arXiv AI July 23, 2026 By Wael AbdAlmageed

SoftReason: A Fully Differentiable Neuro-Soft-Symbolic Deductive Reasoning Architecture over High-Dimensional Perceptual Data

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.

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arXiv AI

Aug 19

Structure-Internalized Rule Language Model for Faithful Knowledge Graph Reasoning

The paper introduces the Structure-Internalized Rule Language Model (SIRLM) to improve Knowledge Graph Reasoning (KGR) by addressing the mismatch between KG structural context and Large Language Model (LLM) parametric knowledge. SIRLM centers on a Structure-Internalized Rule Generator (SIRG) that uses in-context learning, a structural relation memory, a KG tokenizer, and a neuro-symbolic reasoner to generate structural rules and provide faithful rule-execution feedback. Experiments on 36 datasets against 17 state‑of‑the‑art KGR methods show that SIRLM achieves significant performance gains.

By Xingrui Zhuo, Jiapu Wang, Manzong Huang, Gongqing Wu, Xindong Wu

llms nlp fine-tuning benchmarks

arXiv Computation and Language

4d ago

Question-Specific Knowledge Graphs for Efficient Visual Reasoning

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

llms nlp reinforcement-learning multimodal benchmarks

arXiv AI

Jun 3

Distilling Answer-Set Programming Rules from LLMs for Neurosymbolic Visual Question Answering

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

llms nlp efficiency multimodal safety

arXiv Computer Vision

Sep 21

DRT: Dense Reasoning Trace for Efficient and Grounded Multimodal Reasoning

arXiv:2609.21675v1 Announce Type: new Abstract: Despite the remarkable progress in Multimodal Large Language Models (MLLMs), prevailing Chain-of-Thought (CoT) paradigms remain confined to the natural...

By Wan Xu, Yuanfan Guo, Kevin Han, LaLa Chen, Wangmeng Zuo

llms reinforcement-learning multimodal benchmarks safety

arXiv AI

Jul 1

Can VLMs Reason Robustly? A Neuro-Symbolic Investigation

arXiv:2603. 23867v2 Announce Type: replace-cross Abstract: Vision-Language Models (VLMs) have been applied to a wide range of reasoning tasks, yet it remains unclear whether they can reason robustly under distribution shifts.

By Weixin Chen, Antonio Vergari, Han Zhao

llms fine-tuning multimodal

arXiv Computer Vision

Sep 1

LaME: Learning to Think in Latent Space for Multimodal Embedding via Information Bottleneck

arXiv:2606.13061v3 Announce Type: replace Abstract: Reasoning-driven universal multimodal embedding has advanced rapidly by introducing Chain-of-Thought (CoT) reasoning into the embedding pipeline. D...

By Peixi Wu, Biao Yang, Feipeng Ma, Bosong Chai, Bo Lin, Wei Yuan, Fan Yang, Tingting Gao, Hebei Li, Xiaoyan Sun

rag diffusion efficiency multimodal

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Structure-Internalized Rule Language Model for Faithful Knowledge Graph Reasoning

Question-Specific Knowledge Graphs for Efficient Visual Reasoning

Distilling Answer-Set Programming Rules from LLMs for Neurosymbolic Visual Question Answering

DRT: Dense Reasoning Trace for Efficient and Grounded Multimodal Reasoning

Can VLMs Reason Robustly? A Neuro-Symbolic Investigation

LaME: Learning to Think in Latent Space for Multimodal Embedding via Information Bottleneck