Narrative comprehension on long stories and novels has been a challenging domain attributed to their intricate plotlines and entangled, often evolving relations among characters and entities. Given the LLM's diminished reasoning over extended context and its high computational cost, retrieval-based approaches remain a pivotal role in practice. However, traditional RAG methods could fall short due to their stateless, single-step retrieval process, which often overlooks the dynamic nature of capturing interconnected relations within long-range context. In this work, we propose ComoRAG, holding the principle that narrative reasoning is not a one-shot process, but a dynamic, evolving interplay between new evidence acquisition and past knowledge consolidation, analogous to human cognition on reasoning with memory-related signals in the brain. Specifically, when encountering a reasoning impasse, ComoRAG undergoes iterative reasoning cycles while interacting with a dynamic memory workspace. In each cycle, it generates probing queries to devise new exploratory paths, then integrates the retrieved evidence of new aspects into a global memory pool, thereby supporting the emergence of a coherent context for the query resolution. Across four challenging long-context narrative benchmarks (200K+ tokens), ComoRAG outperforms strong RAG baselines with consistent relative gains up to 11% compared to the strongest baseline. Further analysis reveals that ComoRAG is particularly advantageous for complex queries requiring global comprehension, offering a principled, cognitively motivated paradigm for retrieval-based stateful reasoning.

Lumbar disc herniation, as one of the most common spinal degeneration diseases, significantly affects the quality of people’s lives. Effective identification and diagnosis of this disease is highly demanded and crucial to improve lumbar disc health care. In this paper, we propose a unified framework for diagnosing multiple lumbar degeneration diseases in MRI. Considering the basis of diagnosis is the accurate lumbar identification of vertebrae and discs, we thus tailor an anatomical knowledge-based process to identify the index of the detected vertebrae and discs. Specifically, the main difficulty of diagnosis lies in the accurate classification of the disc degenerative level if only one view of MRI is available. To combat this problem, we introduce multi-view and multi-scale MRI clues to the model learning, and equip our framework with a context-guided multi-view feature fusion module to fully exploit spatial-correlations and semantic-correlations in multi-view MRI, leading to significant improvements of diagnosis. Extensive results on two public datasets demonstrate the superiority of our proposed framework over the existing competitives in terms of lumbar localization, identification, and diagnosis.