ResearchResearch paperLarge Language Models · Training & Scaling · Efficiency & Inference1 source · Oct 7, 2026

CHASE: Channel-Aligned Structure Exploitation for Geometry-Aware Model Engineering

Geometric and Spectral Alignment (GSA) characterizes trained networks through spectral concentration, physical-channel alignment, support structure, and changes in singular bases.

Key points

  • In this paper, we propose CHASE (Channel-Aligned Structure Exploitation) to use these structures in practical model design.
  • CORA, COEC, and CORAM apply GSA to parameter-efficient finetuning, structured-pruning compensation, and model merging.
  • CAGA uses GSA to identify multi-head attention heads that can share a KV representation and constructs the shared key and value heads through geometric alignment and low-rank subspace extraction.
  • Experiments on CAGA show that geometric shared-head construction substantially improves MHA-to-GQA conversion, and SAKV and CAPS improve over representative baselines for KV-cache compression and structured pruning.

Sources (1)

  • [1]CHASE: Channel-Aligned Structure Exploitation for Geometry-Aware Model Engineering
    arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 7, 05:33 AM
    Geometric and Spectral Alignment (GSA) characterizes trained networks through spectral concentration, physical-channel alignment, support structure, and changes in singular bases.
    In this paper, we propose CHASE (Channel-Aligned Structure Exploitation) to use these structures in practical model design.

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