mlsanityData to 14 Oct 2026

Monitoring latent reasoning in looped language models

arXiv 2610.18127 ↗First posted 13 Oct 2026cs.AI cs.CLcited by 0

On the map: LLM reasoning → Test-time compute and efficient thinking also fits: Reasoning benchmarks and evaluation

Abstract (arXiv)

Looped language models (LMs) reuse one block of layers several times per token generation, promising more latent computation at no additional storage cost. Despite their rising popularity, the analysis of looped LMs is still in its infancy: little is known about whether the iterative transformations actually improve their capabilities, or whether these models remain stable under prompt perturbation. We construct an inspection protocol rooted in Mechanistic Interpretability comprising five diverse lenses. We apply it to examine eight looped LMs from four families. Models differ in many aspects but the key one is whether the prompt is transmitted only within the looped state (carry loops: Ouro, LoopUS) or re-supplied at every iteration (re-read loops: Parcae, and Rec-OLMo & Huginn from the Raven family). The results of our study provide valuable insights concerning safety and efficient use of looped language models.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Test-time compute and efficient thinking, one of 17 directions in LLM reasoning. Builds on 40 of its 63 references that are in the map. First posted on arXiv 13 Oct 2026.

Builds on

40 of 63 references.

Closest ancestors · 7

Same direction, newest first

  1. 2026Monitoring the Internal Monologue: Probe Trajectories Reveal Reasoning Dynamics18 May
  2. 2026LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models10 May
  3. 2025Teaching Pretrained Language Models to Think Deeper with Retrofitted Recurrence10 Nov
  4. 2025Scaling Latent Reasoning via Looped Language Models29 Oct
  5. 2025Reasoning with Latent Thoughts: On the Power of Looped Transformers24 Feb
  6. 2025Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach7 Feb
  7. 2025s1: Simple test-time scaling31 Jan

Other references · 33

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  5. 2017On Calibration of Modern Neural Networkscited by 4,960
  6. 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
  7. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
  8. 2023Universal and Transferable Adversarial Attacks on Aligned Language Modelscited by 3,104
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  10. 2023Efficient Streaming Language Models with Attention Sinkscited by 2,186
  11. 2024HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusalcited by 1,429
  12. 2023Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Promptingcited by 1,122
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  4. 2019Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Prunedcited by 856
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  13. 2024Soft Prompt Threats: Attacking Safety Alignment and Unlearning in Open-Source LLMs through the Embedding Spacecited by 91
  14. 2023CValues: Measuring the Values of Chinese Large Language Models from Safety to Responsibilitycited by 85
  15. 2026Parcae: Scaling Laws For Stable Looped Language Modelscited by 55
  16. 2025Latent Chain-of-Thought? Decoding the Depth-Recurrent Transformercited by 27
  17. 2026Adaptive Depth in Looped Transformers: Diagnosing Learned Halting Gates and Trajectory Readoutscited by 12
  18. 2026Looped Transformers under the Jacobian Lens: Does the Global Workspace Survive Recurrence?cited by 6
  19. 2026Investigating Single-Block Recurrence in Vision Transformers for Image Recognitioncited by 4
  20. 2026Same path, different: a mechanistic comparison of looped and stacked transformer encoders on 12-lead ECGcited by 1
  21. 2026Prediction Dynamics in Depth-Recurrent Language Modelscited by 1

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