Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies

RoBERTa: A Robustly Optimized BERT Pretraining Approach

Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019) · arXiv · doi:10.48550/arxiv.1907.11692 · Cited by 16,515

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.703 · Cited by 8,821

SQuAD: 100,000+ Questions for Machine Comprehension of Text

Rajpurkar, P., Zhang, J., Lopyrev, K., & Liang, P. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d16-1264 · Cited by 6,208

A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference

Williams, A., Nangia, N., & Bowman, S. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1101 · Cited by 3,342

Natural Questions: A benchmark for question answering research

Kwiatkowski, T., Palomaki, J., Redfield, O., Collins, M., Parikh, A., Alberti, C., Epstein, D., Polosukhin, I., Devlin, J., Lee, K., et al. (2019) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00276 · Cited by 2,069

HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering

Yang, Z., Qi, P., Zhang, S., Bengio, Y., Cohen, W., Salakhutdinov, R., & Manning, C. D. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1259 · Cited by 1,804

Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

Mihaylov, T., Clark, P. E., Khot, T., & Sabharwal, A. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1260 · Cited by 1,602

Annotation Artifacts in Natural Language Inference Data

Gururangan, S., Swayamdipta, S., Levy, O., Schwartz, R., Bowman, S., & Smith, N. A. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers) · doi:10.18653/v1/n18-2017 · Cited by 907

BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

Clark, C., Lee, K., Chang, M.-W., Kwiatkowski, T., Collins, M., & Toutanova, K. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1300 · Cited by 655

A Corpus for Reasoning about Natural Language Grounded in Photographs

Suhr, A., Zhou, S., Zhang, A. Q., Zhang, I., Bai, H., & Artzi, Y. (2019) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p19-1644 · Cited by 634

Exploiting Latent and Implicit Chain of Thought for Efficient Multi-Hop Question Answering

Shakil, H., Srinivasan, V., Jeelani, H., Gunaratna, K., & Chappidi, S. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11461764

CoV-ME: Memory Editing for Self-Consistent Reasoning in Large Language Models

Hossain, M. T., Nourani, M. S., & Sultana, N. (2025) · IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things (RAAICON) · doi:10.1109/raaicon69033.2025.11502550

HERTy-Wiki: A Benchmark for Hierarchical Entity Reasoning and Typing in Wikidata

Obretincheva, N., Yannakoudakis, H., & Simperl, E. P. B. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-25159-6_15

Automating structured reasoning with entity-driven semantic simplification in Llms

Liao, J., Yan, C., Wang, P., & Wan, Y. (2026) · Systems Science & Control Engineering · doi:10.1080/21642583.2026.2672171

Thoughts-as-Planning: Latent World Models for Chain-of-Thoughts Optimization via Reinforcement PlanningPreprint

Liu, D., Yu, Y., & Wu, Y. N. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.10.724161

Measuring Reasoning Quality in LLMs: A Multi-Dimensional Behavioral Framework

Şenol, A., Agrawal, G., & Liu, H. (2026) · Big Data and Cognitive Computing · doi:10.3390/bdcc10090300 · Cited by 3

PriRAG: Privacy-Preserving Inference for Retrieval-Augmented Large Language Models

Wang, H., Huang, Y., Zheng, W., Fan, P., Nan, G., Wang, S., Xu, M., & Qi, T. (2026) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2026.3698487

Eyes Can’t Always Tell: Fusing Eye Tracking and User Priors for User Modeling under AI Advice Conditions

Sun, X., Wei, S., Pan, T., Wang, Y., Bosch, J. A., Echizen, I., Ali, A. E., & Sugawara, S. (2026) · ACM Conference on User Modeling, Adaptation and Personalization (UMAP) · doi:10.1145/3774935.3806190 · Cited by 1

Reasoning That Leaks, Fine-Tuning That Amplifies: Exposing the Hidden Threats of Chain-of-Thought Models

