Neuro-symbolic Training for Reasoning over Spatial Language (2025.findings-naacl)
Copied to clipboard
| Challenge: | Spatial reasoning is essential for everyday human tasks and is crucial for robots to interact with their environment in a human-like manner. |
| Approach: | They propose to train language models to adhere to spatial reasoning rules as constraints . this allows them to capture the necessary level of abstraction for spatial reasoning . |
| Outcome: | The proposed technique improves language models in multi-hop spatial reasoning over text . it achieves higher accuracy than other competitive Spatial Question-answering benchmarks . |
Similar Papers
Representation, Learning and Reasoning on Spatial Language for Downstream NLP Tasks (2020.emnlp-tutorials)
Copied to clipboard
| Challenge: | In this tutorial, we discuss the cutting-edge research results and existing challenges related to spatial language understanding including semantic annotations, existing corpora, symbolic and sub-symbolic representations, qualitative spatial reasoning, spatial common sense, deep and structured learning models. |
| Approach: | This tutorial presents cutting-edge research results and current challenges related to spatial language understanding including semantic annotations, existing corpora, symbolic and sub-symbolic representations, qualitative spatial reasoning, spatial common sense, deep and structured learning models. |
| Outcome: | This paper reviews the cutting-edge research results and current challenges related to spatial language understanding including semantic annotations, existing corpora, symbolic and sub-symbolic representations, qualitative spatial reasoning, spatial common sense, deep and structured learning models. |
Disentangling Extraction and Reasoning in Multi-hop Spatial Reasoning (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Recent studies highlight the struggles even large language models encounter when it comes to performing spatial reasoning over text. |
| Approach: | They propose to disentangle spatial reasoning over text and compare them to state-of-the-art models with no explicit design for these parts. |
| Outcome: | The proposed models show that they can perform spatial reasoning over text and can generalize within real data domains. |
SpatialMath: Spatial Comprehension-Infused Symbolic Reasoning for Mathematical Problem-Solving (2026.findings-eacl)
Copied to clipboard
| Challenge: | Current models struggle to accurately decompose intricate visual inputs and connect perception with structured reasoning, leading to suboptimal performance. |
| Approach: | They propose a Spatial Comprehension-Infused Symbolic Reasoning Framework to integrate spatial representations into structured symbolic reasoning chains. |
| Outcome: | The proposed framework outperforms existing models in vision-intensive mathematical problems. |
SPARTQA: A Textual Question Answering Benchmark for Spatial Reasoning (2021.naacl-main)
Copied to clipboard
| Challenge: | Existing studies have focused on the spatial reasoning capabilities of modern language models (LMs) however, there has been limited research into the spatial thinking capabilities of LMs. |
| Approach: | They propose a question-answering (QA) benchmark for spatial reasoning on natural language text which contains more realistic spatial phenomena not covered by prior work. |
| Outcome: | The proposed method significantly improves LMs' ability on spatial understanding, which in turn helps solve two external datasets, bAbI, and boolQ. |
SpaRE: Enhancing Spatial Reasoning in Vision-Language Models with Synthetic Data (2025.acl-long)
Copied to clipboard
| Challenge: | Vision-language models struggle with spatial reasoning, a skill that humans excel at. |
| Approach: | They propose to use a spatial-reasoning Enhanced (SpaRE) VLM to improve spatial reasoning in visual question answering and robotics. |
| Outcome: | The proposed model achieves a 49% performance gain on the What's Up benchmark while maintaining strong results on general tasks. |
Sparkle: Mastering Basic Spatial Capabilities in Vision Language Models Elicits Generalization to Spatial Reasoning (2025.findings-emnlp)
Copied to clipboard
Yihong Tang, Ao Qu, Zhaokai Wang, Dingyi Zhuang, Zhaofeng Wu, Wei Ma, Shenhao Wang, Yunhan Zheng, Zhan Zhao, Jinhua Zhao
| Challenge: | Currently, vision-language models excel in many downstream tasks but struggle with spatial reasoning, which is crucial for navigation and interaction with physical environments. |
| Approach: | They propose a framework that generates synthetic data to provide targeted supervision for VLMs across these basic spatial capabilities. |
| Outcome: | The proposed framework disentangles 2D spatial reasoning into three core components: direction comprehension, distance estimation, and localization. |
Complex Reasoning in Natural Language (2023.acl-tutorials)
Copied to clipboard
| Challenge: | Recent research shows that pretrained language models are often brittle for complex reasoning tasks. |
| Approach: | They propose to use pre-trained language models to teach machines to reason over texts . they will review recent promising approaches to tackling complex reasoning tasks . |
| Outcome: | This tutorial reviews promising approaches to complex reasoning tasks . it reviews the methods that can be used to augment models with robustness . |
A Neural-Symbolic Approach to Natural Language Understanding (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Pre-trained language models have enabled deep neural networks to perform natural language understanding tasks, but their performance can drastically deteriorate when logical reasoning is needed. |
| Approach: | They propose a framework for NLU based on analogical reasoning based upon neural processing and logical reasoning using both neural and symbolic processing. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on two NLU tasks, question answering (QA) and natural language inference (NLI). |
Neuro-Symbolic Natural Language Processing (2025.emnlp-tutorials)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have limitations in terms of safe and controlled reasoning, interpretability and adaptability . this tutorial aims to bridge the gap between the practical performance of LLMs and the principled modelling of language and inference of formal methods. |
| Approach: | This tutorial aims to bridge the gap between the practical performance of Large Language Models and the principled modelling of language and inference of formal methods. |
| Outcome: | This tutorial aims to bridge the gap between the performance of LLMs and the principled modelling of language and inference of formal methods. |
Spatial-Agent: Agentic Geo-spatial Reasoning with Scientific Core Concepts (2026.acl-long)
Copied to clipboard
| Challenge: | Existing LLM-based agents lack inherent spatial awareness, relying on web search or text matching while hallucinating spatial relationships. |
| Approach: | They propose a spatial-based agent that can perform real-world geospatial computations . they use natural-language questions to parse into executable workflows based on geoFlow Graphs - directed acyclic graphs with nodes corresponding to spatial concepts and edges representing transformations. |
| Outcome: | The proposed agent outperforms existing baselines on MapEval-API and MapQA benchmarks while producing interpretable and executable geospatial workflows. |