mlsanityData to 7 Oct 2026

The Ignition Is Real, and It Lives at the Readout: Latent composition, difficulty-clocked ignition, and the interface-constituted commit in a recurrent-depth reasoner

arXiv 2608.03263 ↗First posted 4 Aug 2026cs.LG cs.AIcited by 2

On the map: LLM reasoning → Test-time compute and efficient thinking

Abstract (arXiv)

We test whether the "compositional ignition" reported in latent-reasoning models is real computation, an instrument artifact, or inherited from verbal training data. We grow an independent realization of a published 30M-parameter recurrent-depth reasoner from scratch (same recipe and seed), film its development, certify fidelity through a pre-registered whole-signature gate, and measure resolution in two channels at once: the vocabulary readout and the hidden state. The ignition is real and lives at the readout: arrival time rises lawfully with problem depth, resolution is sharp and holds, and the signature reproduces across two same-seed realizations with divergent training trajectories. At commitment the decision margin jumps 5.8-8.0 logits in one iteration, exceeding the 90th percentile of near-threshold non-event steps in 96% of cases; the signed margin's zero-crossing there is definitional and carries no evidential weight, so the evidence is that conditioned magnitude. The hidden-state direction snaps in raw geometry, meeting its pre-registered criterion (in the decoder's LayerNorm coordinates it attenuates just below our bar, so the composite decoder-coordinate claim is not confirmed), and then freezes in both (descriptively so in decoder coordinates; angular steps 52.9 to 1.2 degrees over eight iterations), while subsequent displacement is predominantly radial (0.961 of squared-norm) and readout-null to a measured bound (radial logit effect <=5.7e-6). An earlier velocity-trough claim is withdrawn: pre-registered normalization controls showed it coordinate-dependent. Intermediates were never recoverable through the tied readout (relay 0.00). All criteria were frozen before their data; the predictions ledger, including this paper's own withdrawn headline, ships in the companion repository.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Test-time compute and efficient thinking, one of 17 directions in LLM reasoning. Builds on 8 of its 8 references that are in the map. First posted on arXiv 4 Aug 2026.

Builds on

8 of 8 references.

Closest ancestors · 2

Same direction, newest first

  1. 2026Are Latent Reasoning Models Easily Interpretable?6 Apr
  2. 2025Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach7 Feb

Other references · 6

  1. 2026Verbalizable Representations Form a Global Workspace in Language Modelscited by 59
  2. 2026Loop, Think, & Generalize: Implicit Reasoning in Recurrent-Depth Transformerscited by 34
  3. 2025Latent Chain-of-Thought? Decoding the Depth-Recurrent Transformercited by 24
  4. 2026Adaptive Depth in Looped Transformers: Diagnosing Learned Halting Gates and Trajectory Readoutscited by 7
  5. 2026Dense Supervision Is Not Enough: The Readout Blind Spot in Looped Language Modelscited by 5
  6. 2026DiscoLoop: Looping Discrete Embeddings and Continuous Hidden States for Multi-hop Reasoningcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

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Close in wording, no citation either way

  1. 2026Latent Recurrent Thoughts: Recurrent Refinement of Proposed Latents for Reasoning with Frozen LLMs
  2. 2026When to Re-Plan: Subgoal Persistence in Hierarchical Latent Reasoning
  3. 2026LLM Reasoning Is Latent, Not the Chain of Thought
  4. 2026Where Cognition Lives: Dissecting Emergent from Computed Function in a Minimal Complete Cognitive Architecture
  5. 2026Reasoning as State Transition: A Representational Analysis of Reasoning Evolution in Large Language Models
  6. 2026Track, Rank, Crack: Epistemic Working Memory Scales Multi-Hop Reasoning in Language Agents
  7. 2026LatentHarness: Learning Latent Actions for Memory and Reasoning via Counterfactual Policy Distillation
  8. 2026Final Checkpoints Are Not Enough: Analyzing Latent Reasoning Faithfulness Along Training Trajectories