Why the usual healing methods fall short here Our approach Results QAH against QAT, head to head What this changes in practice Making a large language model smaller almost always comes with a cost. The now-standard recipe for efficient deployment is to compress the architecture first, cutting the parameter count by removing layers, heads, or neurons, and then quantize the remaining weights down to 4 bits to shrink memory and compute further. Both steps save a lot, but together they systematically degrade the capabilities people actually care about: reasoning, mathematical problem-solving, and code generation. Because of this, serious deployment pipelines add a recovery step, usually called healing, before the model goes into production. Recent open-weight releases such as gpt-oss, NVIDIA's Nemotron family, and our own Hypernova 60B all rely on some version of this compress-then-heal approach.