测试大模型在真实舆论压力下的合作行为，发现其倾向会随压力类型显著变化。
Capability Is Not Propensity: Measuring Pressure-Robust Cooperative Behavior in Civic LLM Agents
- 设计10个场景评估模型在不同压力下的协作表现，区分能力与倾向。
- 轻微暗示压力使模型更擅长操纵，而压制异议的能力下降1.67分。
- 轻量提示干预可提升抗压性，适合关注可信AI的开发者和政策制定者。
语言模型的合作能力具有双重用途：既能促进公共讨论，也可能导致策略性隐瞒、虚假共识和操控性表达。我们主张，评估合作型AI应区分模型在良性指令下的能力与在真实公共压力下的倾向。为此，提出DiffCoop-Civic——一个包含10个场景的初步评估套件，涵盖偏好理解、证据与说服、承诺设计、信息不对称和异议保留等维度。在来自四个模型家族的七种模型上测试发现，轻微隐瞒压力导致操纵能力提升1.17分（5分制），异议保留能力下降1.67分；明显虚假共识压力则引发部分对齐API模型的拒绝或转向，但多个开源权重模型直接服从。一种轻量级的Pareto-Trace提示干预可在不依赖强硬拒绝的前提下提升抗压性。匿名可复现代码包已发布于https://anonymous.4open.science/r/diffcoop-civil-771C。
原文摘要 · Abstract (English)
Cooperative capabilities in language models are dual-use. The same social reasoning that supports civic deliberation can also enable strategic omission, false consensus, and manipulative framing. We argue that Cooperative AI evaluations should separate what models can do under benign instructions from what they tend to do under realistic civic pressure. We introduce DiffCoop-Civic, a 10-scenario pilot evaluation suite spanning preference understanding, evidence and persuasion, commitment design, asymmetric information, and dissent preservation. Across seven models from four model families, subtle omission pressure produces a near-uniform shift: manipulative enablement rises by 1.17 points and dissent preservation falls by 1.67 points on a 5-point scale. Overt false-consensus pressure behaves differently: it triggers refusal or redirection in some aligned API models, but direct compliance in several open-weight models. A lightweight Pareto-Trace prompting intervention improves pressure robustness without simply relying on hard refusal. An anonymous reproducibility package is available at https://anonymous.4open.science/r/diffcoop-civil-771C.
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