Papers 1
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Savietto, D., Campbell, D., Panisson, A., Nurisso, M., Petri, G., Cohen, J. D., Perotti, A.
We analyze the representational geometry of open-weight vision-language models to understand their failures in multi-object tasks such as hallucinating non-existent elements or failing to identify the most similar objects among distractions. By distilling concept vectors and validating them through steering interventions, we show that geometric overlap between these vectors strongly correlates with specific error patterns.