SMTB: Fast Structure-Mapping with Tight Bounds
arXiv cs.AIen
arXiv:2609.25508v1 Announce Type: new Abstract: Structure-mapping forms analogies by aligning systems of relationally connected elements based on shared structure instead of surface features. We introduce a new structure-mapping algorithm: Structure-Mapping with Tight Bounds (SMTB) that is 5--15x faster than the structure-mapping engine (SME) and about 50\% better at finding mappings in large nested domains. SMTB is part of the broader Cognitive Rule Engine (CRE) project, a flexible multi-language-compatible framework with an accessible Python interface to state-of-the-art C++ implementations of core algorithms commonly used in cognitive systems such as pattern matching, planning, and struct
This is a short summary published by AI Global Wire. The full article is owned and hosted by arXiv cs.AI — open it there to read it in full.
Read the full story at arXiv cs.AI- Verktyg
- Forskning
Related AI news
- From one of Europe’s biggest fintech exits to bootstrapping an AI startup: ‘There’s no limitation’Sifted · September 23, 2026
- They built AI agents on WhatsApp. Then Meta entered the chatTech in Asia · September 23, 2026
- 4DGS-JEPA: Temporally Compositional Joint-Embedding Prediction for Dynamic Gaussian SplattingarXiv cs.AI · September 23, 2026
- Lean Pool: An AI-Maintained Archive of Formalized MathematicsarXiv cs.AI · September 23, 2026
- Making Agents More Consistent: Skills Should Form Habits for Repeat TasksarXiv cs.AI · September 23, 2026
- Efficient Iterative Retrieval with Heterogeneous BatchingarXiv cs.AI · September 23, 2026