MATH · IN · MODELS

In-context conflict shows architecture-dependent dilution but universal orthogonal interference

measured in 1 paper

Zhang & Lin inject counterfactual contexts into MMLU questions and decompose the residual-stream update into a radial (norm-ratio) and an angular (cosine to the correct-answer direction) component [zhang-lin-2026-simulated-adoption-decoupling-magnitude-and-direction-in-llm-in-context-conflict-resolution] Radial "Manifold Dilution" is architecture-dependent: only Llama-3.1-8B shows real dilution (gamma=0.978) while Qwen3-4B and GLM-4-9B show none despite equal logit collapse [zhang-lin-2026-simulated-adoption-decoupling-magnitude-and-direction-in-llm-in-context-conflict-resolution] Angular "Orthogonal Interference" holds universally: interference-to-correct-answer cosine clusters near zero across all three models, not antiparallel suppression [zhang-lin-2026-simulated-adoption-decoupling-magnitude-and-direction-in-llm-in-context-conflict-resolution] Regressing angular deviation against logit drop gives R^2=0.90 (Qwen) and 0.87 (GLM) [zhang-lin-2026-simulated-adoption-decoupling-magnitude-and-direction-in-llm-in-context-conflict-resolution]

Context

knowledge-conflict, in-context-learning

Papers

Simulated Adoption: Decoupling Magnitude and Direction in LLM In-Context Conflict Resolution — Zhang, Long, Lin, Fangwei2026 · arXiv:2602.04918