Beyond Redundancy: Diverse and Specialized Multi-Expert Sparse Autoencoder

• URL: http://arxiv.org/abs/2511.05745v1
• Date: Fri, 07 Nov 2025 22:19:34 GMT
• Title: Beyond Redundancy: Diverse and Specialized Multi-Expert Sparse Autoencoder
• Authors: Zhen Xu, Zhen Tan, Song Wang, Kaidi Xu, Tianlong Chen,
• Abstract summary: Sparse autoencoders (SAEs) have emerged as a powerful tool for interpreting large language models.<n>SAEs' hidden layers have high dimensionality to satisfy sparsity constraints, resulting in prohibitive training and inference costs.<n>Recent Mixture of Experts (MoE) approaches attempt to address this by SAEs into narrower expert networks with gated activation.<n>We propose two key innovations: (1) Multiple Expert Activation that simultaneously engages semantically weighted expert subsets to encourage specialization, and (2) Feature Scaling that enhances diversity through adaptive high-frequency scaling.
• Score: 59.89996751196727
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sparse autoencoders (SAEs) have emerged as a powerful tool for interpreting large language models (LLMs) by decomposing token activations into combinations of human-understandable features. While SAEs provide crucial insights into LLM explanations, their practical adoption faces a fundamental challenge: better interpretability demands that SAEs' hidden layers have high dimensionality to satisfy sparsity constraints, resulting in prohibitive training and inference costs. Recent Mixture of Experts (MoE) approaches attempt to address this by partitioning SAEs into narrower expert networks with gated activation, thereby reducing computation. In a well-designed MoE, each expert should focus on learning a distinct set of features. However, we identify a \textit{critical limitation} in MoE-SAE: Experts often fail to specialize, which means they frequently learn overlapping or identical features. To deal with it, we propose two key innovations: (1) Multiple Expert Activation that simultaneously engages semantically weighted expert subsets to encourage specialization, and (2) Feature Scaling that enhances diversity through adaptive high-frequency scaling. Experiments demonstrate a 24\% lower reconstruction error and a 99\% reduction in feature redundancy compared to existing MoE-SAE methods. This work bridges the interpretability-efficiency gap in LLM analysis, allowing transparent model inspection without compromising computational feasibility.

Related papers

• AnyExperts: On-Demand Expert Allocation for Multimodal Language Models with Mixture of Expert [26.761443359046286]
  We propose AnyExperts, a novel on-demand, budget-aware dynamic routing framework.<n>It allocates a variable total number of expert slots per token based on its semantic importance.<n>It is evaluated across diverse tasks in visual understanding, audio understanding, and NLP understanding.
  arXiv  Detail & Related papers  (2025-11-23T06:53:43Z)
• One-Prompt Strikes Back: Sparse Mixture of Experts for Prompt-based Continual Learning [52.966712416640085]
  We propose SMoPE, a novel framework that integrates the benefits of both task-specific and shared prompt strategies.<n>SMoPE consistently outperforms task-specific prompt methods and achieves performance competitive with state-of-the-art approaches.
  arXiv  Detail & Related papers  (2025-09-29T08:54:58Z)
• Dynamic Experts Search: Enhancing Reasoning in Mixture-of-Experts LLMs at Test Time [35.31371938688921]
  Test-Time Scaling (TTS) enhances the reasoning ability of large language models (LLMs) by allocating additional computation during inference.<n>We propose Dynamic Experts Search (DES), a TTS strategy that elevates expert activation into a controllable dimension of the search space.
  arXiv  Detail & Related papers  (2025-09-26T16:49:10Z)
• Unveiling Super Experts in Mixture-of-Experts Large Language Models [5.36587814108884]
  Sparsely activated Mixture-of-Experts (MoE) models have shown promise in enhancing the learning capacity of large language models (LLMs)<n>We present the first discovery and investigation of a distinct subset of experts that play a crucial role in the underlying mechanisms during the model's forward inference.<n>Our findings confirm that MoE LLMs rely on SEs to induce attention sinks, which are crucial for the distribution of attention scores but are significantly disrupted by SE pruning.
  arXiv  Detail & Related papers  (2025-07-31T06:35:33Z)
• Finding Fantastic Experts in MoEs: A Unified Study for Expert Dropping Strategies and Observations [86.90549830760513]
  Sparsely activated Mixture-of-Experts (SMoE) has shown promise in scaling up the learning capacity of neural networks.<n>We propose MoE Experts Compression Suite (MC-Suite) to provide a benchmark for estimating expert importance from diverse perspectives.<n>We present an experimentally validated conjecture that, during expert dropping, SMoEs' instruction-following capabilities are predominantly hurt.
  arXiv  Detail & Related papers  (2025-04-08T00:49:08Z)
• Mixture of Experts Made Intrinsically Interpretable [34.36996159677674]
  We present textbfMoE-X, a Mixture-of-Experts (MoE) language model designed to be emphintrinsically interpretable.<n>Our approach is motivated by the observation that, in language models, wider networks with sparse activations are more likely to capture interpretable factors.<n>MoE-X achieves perplexity better than GPT-2, with interpretability surpassing even sparse autoencoder (SAE)-based approaches.
  arXiv  Detail & Related papers  (2025-03-05T17:40:54Z)
• Enhancing Multiple Dimensions of Trustworthiness in LLMs via Sparse Activation Control [44.326363467045496]
  Large Language Models (LLMs) have become a critical area of research in Reinforcement Learning from Human Feedback (RLHF) representation engineering offers a new, training-free approach. This technique leverages semantic features to control the representation of LLM's intermediate hidden states. It is difficult to encode various semantic contents, like honesty and safety, into a singular semantic feature.
  arXiv  Detail & Related papers  (2024-11-04T08:36:03Z)
• FactorLLM: Factorizing Knowledge via Mixture of Experts for Large Language Models [50.331708897857574]
  We introduce FactorLLM, a novel approach that decomposes well-trained dense FFNs into sparse sub-networks without requiring any further modifications. FactorLLM achieves comparable performance to the source model securing up to 85% model performance while obtaining over a 30% increase in inference speed.
  arXiv  Detail & Related papers  (2024-08-15T16:45:16Z)
• Multi-Head Mixture-of-Experts [100.60556163597946]
  We propose Multi-Head Mixture-of-Experts (MH-MoE), which employs a multi-head mechanism to split each token into multiple sub-tokens. MH-MoE is straightforward to implement and decouples from other SMoE optimization methods, making it easy to integrate with other SMoE models for enhanced performance.
  arXiv  Detail & Related papers  (2024-04-23T13:47:09Z)
• Diversifying the Mixture-of-Experts Representation for Language Models with Orthogonal Optimizer [59.43462055143123]
  The Mixture of Experts (MoE) has emerged as a highly successful technique in deep learning. In this study, we shed light on the homogeneous representation problem, wherein experts in the MoE fail to specialize and lack diversity. We propose an alternating training strategy that encourages each expert to update in a direction to the subspace spanned by other experts.
  arXiv  Detail & Related papers  (2023-10-15T07:20:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.