• fine-tuning — Taking a trained model and training it a bit more on your own examples so it gets better at one specific job.

If you rely on unlearning for safety or compliance, clean outputs aren't enough — residual internal accessibility predicts how quickly removed knowledge comes back under further fine-tuning.

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• articleRepresented but Ignored: A Causal Account of Prosodic Underuse in Audio-Language ModelsLinkai Peng, Baorian Nuchged
• articleBeyond the Traceback: Using LLMs for Adaptive Explanations of Programming ErrorsAlexandru-Radu Moraru, Shreyan Biswas, Ujwal Gadiraju

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