arXiv cs.AI· Ekrem Aksoy·· 10 小时前AI 评分31
W-ISA：面向预训练语言模型的权重空间干预程序事务化执行框架
W-ISA: Transactional Execution of Weight-Space Intervention Programs
AI 导读
W-ISA 是一套用于在预训练语言模型上执行权重空间干预程序的软件指令集与事务化运行时，支持参数更新、投影、消融和 rank-one 变换等形状校验指令，并在请求级执行上下文中于完成或中断后恢复已保存参数。
正文 · 原文
Abstract:We present W-ISA, a software instruction set and transactional runtime for executing weight-space intervention programs on pretrained language models. Shape-checked instructions express parameter updates, projections, ablations, and rank-one transformations. The runtime applies these programs within request-scoped execution contexts and restores saved parameters after completion or interruption. Activation-based match-action rules connect measured model states to intervention selection. We evaluate restoration and replay on SmolLM-135M-Instruct and Qwen2.5-1.5B-Instruct, and compare materialized rank-one updates with equivalent PyTorch hooks on the latter. In 45 timed requests, both intervention implementations produce identical greedy sequences at 32, 128, and 512 tokens; their maximum prompt-logit difference is 0.000184. On the complete WikiText-2 raw test split, the combined SmolLM attention-head ablation and MLP projection changes perplexity from 22.2353 to 22.3188, a relative increase of 0.3754%. W-ISA separates intervention specification, execution, and measurement, making parameter-level experiments reproducible and composable.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.13232 [cs.AI] |
| (or arXiv:2611.13232v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.13232 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ekrem Aksoy [view email]
[v1]
Mon, 9 Nov 2026 21:01:51 UTC (27 KB)
来源：arXiv cs.AI · arxiv.org