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arXiv · 2508.15099

Hydra: A Modular Architecture for Efficient Long-Context Reasoning

Siddharth Chaudhary·Dev Patel·Maheep Chaudhary·Bennett Browning
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Abstract

The quadratic complexity of transformers fundamentally limits reasoning system deployment in resource-constrained and long-context settings. We introduce Hydra, a modular architecture based upon a state-space backbone which adaptively routes between complementary efficiency mechanisms: sparse global attention, mixture-of-experts, and dual memories comprising a reasoning workspace and product key memory. We evaluate a 29M parameter model measuring logical chaining accuracy and throughput on synthetic sequences, plus throughput on WikiText. Ablation studies use component-specific synthetic datasets to isolate individual mechanisms. Hydra achieves $3.01\times$ and $3.0\times$ throughput gains at 8K tokens for synthetic and WikiText datasets, respectively, and $10\times$ accuracy improvements on multi-step logical composition compared to equal-sized transformers. Ablations confirm each component's contribution: sparse attention captures long-range dependencies, experts specialize to input domains, and product key memory enables selective retrieval.

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Siddharth Chaudhary, Dev Patel, Maheep Chaudhary, Bennett Browning. 2025-08-20. Hydra: A Modular Architecture for Efficient Long-Context Reasoning. https://arxiv.org/abs/2508.15099

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