Unlocking the Black Box of Latent Reasoning: An Interpretability-Guided Approach to Intervention
Uses probes, ablations, and targeted edits to study how continuous reasoning states affect answers. Builds interventions from learned semantic mappings and geometric constraints, improving the evaluated models by editing their latent states without updating base-model weights.
First public: · Added to this list: October 18, 2026
Cite this paper
ArXiv preprint
arXiv:2606.01243 · Initial submission: 2026-05-31
A published-version citation has not been verified for this entry. This does not mean the paper is unpublished.
Choose the version you used. The arXiv link may serve a revision; check the original paper for the exact result, assumptions and limitations you cite.
Explore this approach
Analysis and 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.
Continuous-state reasoning — Use vectors—lists of numbers—to carry intermediate reasoning from one step to the next, instead of selecting a text token at every step. Some methods use the model's hidden states; others combine several candidate token representations.
Read or reuse this entry
Plain-text Markdown · JSON record · Find in the full list
Our summary and categories are a reading aid, not the authors’ abstract. Use the original paper for research claims. Suggest a correction.