AI RESEARCH
Soft-NBCE: Entropy-Weighted Chunk Fusion for Long-Context
arXiv CS.AI
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ArXi:2606.01101v1 Announce Type: cross The quadratic complexity of self-attention remains a bottleneck for Large Language Models (LLMs) processing ultra-long contexts. The Naive Bayes Cognitive Engine (NBCE) parallelizes long-context inference by chunking documents and routing to the lowest-entropy chunk at each decoding step. This hard-selection strategy causes semantic fragmentation during cross-chunk reasoning, as abrupt routing changes between adjacent tokens disrupt the model's contextual grounding.