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
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 An earlier velocity-trough claim is withdrawn: pre-registered normalization controls showed it coordinate-dependent.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 All criteria were frozen before their data; the predictions ledger, including this paper's own withdrawn headline, ships in the companion repository.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
- 2026Are Latent Reasoning Models Easily Interpretable?6 Apr
- 2025Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach7 Feb
Other references · 6
- 2026Verbalizable Representations Form a Global Workspace in Language Modelscited by 59
- 2026Loop, Think, & Generalize: Implicit Reasoning in Recurrent-Depth Transformerscited by 34
- 2025Latent Chain-of-Thought? Decoding the Depth-Recurrent Transformercited by 24
- 2026Adaptive Depth in Looped Transformers: Diagnosing Learned Halting Gates and Trajectory Readoutscited by 7
- 2026Dense Supervision Is Not Enough: The Readout Blind Spot in Looped Language Modelscited by 5
- 2026DiscoLoop: Looping Discrete Embeddings and Continuous Hidden States for Multi-hop Reasoningcited by 4
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Citing papers per year: 2026: 2.
2 papers.
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Close in wording, no citation either way
- 2026Latent Recurrent Thoughts: Recurrent Refinement of Proposed Latents for Reasoning with Frozen LLMs
- 2026When to Re-Plan: Subgoal Persistence in Hierarchical Latent Reasoning
- 2026LLM Reasoning Is Latent, Not the Chain of Thought
- 2026Where Cognition Lives: Dissecting Emergent from Computed Function in a Minimal Complete Cognitive Architecture
- 2026Reasoning as State Transition: A Representational Analysis of Reasoning Evolution in Large Language Models
- 2026Track, Rank, Crack: Epistemic Working Memory Scales Multi-Hop Reasoning in Language Agents
- 2026LatentHarness: Learning Latent Actions for Memory and Reasoning via Counterfactual Policy Distillation
- 2026Final Checkpoints Are Not Enough: Analyzing Latent Reasoning Faithfulness Along Training Trajectories