# LOCKR: A Hidden-State Trajectory-Guided Planner for Detecting and Repairing Stable-but-Wrong Lock-In in Diffusion Language Models

Authors: Guoshenghui Zhao, Tan Yu, Weijie Zhao.

Uses the history of diffusion hidden states to detect answers that become stable but remain wrong. A planner selects targeted repair branches and verifies continuations, testing whether these internal trajectories support better repair decisions than confidence or a single snapshot.

First public: 2026-09-23.
ArXiv initial submission: 2026-09-23.

[Original paper](https://arxiv.org/abs/2609.27220) · [Reading-list entry](https://latent-implicit-thinking.github.io/papers/2609.27220/)
[Preprint BibTeX](https://latent-implicit-thinking.github.io/papers/2609.27220/citation.bib) · [Preprint CSL-JSON](https://latent-implicit-thinking.github.io/papers/2609.27220/citation.csl.json) · [JSON record](https://latent-implicit-thinking.github.io/papers/2609.27220/record.json)

## Approaches

- [Alternative generation methods](https://latent-implicit-thinking.github.io/topics/alternative-reasoning-schedules/): Change how intermediate steps are generated, revised, or searched—for example, by refining several steps in parallel. These are related approaches: some still produce written chains of thought.
- [Analysis and evaluation](https://latent-implicit-thinking.github.io/topics/implicit-reasoning-evaluation/): Measure how well these methods work, investigate what their internal computation does, and identify where they fail. This includes experiments, studies of model internals, and mathematical results.

Summary and categories are editorial. Read the original paper to verify claims and limitations. A citation to a paper does not, by itself, establish that a later method builds on it.

Reading-list version 0.9.5; literature checked through 2026-10-18.