arXiv: cs.AI preprintsInternational6 November 2026
When Bigger Becomes Brittle: Why Compression Hurts More as Language Models Scale
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arXiv:2611.07891v1 Announce Type: new Abstract: Quantization and pruning reduce the cost of deploying language models, but their effects are usually reported as benchmark scores. Such scores record only the loss that remains at the output. Inside the model, compression damages some components more than others, and the rest of the network can partly compensate. The observed loss therefore reflects both the damage and this compensation, which benchmark scores cannot separate. How the two combine, and whether their balance changes with model size, remains unclear. We study this compensation for i
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