Xu, Z., Gardiner, J., & Belguith, S. (2026) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/3779208.3785271

Adaptive Beam Search with Shannon Entropy for Data-Centric Reasoning in LLMs

Kim, Y., Jeong, Y., Kim, J., & Cho, S. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-1468-6_43

ASTRA: Adversarial Stealthy Trigger Reasoning Attacks for Black-Box LLMs

Park, S.-G., Park, S., & Choi, D. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-1468-6_41

Reasoning-targeted Jailbreak Attacks on Large Reasoning Models via Semantic Triggers and Psychological Framing

Wang, Z., & Wang, L. (2026) · Machine Intelligence Research · doi:10.1007/s11633-025-1602-0

DDPT: Enhancing complex reasoning in large language models via distillation and dynamic prompt tuning

遠藤, 格., Shen, C., Wang, W., Fan, S., Xie, L., Tian, X., Chen, P., Liu, X. D., Chen, Y., & Ye, J. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.134297

Automatic prompt generation via semantic decomposition-and-recomposition for multi-hop question answering

Lee, S., & Lee, D.-G. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.115378

DynaChain: On-Demand Retrieval-Augmented Generation via Dynamic Reasoning Chain Correction

Cui, H., Chen, Y., Kou, X., Ding, Y., & Wang, H. (2026) · International Conference on Artificial Intelligence and Industrial Technology Applications (AIITA) · doi:10.1109/aiita69518.2026.11567339

Agentic Self-RAG: Multi-Agent Reasoning for Self-Correcting Retrieval-Augmented Generation

Hassan, S. B., -, A., & Abbas, M. (2026) · International Conference on IT and Industrial Technologies (ICIT) · doi:10.1109/icit68548.2026.11577708 · Cited by 1

Self-Awareness before Action: Mitigating Logical Inertia via Proactive Cognitive Awareness

Fan, F., Liu, P., FengZhe, L., Yang, S., & Yan, G. (2026) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2026.findings-acl.793

Temporal Structure in LLM Reasoning: Analyzing Hidden States and Semantic Embeddings

Otomura, K., Nakamura, K., Kobayashi, K., Nakamura, M., Hase, R., & Hato, J. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-29532-3_21

D$$^3$$: Dispute-Driven Debate for Stabilizing Multi-agent Reasoning in Large Language Models

Liu, Y., Sun, S. Y., Wang, Y., Zhang, S., & Li, J. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-2852-2_47

CommonWhy: A Dataset for Evaluating Entity-Based Causal Commonsense Reasoning in Large Language Models

Toroghi, A., Kalarde, F. M., & Sanner, S. (2026) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3805712.3808628

Reward Shaping for Robust Refusal in Small Language Models for Retrieval-Augmented Question Answering

Rajapakse, T. C., & de Rijke, M. (2026) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3805712.3809891

SARAG: Sufficiency-Aware Retrieval Augmented Generation

Zheng, Z., Zhang, Y., Wang, Y., Luo, W., & Zuo, L. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3417-2_37

A Survey of Commonsense Reasoning in LLMs

Teo, N., Huang, D., & Wang, Z. (2026) · ACM Computing Surveys · doi:10.1145/3832753

IMRDF: A Training-Free Framework for Memory-Reasoning Decoupling in Large Language Models

Li, Y., Weng, Z., Cai, Z., & Yang, B. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3426-4_25

CoTMind: A closed-loop framework for optimizing Chain-of-Thought reasoning in complex question answering

Lu, G., Zhan, W., Peng, L., & Li, L. (2026) · Information Processing & Management · doi:10.1016/j.ipm.2026.104980

Retrieval is NOT always needed: Exploring the timing of retrieval in dynamic retrieval-augmented generation

Liu, Y., Huang, H., Yang, Y., Zeng, Z., Zhou, Y., Wu, Z., & Gao, Y. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.133921

SPOT: Span-level Pause-of-Thought for Efficient and Interpretable Latent Reasoning in Large Language Models

