Large language models (LLMs) acquire most of their knowledge during pretraining, which ties them to a fixed snapshot of the world and makes adaptation to continuously evolving knowledge challenging. As facts, entities, and events change over time, models may experience continuous knowledge drift, resulting not only in outdated predictions but also in temporally inconsistent reasoning. Although existing approaches, such as continual finetuning, knowledge editing, and retrieval-augmented generation (RAG), aim to update or supplement model knowledge, they are rarely evaluated in settings that reflect chronological, evolving, and real-world knowledge evolution. In this work, we introduce a new benchmark of real-world dynamic events, constructed from time-stamped evidence that captures how knowledge evolves over time, which enables systematic evaluation of model adaptation under continuous knowledge drift. The benchmark reveals that most existing methods, including vanilla RAG and several learning-based approaches, struggle under this setting, exposing critical limitations such as catastrophic forgetting and temporal inconsistency. To mitigate these limitations, we propose a time-aware retrieval baseline, Chronos, which progressively organizes retrieved evidence into an Event Evolution Graph to enable more temporally consistent understanding in LLMs without additional training. Overall, this work provides a foundation for analyzing and advancing LLM adaptation to continuous knowledge drift in realistic settings.

Mixture-of-Experts (MoE) based large language models (LLMs) have gained popularity due to their multi-task capability, where each input token activates only a subset of “expert” subnetworks. However, whether each expert can truly specialize to a certain task remains poorly understood, while activation analysis shows frequent cross-layer co-activation of experts for the same input, resembling a collaborative behavior. In this paper, we use a dictionary learning approach to show that experts in MoE LLMs form hierarchical and semantically coherent collaborative groups that correspond to specific linguistic and cognitive functions (e.g., mathematical reasoning, syntactic processing), mirroring specialized functional region observed in neuroscience. Furthermore, leveraging these discovered expert groups enables significant model compression with minimal performance degradation, outperforming existing methods by 2.5% while enabling up to 50% expert reduction. These findings provide the first systematic analysis of expert collaboration mechanisms in MoE LLMs, revealing that specialization emerges from joint activation of experts across all layers. We further developed an interactive visualization platform that enables researchers to explore expert collaboration patterns and their semantic associations.