arXiv：cs.CL· Masaaki Nakatsu (AO, Inc. / OrbLabs AG), Reno Wang (AO, Inc.)·· 3 小时前AI 评分44
将逻辑与人设解耦：边缘 LLM 智能体对上下文污染的结构性免疫
Decoupling Logic from Persona: Structural Immunity of Edge LLM Agents to Context Pollution
AI 导读
研究提出解耦架构 AO-DA，将逻辑推理（"What"）与人设表达（"How"）拆分为同一 INT4 基础模型上的两条推理路径，通过可热插拔 LoRA 适配器实现。
正文
Abstract:Small language-model agents on edge devices must hold a persona and reason correctly at once, inside one context window that fills with conversational history and persona instructions. We study what happens to the logical part of such an agent when that history is long, misleading and persona-heavy (persona-logic interference), and present a Decoupling Architecture (AO-DA) that separates logical inference ("What") from persona expression ("How") into two inference paths on one INT4 base model with hot-swappable LoRA adapters. The logic path receives only the core turn and emits a verifiable structured state (Micro-State); the persona path renders it in character with the full history. In same-base-model ablations on an Apple M2 laptop (Llama-3.1-8B-Instruct and Gemma-3-4B-it, 4-bit; 480 runs over 4 pollution levels x 3 arms x 2 tasks x 2 personas x 5 seeds) we find: (i) the decoupled logic path is structurally invariant to pollution: its prompt stays at 180 (Llama) or 167 (Gemma) tokens while the mixed single-pass prompt grows from 242 to 1,203, and its outputs are byte-identical across levels (40/40); (ii) the mixed single pass degrades monotonically (composite logic score 0.669 to 0.150 on Llama, 0.487 to 0.150 on Gemma), mostly by failing to emit the required structured output (80-95% of runs on Llama, 100% on Gemma at the two highest levels); (iii) with the same pollution fed into the decoupled logic path, the dedicated-adapter, dedicated-format path is still more robust than the single pass on the 8B model (failure 0-20% vs 80-95%; paired $\Delta$ +0.30 to +0.50, Cliff's $\delta$ 0.50-0.85, Holm-adjusted $p \le 0.03$) but not on the 4B model, where both collapse. Separation costs one extra decode on a topic's first turn (28.2 s vs 18.2 s on Llama) and buys persona hot-swapping in 1.7 ms without re-running the logic path. Code, rubric, fixtures, adapters and logs are released.
| Comments: | 28 pages, 3 figures. Experiment code, scoring rubric, pollution fixtures, adapters and run logs are released (see Appendix G) |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.09772 [cs.CL] |
| (or arXiv:2610.09772v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.09772 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Masa Nakatsu [view email]
[v1]
Wed, 7 Oct 2026 09:53:28 UTC (82 KB)
来源：arXiv：cs.CL · arxiv.org