High Quality Embeddings for Horn Logic Reasoning
The paper discusses the development of high-quality embeddings for Horn logic reasoning. It introduces various methods to create numeric representations of logical statements that enhance the efficiency of neural networks in ranking choices made by logical reasoners. The authors conduct experiments to evaluate the effectiveness of these embeddings across different knowledge bases.
- ▪Neural networks can be trained to rank choices made by logical reasoners for more efficient searches.
- ▪The paper introduces methods for creating embeddings that improve downstream results.
- ▪Experiments compare different embeddings to identify characteristics suited for specific reasoning tasks.
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Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20467 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | EWLCc5-phNKF |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.20467 (cs) [Submitted on 19 May 2026] Title:High Quality Embeddings for Horn Logic Reasoning Authors:Yifan Zhang, Yasir White, Dean Clark, Joseph Sanchez, Jevon Lipsey, Ashely Hirst, Jeff Heflin View a PDF of the paper titled High Quality Embeddings for Horn Logic Reasoning, by Yifan Zhang and 6 other authors View PDF HTML (experimental) Abstract:Neural networks can be trained to rank the choices made by logical reasoners, resulting in more efficient searches for answers. A key step in this process is creating useful embeddings, i.e., numeric representations of logical statements. This paper introduces and evaluates several approaches to creating embeddings that result in better downstream results.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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