发表机构
Independent Researcher（独立研究者）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究将同理心重新定义为预测性偏差容忍度，提出解释性错误容忍（IET）框架，通过两个计算探针评估。结果发现修复存在依赖机制的结构，揭示噪声水平等因素间相互作用，表明长时间互动中同理心是调节分歧动态，促使共情AI设计转向管理解释距离。
AI 中文摘要
同理心通常被理论化为共鸣：对他人当前情绪或认知状态的镜像反映。这种同步框架塑造了人工系统，其中共情行为被定义为情感识别和反应对齐。我们认为，对于长时间对话来说，这是错误的目标，因为理解是通过预测、分歧和修复随着时间展开的。我们将同理心重新定义为预测性偏差容忍度：即预测并调节跨时间分歧而非消除它的能力。我们将其形式化为解释性错误容忍（IET），这是一种动态阈值启发式方法，将同理心建模为在主体之间维持一个可行的分歧带。我们在受控噪声下用两个计算探针评估这个框架。IET更新规则并不优于固定基线。相反，我们发现了一个强大的依赖机制的结构：修复以保留主旨为代价换取辨别保真度。在低噪声下，修复会降低检索准确性；在高噪声下，它会保留主旨意义，揭示了噪声水平、修复和评估指标之间的相互作用。我们通过IET解释这个结构，表明长时间互动中的同理心不是消除分歧，而是调节其动态变化。这促使共情人工智能设计从趋同转向管理解释距离。
英文摘要
Empathy is most often theorized as resonance: a mirroring of another's present emotional or cognitive state. This synchronic framing has shaped artificial systems, where empathic behavior is defined as affect recognition and response alignment. We argue this is the wrong target for extended dialogue, where understanding unfolds over time through prediction, divergence, and repair. We reframe empathy as predictive misalignment tolerance: the capacity to anticipate and regulate divergence across time rather than collapse it. We formalize this as Interpretive Error Tolerance (IET), a dynamic-threshold heuristic that models empathy as maintaining a viable band of divergence between agents. We evaluate this framework with two computational probes under controlled noise. The IET update rule does not outperform fixed baselines. Instead, we find a robust regime-dependent structure: repair trades discriminative fidelity for gist preservation. At low noise, repair degrades retrieval accuracy; at high noise, it preserves gist meaning, revealing an interaction between noise level, repair, and evaluation metric. We interpret this structure through IET, suggesting that empathy in extended interaction is not eliminating divergence but regulating its dynamics. This motivates a shift in empathic AI design from convergence toward managing interpretive distance.
