这个框架解决了 LLM 行为研究中一个长期矛盾的谜题——为什么有的实验说模型死记硬背，有的说模型灵活跟随。做 LLM 评测或 prompt 工程的人，看完能更精准地预测模型在知识冲突场景下的行为，建议直接读原文的机制划分部分。
该研究提出一个三机制框架，解释大语言模型在处理训练知识与上下文文档冲突时的矛盾现象。先前研究结果不一：有的发现模型顽固保留训练答案，有的则发现模型几乎完全遵循上下文。研究者认为这是因为未区分三种不同的处理情境：机制1（单源更新，主导因素为证据连贯性）、机制2（竞争整合，主导因素为参数确定性）、机制3（任务适配选择，主导因素为任务知识需求）。通过 9970 次 API 调用，在 Claude Sonnet 4.6、GPT-5.5、Gemini 2.5 Flash、Llama 4 Maverick 和 DeepSeek V3 上验证了该框架，确认了机制2的确定性梯度，并发现任务框架能将上下文遵循率从近100%翻转至6-71%。
Three Regimes of Context-Parametric Conflict: A Predictive Framework and Empirical Validation
The literature on how large language models handle conflict between their training knowledge and a contradicting document presents a persistent empirical contradiction: some studies find models stubbornly retain their trained answers, ignoring provided documents nearly half the time, while others find models readily defer to the document, following context approximately 96% of the time. We argue these contradictions dissolve once one recognises that prior experiments have studied three qualitatively distinct processing situations without distinguishing them. We propose a three-regime framework: Regime 1 (single-source updating, dominant predictor: evidence coherence), Regime 2 (competitive integration, dominant predictor: parametric certainty), and Regime 3 (task-appropriate selection, dominant predictor: task knowledge requirement). We formalise a distinction between parametric strength (exposure frequency) and parametric uniqueness (encoding consistency), showing empirically that these are orthogonal dimensions (r = -0.002, p = .97) with strength as the operative predictor in stable factual domains. We validate the framework across Claude Sonnet 4.6, GPT-5.5, Gemini 2.5 Flash, Llama 4 Maverick, and DeepSeek V3 using 9,970 API calls in three experimental phases. GEE logistic regression confirms the predicted Regime 2 certainty gradient for all five models (beta = -0.38 to -0.50, all p <= .013, BH-FDR corrected). A Regime 3 ablation shows task framing alone flips context-following from near-100% (contextual knowledge condition) to 6-71% (parametric knowledge condition), with all five models significant (p < .001). The certainty gradient is robust to multinomial outcome modeling, sensitivity analyses for hedging responses, and FDR correction.