System-1.5 Reasoning: Traversal in Language and Latent Spaces with Dynamic Shortcuts
Learns dynamic shortcuts between language and latent states to skip portions of an otherwise explicit reasoning trajectory.
First public:
Cite this paper
ArXiv preprint
arXiv:2505.18962 · Initial submission: 2025-05-25
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 This does not mean the paper is unpublished.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
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.
Internalized reasoning — Learn from worked solutions so that some or all of the reasoning can later happen without writing those steps. Training may use a separate teacher model or progressively remove written steps from the model being trained.
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.