Dense large language models (LLMs) face critical efficiency bottlenecks, as they rigidly activate all parameters regardless of input complexity. While existing sparsity methods (static pruning or dynamic activation) partially address this issue, they either lack adaptivity to contextual or model structural demands or incur prohibitive computational overhead. Inspired by the human brain’s dual-process mechanisms — predictive coding (N400) for backbone sparsity and structural reanalysis (P600) for complex contexts — we propose CLADA, a Cognitive-Load-Aware Dynamic Activation framework that synergizes statistical sparsity with semantic adaptability.Our key insight is that LLM activations exhibit two complementary patterns: 1. Global Statistical Sparsity driven by sequence-level prefix information, and 2. Local Semantic Adaptability modulated by cognitive load metrics (e.g., surprisal and entropy). CLADA employs a hierarchical thresholding strategy: a baseline derived from offline error-controlled optimization ensures over 40% sparsity, which is then dynamically adjusted using real-time cognitive signals. Evaluations across six mainstream LLMs and nine benchmarks demonstrate that CLADA achieves 20% average speedup with less than 2% accuracy degradation, outperforming Griffin (over 5% degradation) and TT (negligible speedup).Crucially, we establish the first formal connection between neurolinguistic event-related potential (ERP) components and LLM efficiency mechanisms through multi-level regression analysis (R² = 0.17), revealing a sparsity–adaptation synergy. Requiring no retraining or architectural changes, CLADA provides a deployable solution for resource-aware LLM inference while advancing biologically inspired AI design.

"本文聚焦于“叙实性推理”任务,即判断语言中事件真实性的语义理解能力。该任务不依赖外部知识,而基于语言结构本身进行推理,对当前大语言模型(LLMs)提出挑战。为解决模型在叙实性漂移、多义词处理等方面的不足,作者提出一种结合RAG(检索增强生成)与谓词相似性的方法,构建了一个融合参数化与非参数化知识的叙实性检测智能体系统。该系统通过分步提示与知识库支持,实现了更高的一致性、准确性与可解释性,在评测任务中取得了0.9240的稳健表现。"