Key Information Extraction (KIE) in visually-rich documents is inherently token-centric, yet prevailing multimodal encoders often fuse dense visual patches with text tokens indiscriminately, which can introduce low-density visual noise, intensify modality competition, and cause robustness collapse under distribution shifts. We propose OTCR, a lightweight and architecture-agnostic framework that turns vision from a competitor into a selective supporter for extraction. OTCR learns sparse, interpretable cross-modal coupling via optimal transport to route local visual evidence to the most relevant text tokens, applies token-level gating to control injection strength, and further suppresses spurious correlations through a variational information bottleneck. Experiments on FUNSD, CORD, and SROIE show consistent gains when OTCR is plugged into LayoutLMv3 and GeoLayoutLM, and ablations verify the complementary contributions of coupling, gating, and bottlenecking. Under distribution shifts from Do-GOOD and EC-FUNSD, OTCR markedly mitigates performance degradation, indicating that controlled visual evidence can effectively compensate when text/layout shortcuts become unreliable.

The rapid advancement of large language models (LLMs) has significantly improved their performance in code generation tasks. However, existing code benchmarks remain static, consisting of fixed datasets with predefined problems. This makes them vulnerable to memorization during training, where LLMs recall specific test cases instead of generalizing to new problems, leading to data contamination and unreliable evaluation results. To address these issues, we introduce DynaCode, a dynamic, complexity-aware benchmark that overcomes the limitations of static datasets. DynaCode evaluates LLMs systematically using a complexity-aware metric, incorporating both code complexity and call-graph structures. DynaCode achieves large-scale diversity, generating up to 189 million unique nested code problems across 4 units of code complexity and 16 types of call graphs. Results on 12 latest LLMs show an average performance drop of 16.8 to 45.7 compared to MBPP+, with performance progressively decreasing as complexity increases. This demonstrates DynaCode’s ability to effectively differentiate model performance based on code complexity and how different parts of a program interact. Our benchmark and evaluation code are available at https://github.com/HWH-2000/DynaCode.