AROMA：面向 Llama-3.2-11B-Vision 的冻结 VLM 可控干预框架
AROMA: Adaptive Representation-Oriented Multimodal Actuation for Vision-Language Model Control
用 Llama-3.2-11B-Vision 做的表示干预研究，把数数这类视觉能力拆成定位、干预和目标选择几个环节分别攻克，计数准确率提升 8 个百分点且不动权重。做模型可解释性和干预控制的开发者值得看，比常见的 attribution-steering 方法设计得更系统。
论文提出 AROMA（Adaptive Representation-Oriented Multimodal Actuation），一个在冻结视觉语言模型上做表示干预的控制框架，把干预拆成定位、几何与增益、可达性、目标选择、时机五个独立问题，不修改模型权重。
在 Llama-3.2-11B-Vision-Instruct 上的实验显示核心矛盾：一个因果验证过的 cross-attention 头的四个维度解释了 89.82% 的计数敏感性，却只占 9.52% 的激活能量。作者用幅度归一化的因果子空间干预把这一错配转化为有限控制，在未见过的程序化数据上将精确计数准确率提升 8.00 个百分点。配套的迭代式最小干预策略 MEA 在相同步数预算下命中 81.25% 的 oracle 目标，而冻结方向只有 15.18%。
在目标与门控于评测前冻结的设定下，targeted MEA 在 N=630 的自然 Stage-2 队列上把此前的冻结策略从 34.444% 提升到 38.730%（配对 p=4.63e-7）；在全部 7,992 个样本上为 65.741% 对 65.153%，仅实际编辑 63 个输入。论文还包含跨头与跨模型的复现实验，以及与 attribution-steering 基线的对比。
AROMA: Adaptive Representation-Oriented Multimodal Actuation for Vision-Language Model Control
Mechanistic localization does not by itself yield usable control: causal sensitivity may occupy little activation energy, an accurate local direction may fail at finite scale, and a reachable numeral target may be wrong. We introduce AROMA (Adaptive Representation-Oriented Multimodal Actuation), a frozen-VLM control framework separating where to intervene, which geometry and gain to use, how to reach a target, which target to pursue, and when to act. In Llama-3.2-11B-Vision-Instruct, four dimensions of a causally validated cross-attention head explain 89.82% of cardinality sensitivity but only 9.52% of median activation energy; magnitude-normalized causal subspace actuation converts this mismatch into finite control, improving exact-count accuracy on untouched procedural data by +8.00 pp. Iterative Minimal-Effort Actuation (MEA) reaches 81.25% of oracle targets versus 15.18% for a frozen direction under matched step budgets. With target and gate frozen before evaluation, targeted MEA improves the previous frozen policy from 34.444% to 38.730% on a new natural Stage-2 cohort (N=630; paired p=4.63e-7) and yields 65.741% versus 65.153% over all 7,992 examples while editing only 63 inputs. Prospective routing, replication across heads and models, and comparisons with attribution-steering baselines further characterize each stage, separating localization, actuation, reachability, target quality, and selective deployment as distinct control problems.
本条情报归属 AITOP「论文」核心栏目，重点聚焦北美前沿实验室与硅谷科技巨头。在跨区域产业观察坐标中，该动态直接关联「AI 开发者工具链、Coding Agent 与工程化实践」研究主线，反映出当前技术演进与商业落地的最新风向。涉及核心实体与技术议题包括：Llama-3.2-Vision、VLM、模型可解释性、表示干预，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。
信源核验说明：本文原始线索采编自「arXiv cs.LG」（@Zhaoning Zhang），经 AITOP 多维评分管线结构化提炼。