mlsanityData to 7 Oct 2026

Route-Induced Density and Stability (RIDE): Controlled Intervention and Mechanism Analysis of Routing-Style Meta Prompts on LLM Internal States

arXiv 2603.29206 ↗First posted 31 Mar 2026cs.AIcited by 0

On the map: Mixture-of-experts → Expert routing strategies

Abstract (arXiv)

Routing is widely used to scale large language models, from Mixture-of-Experts gating to multi-model/tool selection. A common belief is that routing to a task ``expert'' activates sparser internal computation and thus yields more certain and stable outputs (the Sparsity--Certainty Hypothesis). We test this belief by injecting routing-style meta prompts as a textual proxy for routing signals in front of frozen instruction-tuned LLMs. We quantify (C1) internal density via activation sparsity, (C2) domain-keyword attention, and (C3) output stability via predictive entropy and semantic variation. On a RouterEval subset with three instruction-tuned models (Qwen3-8B, Llama-3.1-8B-Instruct, and Mistral-7B-Instruct-v0.2), meta prompts consistently densify early/middle-layer representations rather than increasing sparsity; natural-language expert instructions are often stronger than structured tags. Attention responses are heterogeneous: Qwen/Llama reduce keyword attention, while Mistral reinforces it. Finally, the densification--stability link is weak and appears only in Qwen, with near-zero correlations in Llama and Mistral. We present RIDE as a diagnostic probe for calibrating routing design and uncertainty estimation.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Expert routing strategies, one of 12 directions in Mixture-of-experts. Builds on 23 of its 30 references that are in the map. First posted on arXiv 31 Mar 2026.

Builds on

23 of 30 references.

References · 23

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  2. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
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  10. 2023Towards Automated Circuit Discovery for Mechanistic Interpretabilitycited by 700
  11. 2019Attention is not not Explanationcited by 629
  12. 2022DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scalecited by 400
11 more
  1. 2020Movement Pruning: Adaptive Sparsity by Fine-Tuningcited by 398
  2. 2022ST-MoE: Designing Stable and Transferable Sparse Expert Modelscited by 391
  3. 2020DeeBERT: Dynamic Early Exiting for Accelerating BERT Inferencecited by 322
  4. 2023In-Context Learning Creates Task Vectorscited by 302
  5. 2020BERT Loses Patience: Fast and Robust Inference with Early Exitcited by 267
  6. 2023In-context Vectors: Making In Context Learning More Effective and Controllable Through Latent Space Steeringcited by 240
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  8. 2023Stabilizing Transformer Training by Preventing Attention Entropy Collapsecited by 154
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  10. 2025RouterEval: A Comprehensive Benchmark for Routing LLMs to Explore Model-level Scaling Up in LLMscited by 58
  11. 2025EAP-GP: Mitigating Saturation Effect in Gradient-based Automated Circuit Identificationcited by 21

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  2. 2025Internal Chain-of-Thought: Empirical Evidence for Layer-wise Subtask Scheduling in LLMs
  3. 2025Unveiling Instruction-Specific Neurons & Experts: An Analytical Framework for LLM's Instruction-Following Capabilities
  4. 2025Thinking Inside the Mask: In-Place Prompting in Diffusion LLMs
  5. 2026RIFT: Reordered Instruction Following Testbed To Evaluate Instruction Following in Singular Multistep Prompt Structures
  6. 2026Do as I Say, Not as I Do: Instruction-Induction Conflict in LLMs
  7. 2026Routing-Aligned Fine-Tuning for Multilingual Downstream Tasks in Mixture-of-Experts Models
  8. 2024Do LLMs "know" internally when they follow instructions?