arXiv:2607. 08136v1 Announce Type: new Abstract: We present a general neurosymbolic reasoning and learning methodology based on a modular integration of answer set programming with an energy based model substrate.
By Jakob Suchan, Julius Monsen, Salim Baloch, Mehul Bhatt
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:2605. 01797v2 Announce Type: replace Abstract: Integration of Answer Set Programming (ASP) with neural networks has emerged as a promising tool in Neuro-symbolic AI.
By Thomas Eiter, Katsumi Inoue, Sota Moriyama
The paper introduces a multimodal reasoning framework for cross‑domain visual question answering in Printed Circuit Board Assembly (PCBA) inspection, converting diverse data sources into a unified instruction format and generating verified reasoning traces. It proposes Task‑Aware Group Relative Policy Optimization (GRPO) that uses semantic, distance‑aware, and format rewards to improve choice‑based and counting tasks beyond exact‑match supervision. During inference, the system applies semantic consistency correction, self‑consistency voting, and multi‑model arbitration, achieving an overall score of 83.24 on the PCBA Standard‑to‑Real Grand Challenge leaderboard.
By Jia Li, Li Dai, Peng Jia, Zhenzhen Hu, Chee Seng Chan, Bingkun Bao, Richang Hong
arXiv:2609.35942v1 Announce Type: new
Abstract: Recent work in visual question answering has shown that vision-language models can exhibit strong reasoning capabilities by translating visual inputs i...
By Ting-Chih Chen, Emile van Krieken, Shujian Yu, Filip Ilievski
arXiv:2606. 16811v1 Announce Type: new Abstract: For the development of Large language models (LLMs), recent approaches to generating pseudo intermediate reasoning have shown remarkable progress.
By Keizo Kato, Chenhui Chu, Yugo Murawaki, Sado Kurohashi