谷歌DeepMind新研究：SkillSmith将模型权重作为模态实现技能合成

New research from Google DeepMind.

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SkillSmith treats model weights as an additional modality the LLM reads natively. The augmented model ingests existing prefix weights alongside rich text describing how a capability relates to a target, then directly outputs new prefix weights that manifest that skill.

Skill composition becomes an inference-time operation instead of a training run. The team calls this instruction-steered parametric synthesis.

The gains exceed what text-only and weight-only adaptation reach on their own.

Paper: https://arxiv.org/abs/2607.27497

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