mlsanityData to 14 Oct 2026

Domain Restriction via Multi SAE Layer Transitions

arXiv 2605.11920 ↗First posted 12 May 2026cs.AIcited by 0

On the map: Mechanistic interpretability → SAE features and their applications

Abstract (arXiv)

The general-purpose nature of Large Language Models (LLMs) presents a significant challenge for domain-specific applications, often leading to out-of-domain (OOD) interactions that undermine the provider's intent. Existing methods for detecting such scenarios treat the LLM as an uninterpretable black box and overlook the internal processing of inputs. In this work we show that layer transitions provide a promising avenue for extracting domain-specific signature. Specifically, we present several lightweight ways of learning on internal dynamics encoded using a sparse autoencoder (SAE) that exhibit great capability in distinguishing OOD texts. Building on top of SAEs representation transitions enables us to better interpret the LLM internal evolution of input processing and shed light on its decisions. We provide a comprehensive analysis of the method and benchmark it with the gemma-2 2B and 9B models. Our results emphasize the efficacy of the internal process in capturing fine-grained input-related details.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in SAE features and their applications, one of 13 directions in Mechanistic interpretability. Builds on 20 of its 28 references that are in the map. First posted on arXiv 12 May 2026.

Builds on

20 of 28 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025SAEs Are Good for Steering -- If You Select the Right Features26 May
  2. 2023Sparse Autoencoders Find Highly Interpretable Features in Language Models15 Sep

Other references · 18

  1. 2017Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layercited by 3,509
  2. 2016A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networkscited by 2,385
  3. 2023WebArena: A Realistic Web Environment for Building Autonomous Agentscited by 2,006
  4. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,601
  5. 2018A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attackscited by 1,485
  6. 2017Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networkscited by 1,328
  7. 2022Out-of-Distribution Detection with Deep Nearest Neighborscited by 600
  8. 2024Sparse Feature Circuits: Discovering and Editing Interpretable Causal Graphs in Language Modelscited by 435
  9. 2023Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignmentcited by 401
  10. 2020Pretrained Transformers Improve Out-of-Distribution Robustnesscited by 294
  11. 2024RouterBench: A Benchmark for Multi-LLM Routing Systemcited by 238
  12. 2022Toward Transparent AI: A Survey on Interpreting the Inner Structures of Deep Neural Networkscited by 129
6 more
  1. 2021Revisiting Mahalanobis Distance for Transformer-Based Out-of-Domain Detectioncited by 70
  2. 2023A Survey on Out-of-Distribution Detection in NLPcited by 31
  3. 2023Is Fine-tuning Needed? Pre-trained Language Models Are Near Perfect for Out-of-Domain Detectioncited by 29
  4. 2024Out-of-Distribution Detection: A Task-Oriented Survey of Recent Advancescited by 26
  5. 2024Your Finetuned Large Language Model is Already a Powerful Out-of-distribution Detectorcited by 11
  6. 2022Holistic Sentence Embeddings for Better Out-of-Distribution Detectioncited by 6

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  3. 2026When Can We Trust the Sparse Lens? A Certification Framework for SAE Faithfulness
  4. 2026Qwen-Scope: Turning Sparse Features into Development Tools for Large Language Models
  5. 2025Interpreting and Steering LLMs with Mutual Information-based Explanations on Sparse Autoencoders
  6. 2024Tracking the Feature Dynamics in LLM Training: A Mechanistic Study
  7. 2025Unveiling Language-Specific Features in Large Language Models via Sparse Autoencoders
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