Skip to main content

Beyond Chains of Thought: Benchmarking Latent-Space Reasoning Abilities in Large Language Models

Thilo Hagendorff, Sarah Fabi

Encodes the answer in the language of the model's first response token, forcing a choice before any written explanation. Also tests whether successful answers could come from simple heuristics.

First public:

Cite this paper

ArXiv preprint

arXiv:2504.10615 · Initial submission: 2025-04-14

BibTeX · CSL-JSON

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 This does not mean the paper is unpublished.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.

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.