| Challenge: | Recent advances in large language models have demonstrated strong capabilities in tasks such as code generation and mathematical reasoning. |
| Approach: | They investigate whether large language models can construct coherent global spatial cognition by integrating fragmented relational descriptions. |
| Outcome: | The proposed models can generalize to unseen spatial relationships and exhibit latent representations aligned with real-world spatial distributions. |
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Exploring Spatial Schema Intuitions in Large Language and Vision Models (2024.findings-acl)
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| Challenge: | Large language models excel in varied NLP tasks, but lack a direct connection between sensory perception and physical action. |
| Approach: | They examine whether large language models capture implicit human intuitions about building blocks of language . they employ spatial cognitive foundations developed through early sensorimotor experiences . |
| Outcome: | The proposed model captures implicit human intuitions about building blocks of language without a tangible connection to embodied experiences. |
Beyond Lines and Circles: Unveiling the Geometric Reasoning Gap in Large Language Models (2024.findings-emnlp)
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| Challenge: | Recent advances in Large Language Models (LLMs) demonstrate increasing proficiency in complex mathematical and algorithmic tasks, yet their geometric reasoning skills are underexplored. |
| Approach: | They propose a framework that enhances LLMs’ reasoning potential through a multi-agent system conducting internal dialogue. |
| Outcome: | The proposed framework enhances LLMs’ reasoning potential through a multi-agent system conducting internal dialogue. |
On the Scaling Laws of Geographical Representation in Language Models (2024.lrec-main)
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| Challenge: | Language models embed geographical information in their hidden representations, but larger models cannot mitigate this bias. |
| Approach: | They propose to extend this finding to Large Language Models by observing how geographical knowledge evolves when scaling language models. |
| Outcome: | The proposed model scales consistently with increasing model size, but smaller models cannot mitigate geographic bias inherent in training data. |
LLMs as Planning Formalizers: A Survey for Leveraging Large Language Models to Construct Automated Planning Models (2025.findings-acl)
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| Challenge: | Large Language Models excel in various natural language tasks but struggle with long-horizon planning problems requiring structured reasoning. |
| Approach: | They propose to integrate large language models into AP and NLP planning frameworks by reviewing current research and identifying critical challenges and future directions. |
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Can LLMs See Without Pixels? Benchmarking Spatial Intelligence from Textual Descriptions (2026.findings-acl)
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Zhongbin Guo, Zhen Yang, Yushan Li, Xinyue Zhang, Wenyu Gao, Jiacheng Wang, Chengzhi Li, Xiangrui Liu, Ping Jian
| Challenge: | Existing advances in Spatial Intelligence rely on vision-Language Models . however, a critical question remains: does spatial understanding originate from visual encoders? |
| Approach: | They propose to evaluate the SI performance of Large Language Models without pixel-level input. |
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Are Large Language Model Temporally Grounded? (2024.naacl-long)
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| Challenge: | Recent large language models lack a consistent temporal model of textual narratives . sentence ordering in unlabelled texts is only weakly correlated with event ordering . |
| Approach: | They evaluate LLMs with textual narratives and evaluate their common-sense knowledge . they find that LLM models struggle the most with self-consistency . |
| Outcome: | The proposed models lack a consistent temporal model of textual narratives. |
Language Models Struggle to Use Representations Learned In-Context (2026.acl-long)
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| Challenge: | a recent study shows that large language models are capable of inducing rich representations of data that are seen in-context . a novel task, adaptive world modeling, shows that even the most performant LLMs cannot reliably leverage novel semantics defined in-constitut. |
| Approach: | They propose to use in-context representations to induce rich representations of data . they also propose to probe models using a novel task to enable flexible deployment . |
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Representation, Learning and Reasoning on Spatial Language for Downstream NLP Tasks (2020.emnlp-tutorials)
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| 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. |
Large Language Models: The Need for Nuance in Current Debates and a Pragmatic Perspective on Understanding (2023.emnlp-main)
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| Challenge: | Current Large Language Models (LLMs) are unparalleled in their ability to generate grammatically correct, fluent text. |
| Approach: | They argue that LLMs only parrot statistical patterns in training data and that language learning in LLM cannot inform human language learning. |
| Outcome: | The proposed model can generate grammatically correct, fluent text without requiring human intervention. |
Where Do We Go From Here? Multi-scale Allocentric Relational Inferencefrom Natural Spatial Descriptions (2024.eacl-long)
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| Challenge: | Current NLP navigation studies focus on egocentric local descriptions that require reasoning over the agent’s local perception. |
| Approach: | They propose to use a dataset to analyse English geospatial instructions to find locations and paths from natural language descriptions. |
| Outcome: | The proposed task and dataset includes 10,404 examples of English geospatial instructions for reaching a target location using map-knowledge. |