6% success rate on economically valuable workflows.
MLLMs can achieve 10% gains on multimodal reasoning benchmarks by using ground-truth anchored data curation and scaffold-stripping to avoid cognitive drift during self-evolution.
ML evaluation harnesses, the unsung heroes of model development, are plagued by surprisingly mundane software engineering issues like missing documentation and unimplemented features, hindering reliable assessment.
Cats are helping AI researchers: a Bayesian-inspired model that treats context as a prior significantly improves intent inference for non-speaking agents and avoids shortcut biases.
