Naturalistic language comprehension often involves interpretations that go beyond literal meaning. In continuous narratives, literal and non-literal meanings are tightly intertwined, making them difficult to distinguish computationally. Here, we combined literal sentence representations and human-annotated non-literal interpretations for model-brain alignment. Using fMRI data recorded during passive listening to the Chinese version of The Little Prince, we annotated sentences containing non-literal meaning with human-written interpretations of their implied meaning. We then derived the literal and non-literal representations from LLaMA3.1-8B and evaluated their correspondence with neural activity using whole-brain encoding models. Literal representations aligned strongly with left-lateralized frontotemporal regions, whereas non-literal interpretations showed broader right-hemisphere involvement. Combining the two further improved encoding performance in the bilateral temporal and dorsal frontal cortices, suggesting that naturalistic comprehension engages complementary levels of meaning.

Large language models (LLMs) face a significant challenge due to the excessive computational and memory requirements of the commonly used Transformer architecture. While state space model (SSM) is a new type of foundational network architecture offering lower computational complexity, their performance has yet to fully rival that of Transformers. This paper introduces DenseSSM, a novel approach to enhance the flow of hidden information between layers in SSMs. By selectively integrating shallow-layer hidden states into deeper layers, DenseSSM retains fine-grained information crucial for the final output. This incremental improvement maintains the training parallelizability and inference efficiency of SSMs while significantly boosting performance. The proposed method is broadly applicable to various SSM types, including RetNet and Mamba, and DenseSSM achieves significant performance improvements on public benchmarks, demonstrating its effectiveness and versatility.