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Task-Conditioned Routing Signatures in Sparse Mixture-of-Experts Transformers

arXiv:2603.11114v1 Announce Type: new Abstract: Sparse Mixture-of-Experts (MoE) architectures enable efficient scaling of large language models through conditional computation, yet the routing mechanisms responsible for expert selection remain poorly understood. In this work, we introduce routing signatures, a vector representation summarizing expert activation patterns across layers for a given prompt, and use them to study whether MoE routing exhibits task-conditioned structure. Using OLMoE-1B-7B-0125-Instruct as an empirical testbed, we show that prompts from the same task category induce highly similar routing signatures, while prompts from different categories exhibit substantially lower similarity. Within-category routing similarity (0.8435 +/- 0.0879) significantly exceeds across-category similarity (0.6225 +/- 0.1687), corresponding to Cohen's d = 1.44. A logistic regression classifier trained solely on routing signatures achieves 92.5% +/- 6.1% cross-validated accuracy on fou

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Mynampati Sri Ranganadha Avinash · · 1 min read · 8 views
#cs.LG #cs.AI

arXiv:2603.11114v1 Announce Type: new Abstract: Sparse Mixture-of-Experts (MoE) architectures enable efficient scaling of large language models through conditional computation, yet the routing mechanisms responsible for expert selection remain poorly understood. In this work, we introduce routing signatures, a vector representation summarizing expert activation patterns across layers for a given prompt, and use them to study whether MoE routing exhibits task-conditioned structure. Using OLMoE-1B-7B-0125-Instruct as an empirical testbed, we show that prompts from the same task category induce highly similar routing signatures, while prompts from different categories exhibit substantially lower similarity. Within-category routing similarity (0.8435 +/- 0.0879) significantly exceeds across-category similarity (0.6225 +/- 0.1687), corresponding to Cohen's d = 1.44. A logistic regression classifier trained solely on routing signatures achieves 92.5% +/- 6.1% cross-validated accuracy on four-way task classification. To ensure statistical validity, we introduce permutation and load-balancing baselines and show that the observed separation is not explained by sparsity or balancing constraints alone. We further analyze layer-wise signal strength and low-dimensional projections of routing signatures, finding that task structure becomes increasingly apparent in deeper layers. These results suggest that routing in sparse transformers is not merely a balancing mechanism, but a measurable task-sensitive component of conditional computation. We release MOE-XRAY, a lightweight toolkit for routing telemetry and analysis.

Executive Summary

This article introduces routing signatures, a vector representation of expert activation patterns in Sparse Mixture-of-Experts (MoE) transformers. The authors demonstrate that prompts from the same task category induce similar routing signatures, while those from different categories exhibit lower similarity. This suggests that routing in sparse transformers is a task-sensitive component of conditional computation, rather than just a balancing mechanism. The authors release MOE-XRAY, a toolkit for routing telemetry and analysis, and achieve 92.5% accuracy on four-way task classification using routing signatures.

Key Points

  • ▸ Introduction of routing signatures to study MoE routing mechanisms
  • ▸ Task-conditioned structure in MoE routing, with similar routing signatures for prompts from the same task category
  • ▸ Development of MOE-XRAY, a lightweight toolkit for routing telemetry and analysis

Merits

Innovative Methodology

The authors propose a novel approach to analyzing MoE routing mechanisms, providing new insights into the conditional computation of large language models

Empirical Validation

The authors provide robust empirical evidence to support their claims, using a large-scale dataset and achieving high accuracy on task classification

Demerits

Limited Generalizability

The study focuses on a specific MoE architecture and dataset, which may limit the generalizability of the findings to other models and domains

Expert Commentary

The study provides a significant contribution to the understanding of MoE routing mechanisms, highlighting the importance of task-conditioned structure in conditional computation. The development of MOE-XRAY as a lightweight toolkit for routing telemetry and analysis is also a notable achievement. However, further research is needed to explore the generalizability of the findings to other models and domains. The study's implications for efficient scaling and explainability in AI are substantial, and the authors' innovative methodology and empirical validation make this a compelling and impactful piece of research.

Recommendations

  • ✓ Further research to explore the generalizability of the findings to other MoE architectures and datasets
  • ✓ Investigation into the potential applications of task-conditioned routing mechanisms in areas such as natural language processing and human-computer interaction

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