arXiv:2607. 20402v1 Announce Type: new Abstract: In many reasoning problems, the premises are not observed as discrete symbols, but must be inferred from high-dimensional inputs.
By Wael AbdAlmageed
arXiv:2510. 19698v3 Announce Type: replace Abstract: Large Language Models (LLMs) can propose rules in natural language, sidestepping the need for a predefined predicate space in traditional rule learning.
By Yang Yang, Hua XU, Zhangyi Hu, Yutao Yue
arXiv:2510. 23379v2 Announce Type: replace-cross Abstract: We investigate a relatively under-explored class of hybrid neurosymbolic models that integrate symbolic learning with neural reasoning to construct data generators meeting formal correctness criteria.
By Ashwin Srinivasan, Tirtharaj Dash, A Baskar, Michael Bain, Sanjay Kumar Dey, Mainak Banerjee
arXiv:2605. 30456v2 Announce Type: replace Abstract: Many learning tasks in science and engineering are characterized by sparse datasets, which limits the effectiveness of purely data-driven approaches.
By Shraman Pal, Can Li
arXiv:2606. 03269v1 Announce Type: new Abstract: Visual Question Answering (VQA) is the task of answering questions about images, requiring the integration of multimodal input and reasoning.
By Thomas Eiter, Nelson Higuera Ruiz, Johannes Oetsch
arXiv:2604. 03245v2 Announce Type: replace-cross Abstract: The remarkable reasoning and code generation capabilities of large language models (LLMs) have recently motivated increasing interest in automating formal verification (FV), a process that ensures hardware correctness through mathematically precise assertions but remains highly labor-intensive, particularly through the translation of natural language into SystemVerilog Assertions (NL-to-SVA).
By Lily Jiaxin Wan, Chia-Tung Ho, Yunsheng Bai, Cunxi Yu, Ghaith Bany Hamad, Deming Chen, Haoxing Ren