摘要：由大语言模型（LLM）驱动的智能体系统通常具备两项关键机制，可自主解决复杂问题：一是从过往经验中综合基于文本的知识与过程；二是为反复出现的子目标构建参数化（权重空间）技能库。迄今为止，研究大多将二者视为相互独立的方向：要么通过组合与反思来组织文本知识，要么通过权重空间合并来巩固参数化技能。因此，面向特定性能改进、将文本与模型权重无缝整合的研究仍基本未被探索。本研究通过将模型权重视为 LLM 能够原生推理的另一种模态，弥合了这一模态鸿沟。我们通过 prefix-tuning 实现实例化的参数化学习，并增强 LLM，使其能够摄取前缀权重以及捕捉目标能力相关关系的丰富文本数据。我们将这一增强后的 LLM 称为 SkillSmith；它对这些输入进行综合，以执行由指令引导的参数化合成，并直接输出体现目标技能的新前缀权重。我们证明，该方法显著优于纯文本和纯权重空间两类基线，解锁了单模态（仅文本或仅权重）适配方法无法实现的性能提升。

Abstract:Agentic systems driven by large language models (LLMs) regularly feature two key mechanisms to autonomously solve complex problems: synthesizing text-based knowledge and procedures from past experiences and building parametric (weight-space) skill libraries for recurring sub-goals. To date, research has largely treated these as orthogonal pursuits: either organizing textual knowledge through composition and reflection, or consolidating parametric skills via weight-space merging. Consequently, the seamless integration of text and model weights for targeted performance improvements remains largely unexplored. This work bridges this modality gap by treating model weights as an additional modality that an LLM can natively reason over. We instantiate parametric learning via prefix-tuning and augment an LLM to ingest both prefix weights and rich textual data which capture relationships to a target capability. Our augmented LLM, which we call SkillSmith, synthesizes these inputs to perform instruction-steered parametric synthesis, directly outputting new prefix weights that manifest the target skill. We demonstrate that our approach significantly outperforms both text-only and weight-space-only baselines, unlocking performance gains that are out of reach for uni-modal (text-only or weight-only) adaptations.