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MACRO: Markov Chain Routing of Transformer Layers

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Merged summary

TL;DR - MACRO is a training-free framework that learns task-specific execution routes through a frozen LLM's transformer layers, modeling routing as a context-dependent Markov policy. It matters because it extracts meaningful accuracy gains without touching model weights or running per-instance search.

  • Routes are conditioned on layer index, computation-budget phase, directional displacement, and operator context, supporting skip, repeat, and residual hidden-state addition moves.
  • The Markov route distribution is updated from training-data feedback and decoded with a top-k Viterbi algorithm to extract high-probability candidate "programs."
  • Reported results: +5.0% average accuracy over unrouted baselines across reasoning and knowledge benchmarks on multiple open-weight LLMs, with the largest gains on small models.
  • Beats prior dynamic-routing method Dr. LLM by +7.2% while cutting route-search time 9.4x (14.8 → 1.6 hours); code released at github.com/Batorskq/MACRO.

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MACRO: Markov Chain Routing of Transformer Layers

arXiv cs.CL Paweł Batorski, Abtin Pourhadi, Akylgali Aitaza, Przemysław Spurek, Paul Swoboda 2026-08-06 arXiv:2608.05872
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-09-02 14:25:27.225837 UTC

TL;DR - MACRO is a training-free framework that learns task-specific execution routes through a frozen LLM's transformer layers, modeling routing as a context-dependent Markov policy. It matters because it extracts meaningful accuracy gains without touching model weights or running per-instance search.

  • Routes are conditioned on layer index, computation-budget phase, directional displacement, and operator context, supporting skip, repeat, and residual hidden-state addition moves.
  • The Markov route distribution is updated from training-data feedback and decoded with a top-k Viterbi algorithm to extract high-probability candidate "programs."
  • Reported results: +5.0% average accuracy over unrouted baselines across reasoning and knowledge benchmarks on multiple open-weight LLMs, with the largest gains on small models.
  • Beats prior dynamic-routing method Dr. LLM by +7.2% while cutting route-search time 9.4x (14.8 → 1.6 hours); code released at github.com/Batorskq/MACRO.
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