Just How Resilient Are Large Language Models? (www.rdrocket.com)

Researchers and engineers are discovering that large language models (LLMs) are surprisingly resilient to random parameter corruption — even single-bit flips from cosmic rays or deliberate reduction in numerical precision. Experiments show billion-parameter models can tolerate thousands or tens of thousands of randomly corrupted weights and still produce coherent text and correct answers. This robustness emerges because training creates massively redundant, overlapping representations: concepts like “cat” are encoded across many parameters. But importance is unevenly distributed — output layers, attention mechanisms and early input-processing layers act like critical hubs, so corruption there degrades expression, context-following or foundational representations more severely. The practical and technical implications are twofold. Positively, fault tolerance explains why cloud-run models survive hardware faults and why aggressive quantization (e.g., 32-bit → 8-bit) often preserves performance, enabling cheaper, edge and space deployments. On the other hand, targeted parameter corruption can create subtle backdoors or trigger catastrophic failure modes such as mode collapse (repetitive/nonsensical output). Understanding the “geography of importance” in networks guides new architectures and defenses that maximize resilience while mitigating adversarial or surgical attacks — and hints that both artificial and biological intelligence rely on graceful degradation through redundancy.