J-CoT framework enhances LLM reasoning with J-space coordinates

Researchers have introduced J-CoT, a novel recurrent reasoning framework that operates within a "J-space," a coordinate system indexed by vocabulary. This approach allows models to carry intermediate states forward without requiring full verbalization or recurrence over the entire hidden state. J-CoT aims to improve language model reasoning by using vocabulary-indexed coefficients to represent intermediate states, which are then mapped back into the model's hidden representation for subsequent steps. In evaluations, J-CoT-Zero matched or surpassed existing latent-reasoning baselines, while J-CoT-Train achieved top scores across mathematical, scientific, coding, and structured path-reasoning tasks. AI

IMPACT Introduces a novel framework for enhancing LLM reasoning capabilities beyond traditional chain-of-thought methods.

RANK_REASON This is a research paper detailing a new method for improving language model reasoning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• alphaXiv
• arXiv
• CatalyzeX Code Finder for Papers
• CORE Recommender
• DagsHub
• Gotit.pub
• Hugging Face
• Influence Flower
• ScienceCast

• paper
• model release

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →