Antioch Agent is the product name attached to the most interesting idea in robotics tooling this year: describe a test in plain language, and a machine builds the digital twin, writes the scenarios, runs them by the thousand, and reports what broke. Antioch, the New York company behind it, raised a $32 million Series A […]
KAIST has published two methods for cutting sensory hallucinations in multimodal AI: the failure where a model misreads what a sensor physically reports, or invents a perception in one channel because another channel suggested it. DNA optimisation teaches vision-language models the physics of thermal, depth and X-ray sensors using their own wrong answers as the training signal. MAD suppresses cross-modal interference at decoding time with no retraining at all. Here is what each method fixes, what the reported numbers do and do not establish, where sensory hallucinations cost the most in production, and what this line of work still leaves unsolved.
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