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Counting Answer Sets of Disjunctive Answer Set Programs
Mohimenul Kabir, Supratik Chakraborty, Kuldeep S Meel
https://arxiv.org/abs/2507.11655 https://

Counting Answer Sets of Disjunctive Answer Set Programs
Answer Set Programming (ASP) provides a powerful declarative paradigm for knowledge representation and reasoning. Recently, counting answer sets has emerged as an important computational problem with applications in probabilistic reasoning, network reliability analysis, and other domains. This has motivated significant research into designing efficient ASP counters. While substantial progress has been made for normal logic programs, the development of practical counters for disjunctive logic pr…

Cross-Granularity Hypergraph Retrieval-Augmented Generation for Multi-hop Question Answering
Changjian Wang, Weihong Deng, Weili Guan, Quan Lu, Ning Jiang
https://arxiv.org/abs/2508.11247

Cross-Granularity Hypergraph Retrieval-Augmented Generation for Multi-hop Question Answering
Multi-hop question answering (MHQA) requires integrating knowledge scattered across multiple passages to derive the correct answer. Traditional retrieval-augmented generation (RAG) methods primarily focus on coarse-grained textual semantic similarity and ignore structural associations among dispersed knowledge, which limits their effectiveness in MHQA tasks. GraphRAG methods address this by leveraging knowledge graphs (KGs) to capture structural associations, but they tend to overly rely on str…

Enter the Mind Palace: Reasoning and Planning for Long-term Active Embodied Question Answering
Muhammad Fadhil Ginting, Dong-Ki Kim, Xiangyun Meng, Andrzej Reinke, Bandi Jai Krishna, Navid Kayhani, Oriana Peltzer, David D. Fan, Amirreza Shaban, Sung-Kyun Kim, Mykel J. Kochenderfer, Ali-akbar Agha-mohammadi, Shayegan Omidshafiei
https://

Enter the Mind Palace: Reasoning and Planning for Long-term Active Embodied Question Answering
As robots become increasingly capable of operating over extended periods -- spanning days, weeks, and even months -- they are expected to accumulate knowledge of their environments and leverage this experience to assist humans more effectively. This paper studies the problem of Long-term Active Embodied Question Answering (LA-EQA), a new task in which a robot must both recall past experiences and actively explore its environment to answer complex, temporally-grounded questions. Unlike tradition…

Describe Anything Model for Visual Question Answering on Text-rich Images
Yen-Linh Vu, Dinh-Thang Duong, Truong-Binh Duong, Anh-Khoi Nguyen, Thanh-Huy Nguyen, Le Thien Phuc Nguyen, Jianhua Xing, Xingjian Li, Tianyang Wang, Ulas Bagci, Min Xu
https://arxiv.org/abs/2507.12441

Describe Anything Model for Visual Question Answering on Text-rich Images
Recent progress has been made in region-aware vision-language modeling, particularly with the emergence of the Describe Anything Model (DAM). DAM is capable of generating detailed descriptions of any specific image areas or objects without the need for additional localized image-text alignment supervision. We hypothesize that such region-level descriptive capability is beneficial for the task of Visual Question Answering (VQA), especially in challenging scenarios involving images with dense tex…

ExpliCIT-QA: Explainable Code-Based Image Table Question Answering
Maximiliano Hormaz\'abal Lagos, \'Alvaro Bueno S\'aez, Pedro Alonso Doval, Jorge Alcalde Vesteiro, H\'ector Cerezo-Costas
https://arxiv.org/abs/2507.11694

ExpliCIT-QA: Explainable Code-Based Image Table Question Answering
We present ExpliCIT-QA, a system that extends our previous MRT approach for tabular question answering into a multimodal pipeline capable of handling complex table images and providing explainable answers. ExpliCIT-QA follows a modular design, consisting of: (1) Multimodal Table Understanding, which uses a Chain-of-Thought approach to extract and transform content from table images; (2) Language-based Reasoning, where a step-by-step explanation in natural language is generated to solve the prob…

MobQA: A Benchmark Dataset for Semantic Understanding of Human Mobility Data through Question Answering
Hikaru Asano, Hiroki Ouchi, Akira Kasuga, Ryo Yonetani
https://arxiv.org/abs/2508.11163

MobQA: A Benchmark Dataset for Semantic Understanding of Human Mobility Data through Question Answering
This paper presents MobQA, a benchmark dataset designed to evaluate the semantic understanding capabilities of large language models (LLMs) for human mobility data through natural language question answering. While existing models excel at predicting human movement patterns, it remains unobvious how much they can interpret the underlying reasons or semantic meaning of those patterns. MobQA provides a comprehensive evaluation framework for LLMs to answer questions about diverse human GPS traje…

Anthem 2.0: Automated Reasoning for Answer Set Programming
Jorge Fandinno, Christoph Glinzer, Zachary Hansen, Jan Heuer, Yuliya Lierler, Vladimir Lifschitz, Torsten Schaub, Tobias Stolzmann
https://arxiv.org/abs/2507.11704

Anthem 2.0: Automated Reasoning for Answer Set Programming
Anthem 2.0 is a tool to aid in the verification of logic programs written in an expressive fragment of Clingo's input language named mini-gringo, which includes arithmetic operations and simple choice rules but not aggregates. It can translate logic programs into formula representations in the logic of here-and-there, and analyze properties of logic programs such as tightness. Most importantly, Anthem 2.0 can support program verification by invoking first-order theorem provers to confirm that a…

HistoryBankQA: Multilingual Temporal Question Answering on Historical Events
Biswadip Mandal, Anant Khandelwal, Manish Gupta
https://arxiv.org/abs/2509.12720 https://

HistoryBankQA: Multilingual Temporal Question Answering on Historical Events
Temporal reasoning about historical events is a critical skill for NLP tasks like event extraction, historical entity linking, temporal question answering, timeline summarization, temporal event clustering and temporal natural language inference. Yet efforts on benchmarking temporal reasoning capabilities of large language models (LLMs) are rather limited. Existing temporal reasoning datasets are limited in scale, lack multilingual coverage and focus more on contemporary events. To address thes…

Personalized Distractor Generation via MCTS-Guided Reasoning Reconstruction
Tao Wu, Jingyuan Chen, Wang Lin, Jian Zhan, Mengze Li, Kun Kuang, Fei Wu
https://arxiv.org/abs/2508.11184

Personalized Distractor Generation via MCTS-Guided Reasoning Reconstruction
Distractors, incorrect but plausible answer choices in multiple-choice questions (MCQs), play a critical role in educational assessment by diagnosing student misconceptions. Recent work has leveraged large language models (LLMs) to generate shared, group-level distractors by learning common error patterns across large student populations. However, such distractors often fail to capture the diverse reasoning errors of individual students, limiting their diagnostic effectiveness. To address this …

MapIQ: Benchmarking Multimodal Large Language Models for Map Question Answering
Varun Srivastava, Fan Lei, Srija Mukhopadhyay, Vivek Gupta, Ross Maciejewski
https://arxiv.org/abs/2507.11625

MapIQ: Benchmarking Multimodal Large Language Models for Map Question Answering
Recent advancements in multimodal large language models (MLLMs) have driven researchers to explore how well these models read data visualizations, e.g., bar charts, scatter plots. More recently, attention has shifted to visual question answering with maps (Map-VQA). However, Map-VQA research has primarily focused on choropleth maps, which cover only a limited range of thematic categories and visual analytical tasks. To address these gaps, we introduce MapIQ, a benchmark dataset comprising 14,70…