mlsanityData to 13 Oct 2026

The Token Before the Value Is the Key: How Hybrid Architectures Organize Induction Circuits

arXiv 2609.15545 ↗First posted 14 Sep 2026cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

Hybrid language models can improve capability as well as efficiency, raising the question of how architectural complementarity becomes learned computation. We examine the established induction roles of Carrying predecessor information, Matching a source by content, and Copying its value. How are these position-sensitive and content-based computations allocated across heterogeneous layers? We introduce layer-type-agnostic paired probes that track Carrying and Matching through a common block-update interface. In recurrent--global and local--global hybrids, Carrying concentrates in efficient layers and Matching in global receivers. The measured local contribution concentrates on lag one: the token immediately before the historical value. Changing predecessor support through lag-one masking, convolution removal, or early learning-rate reduction can relocate Carrying and Matching between stages. Source-key restoration and fixed-value selection trace the receiver's dependence on the prepared source. These interventions also change natural-text recall, with outcomes depending on configuration and target. Varying local windows and induction-enriched training text changes the early development of functional Carrying and Matching, connecting architectural priors and training evidence to formation timing. Together, the probes and interventions shift the explanatory focus upstream: the organization of Matching follows how Carrying is learned. The token before the value provides a concrete link between a hybrid's architecture, circuit development, and recall. Code is available in https://github.com/ckpassenger/bind-match-copy/tree/main.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 28 of its 30 references that are in the map. First posted on arXiv 14 Sep 2026.

Builds on

28 of 30 references.

References · 28

  1. 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,658
  2. 2020Longformer: The Long-Document Transformercited by 3,393
  3. 2024Gated Delta Networks: Improving Mamba2 with Delta Rulecited by 501
  4. 2023Gated Linear Attention Transformers with Hardware-Efficient Trainingcited by 500
  5. 2024Jamba: A Hybrid Transformer-Mamba Language Modelcited by 394
  6. 2023Towards Best Practices of Activation Patching in Language Models: Metrics and Methodscited by 345
  7. 2024Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Modelscited by 233
  8. 2024Repeat After Me: Transformers are Better than State Space Models at Copyingcited by 208
  9. 2023Causal Abstraction: A Theoretical Foundation for Mechanistic Interpretabilitycited by 200
  10. 2023Zoology: Measuring and Improving Recall in Efficient Language Modelscited by 192
  11. 2025Kimi Linear: An Expressive, Efficient Attention Architecturecited by 178
  12. 2024Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modelingcited by 169
16 more
  1. 2026Kimi K3: Open Frontier Intelligencecited by 122
  2. 2024Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Taskscited by 104
  3. 2024In-Context Language Learning: Architectures and Algorithmscited by 88
  4. 2024What needs to go right for an induction head? A mechanistic study of in-context learning circuits and their formationcited by 87
  5. 2025A Systematic Analysis of Hybrid Linear Attentioncited by 38
  6. 2026On the Design of Qwen3.8-Next Architecture: Evaluation, Efficiency, and Training Stabilitycited by 24
  7. 2026Olmo Hybrid: From Theory to Practice and Backcited by 22
  8. 2025Understanding the Skill Gap in Recurrent Language Models: The Role of the Gather-and-Aggregate Mechanismcited by 15
  9. 2025Short window attention enables long-term memorizationcited by 11
  10. 2025Mechanistic evaluation of Transformers and state space modelscited by 8
  11. 2025Predicting the Emergence of Induction Heads in Language Model Pretrainingcited by 6
  12. 2025On the Emergence of Induction Heads for In-Context Learningcited by 5
  13. 2026Rethinking the Role of Efficient Attention in Hybrid Architecturescited by 5
  14. 2026Expressivity-Efficiency Tradeoffs for Hybrid Sequence Modelscited by 3
  15. 2026Invariant Learning Dynamics of Transformers in Inductive Reasoning Taskscited by 2
  16. 2026Modern Transformers Are Implicit Hybrids: From Functional Differentiation to Principled Hybrid Architecture Designcited by 1

Built on by

Not cited yet.