Summary
Current neural networks confidently hallucinate when given garbage data because the standard Cross-Entropy loss creates a jagged latent space, leaving no mathematical 'place' for uncertain inputs. This fundamental geometry problem prevents models from admitting they don't know. The author is developing a 'HALO-Loss' to fix this, enabling neural networks to abstain and improve reliability.
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