Frontiers in neurorobotics · 2026
Brain-inspired cognitive state modeling of complex English discourse reading: an LLM-parameterized hierarchical semi-Markov approach.
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Xie Bing, Hu Yongjin, Zhao Linlin
Аннотация
INTRODUCTION: Complex English discourse reading requires readers to integrate lexical access, syntactic parsing, local meaning construction, cross-sentence inference, and discourse monitoring over time.
METHODS: We developed a brain-inspired, large-language-model-parameterized hierarchical hidden semi-Markov model that encodes lexical-, sentence-, and discourse-level representations and maps contextual embeddings to state-emission, transition, and duration parameters. Discourse-structure and cognitive-load constraints were incorporated into global decoding.
RESULTS: Experiments on 1,200 English reading passages achieved a macro-F1 of 0.860, a boundary accuracy of 0.83, a duration MAE of 0.52, and a discourse-consistency correlation of 0.76.
DISCUSSION: The results suggest that duration-aware latent-state modeling can provide an interpretable computational account of adaptive discourse comprehension while preserving explicit temporal structure.
METHODS: We developed a brain-inspired, large-language-model-parameterized hierarchical hidden semi-Markov model that encodes lexical-, sentence-, and discourse-level representations and maps contextual embeddings to state-emission, transition, and duration parameters. Discourse-structure and cognitive-load constraints were incorporated into global decoding.
RESULTS: Experiments on 1,200 English reading passages achieved a macro-F1 of 0.860, a boundary accuracy of 0.83, a duration MAE of 0.52, and a discourse-consistency correlation of 0.76.
DISCUSSION: The results suggest that duration-aware latent-state modeling can provide an interpretable computational account of adaptive discourse comprehension while preserving explicit temporal structure.