Hydra: A Modular Architecture for Efficient Long-Context Reasoning

TL;DR

Hydra is a modular transformer architecture that improves long-context reasoning efficiency and accuracy by adaptively combining sparse attention, mixture-of-experts, and dual memories, enabling better performance in resource-constrained settings.

Contribution

Hydra introduces a novel modular architecture with adaptive routing among multiple efficiency mechanisms for improved long-context reasoning.

Findings

Hydra achieves over 3x throughput gains at 8K tokens.

Hydra improves multi-step logical reasoning accuracy by 10x.

Ablation studies confirm the effectiveness of each component.

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.