这篇论文给出了一个不依赖真实参数对的新方法MA-SBI，用文本作为侧信道校正模拟器错误，在多个基准上比当前最好的RoPE还强，而且理论也扎实。
MA-SBI 提出通过侧信道文本（如制度标签或政策公告）校正模拟器误指定，无需真实参数对。理论证明误指定校正的偏差减少受侧信道与误指定互信息上界约束，且对次高斯噪声非平凡。在隐藏校准基准上，仅使用文本的 MA-SBI 在 10 个种子和两个骨干上达到与原 Oracle 后验的 TOST 等价，而 RoPE 即使使用更多数据也未实现。在真实 COVID 和 OxCGRT 流行病学数据上，随机变体改进了后验预测对数似然，并在良好指定的认知科学语料上正确保持后验不变。
MA-SBI: Misspecification-Aware Simulation-Based Inference via Side-Channel Guidance
Simulation-based inference (SBI) of latent parameters is often hindered by simulator misspecification, the mismatch between simulated and real-world observations caused by inherent modeling simplifications. RoPE, the recent state-of-the-art for robust SBI, addresses this through optimal transport between learned representations of real and simulated observations, but requires ground-truth parameter calibration pairs that are typically unavailable in the very settings where SBI is needed. What practitioners do have is unstructured side-information such as regime labels, instruction text, and policy bulletins. We propose Misspecification-Aware Simulation-Based Inference (MA-SBI), a calibration-free framework that turns this side-channel into a posterior correction. A learned corrector maps side-channel text to an observation-space shift applied before any pre-trained amortized posterior, requiring no retraining and no parameter ground-truth. Our main theorem bounds achievable bias reduction by the mutual information between misspecification and side-channel, with a non-vacuous constant that extends to all sub-Gaussian noise via Donsker-Varadhan. On hide-the-calibration benchmarks, MA-SBI with text alone matches the oracle posterior across 10 seeds and two backbones (TOST equivalence), while RoPE given more data does not. The two approaches are complementary: where misspecification is structural and recoverable from parameter pairs, RoPE dominates, as the theory predicts. A stochastic variant improves posterior-predictive log-likelihood on real COVID and OxCGRT epidemiological data, and correctly leaves the posterior unchanged on a well-specified cognitive-science corpus.