aiqbench analysis
This research introduces Post-Boundary Bridge (PBB), a novel architecture for hybrid Transformers that optimizes local computation by prioritizing within-block modeling while delegating long-range dependencies to global full-attention layers. The approach significantly improves decoding throughput and memory efficiency without sacrificing model quality, offering a practical solution for scaling context-aware AI systems.
Post-Boundary Bridge: Must Local Attention Go Global Between Global Layers?
arXiv:2606.02680v2 Announce Type: replace Abstract: Hybrid Transformers reduce the cost of long-context modeling by combining local attention with periodic full-attention layers. When global communication is already available, however, the best use of local computation remains unclear. We introduce Post-Boundary Bridge (PBB), which preserves causal attention within blocks and adds direct connections across their boundaries. Rather than extending the range of information relayed through successive local layers, PBB prioritizes within-block modeling and nearby exchange, leaving long-range communication to full-attention layers. Across dense and mixture-of-experts models with 205 million to 2.07 billion stored parameters, PBB hybrids retain near-Full perplexity and competitive performance on standard downstream benchmarks while improving controlled source retrieval. At 205 million parameters, PBB also matches a hybrid using sliding-window attention (SWA) in perplexity and achieves higher source-retrieval accuracy. The same boundary-aligned structure enables Flash-PBB, an exact decoding implementation that updates its key-value cache without relocating retained entries. Compared with Flash-SWA, Flash-PBB delivers 1.82x decode attention-core throughput with half the allocated local key-value cache. These results show that targeted boundary exchange can preserve model quality while enabling faster, more memory-efficient local attention between global layers.
Title:Post-Boundary Bridge: Must Local Attention Go Global Between Global Layers?
Abstract:Hybrid Transformers reduce the cost of long-context modeling by combining local attention with periodic full-attention layers. When global communication is already available, however, the best use of local computation remains unclear. We introduce Post-Boundary Bridge (PBB), which preserves causal attention within blocks and adds direct connections across their boundaries. Rather than extending the range of information relayed through successive local layers, PBB prioritizes within-block modeling and nearby exchange, leaving long-range communication to full-attention layers. Across dense and mixture-of-experts models with 205 million to 2.07 billion stored parameters, PBB hybrids retain near-Full perplexity and competitive performance on standard downstream benchmarks while improving controlled source retrieval. At 205 million parameters, PBB also matches a hybrid using sliding-window attention (SWA) in perplexity and achieves higher source-retrieval accuracy. The same boundary-aligned structure enables Flash-PBB, an exact decoding implementation that updates its key-value cache without relocating retained entries. Compared with Flash-SWA, Flash-PBB delivers 1.82x decode attention-core throughput with half the allocated local key-value cache. These results show that targeted boundary exchange can preserve model quality while enabling faster, more memory-efficient local attention between global layers.
Machine Learning (cs.LG)
arXiv:2606.02680 [cs.LG]
(or arXiv:2606.02680v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.02680 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 282 words