Soft-NBCE: Entropy-Weighted Chunk Fusion for Long-Context
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. We present Soft-NBCE, a lightweight extension that replaces discrete chunk selection with soft entropy-weighted chunk fusion. A temperature-scaled Softmax over predictive entropies assigns continuous weights to all chunks, ena...
| Authors | Shihao Ji, Mingyu Li, Zihui Song |
|---|---|
| Primary category | cs.LG |
| Published | 31 May 2026 |
| Last updated at source | 22 August 2026 |
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