Omega-S: A Functional Resilience Index for LLM Fine-Tuning

• 类型：arxiv
• 标识：2608.03887
• 链接：https://arxiv.org/abs/2608.03887
• 主分类：engineering
• 形态：method
• 被引：0
• 被引来源：Semantic Scholar
• S2被引：0
• 影响力被引：0
• TLDR：Omega-S, a drop-in penalty computed from the weight matrix alone, is presented, a drop-in penalty computed from the weight matrix alone that needs no previous-task data, no Fisher matrix and no stored copy of the old weights and adds under 4% to the cost of a step.
• 待LLM分类：否
• 标题中文：Omega-S: A Functional Resilience Index for LLM Fine-Tuning
• TLDR中文：本文提出 Omega-S——一种仅由权重矩阵计算得到的即插即用惩罚，无需先前任务数据、无需 Fisher 矩阵、无需保存旧权重副本，且单步开销不足 4%。
• 来源文件：
• /inbox/tom/_candidates/2026-08-12-agent-rag-longcontext-candidates.json
• [S2 enrich]