Chu, Y., Shao, M., Liu, Y., Hao, B., Lin, Y., Wang, J., & Wang, R. (2026) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3770855.3818055 · Cited by 4

Minimal AIF-inspired response control for evidence-aware and dependable LLM behavior

Feng, C., Guo, Y., & Wang, G. (2026) · Computing · doi:10.1007/s00607-026-01723-z

Data and Knowledge Dual-Driven Text Embedding Approaches: A Survey

Chen, X., 詹佳青, Lin, J., Liu, H., Zhang, Q., Chen, J., & Chen, H. (2026) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2026.3721883

Structure-driven dynamic chain-of-thought reasoning: Integrating uncertainty-driven active learning and entity-relation modeling

Gao, J., Chen, X., Miao, D., Zhang, H., Qin, X., & Lu, P. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.134106

ANCHOR: Enhancing complex reasoning in large language models via metacognitive task anchoring

Wang, J., Zheng, C., Tang, Z., & Xie, X. (2026) · Neurocomputing · doi:10.1016/j.neucom.2026.134855

GS-Chaff: Multi-Agent Prompt-Level Semantic Chaffing for Privacy-Preserving LLM Inference

Zhou, Q., Wang, Z., Yue, Z., Cai, L., Liu, K., & Qin, C. (2026) · Sensors · doi:10.3390/s26175385

A survey on synergizing large language models and knowledge graphs for reasoning

Wang, X., Bai, H., Huang, S., Wu, G., Yang, D., Wang, S., Liao, J., Zhang, Y., Liu, M., & Liu, L. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.116132

Large or Small? Cloud or Edge? Investigating the Use of Language Models for Smart Drone Services

He, P., Liu, X., Xu, J., Kua, J., & Jia, D. (2026) · IEEE International Conference on Web Services (ICWS) · doi:10.1109/icws72778.2026.00018

Personality Matters: Task-Specific Effects of Psychological Framings in Agentified LLMs

Bellan, P., Magnolini, S., Haez, S. G., Sanna, L., & Dragoni, M. (2025) · IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) · doi:10.1109/wi-iat67162.2025.00060

Verifier-Guided Risk-Aware Search for LLM Reasoning Under Budget Constraints

Zhao, L., Qin, S., & Shi, Y. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-38410-2_35

LLM Prompt Optimization: From Zero-Shot to Automatic Role-Playing Generation Using Ontology

Hauna, A. D. A., Khomsah, S., Yunus, A. P., & Fukui, M. (2026) · Journal of Advanced Computational Intelligence and Intelligent Informatics · doi:10.20965/jaciii.2026.p1404

Load-Aware Adaptive Degradation Scheduling for Multi-User Concurrent Edge LLM Inference

Tan, Y.-H., Wu, W.-Y., & Zhang, C. (2026) · 2026 5th International Conference on Artificial Intelligence, Internet of Things and Cloud Computing Technology (AIoTC) · doi:10.1109/aiotc71250.2026.11688797

Towards Adaptive Reverse Thinking in Large Language Models: Quantifying and Optimizing

Fang, A., & 朝, 乐. (2026) · Data Intelligence · doi:10.3724/2096-7004.di.2026.2092

Shortcut-Aware Logic-Sinking Distillation with Budget-Adaptive Decision Control for Efficient Long-Horizon Reasoning

Feng, J., Guo, J., He, Y., Zeng, Q., Wang, K., Lin, S., & Lai, J. (2026) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001426510183

COMPASS: Efficient Structured Reasoning Via Action Policy Compression*

Li, K., 杜友田, Xu, Z., Gou, Y., Chen, R., & Yan, C.-B. (2026) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case69030.2026.11704303

ReVerTree: Recursive Verification Tree for Self-checking Mathematical Reasoning

You, C., Zhang, T., Chen, Q., Wang, C., & He, X. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-5696-9_14

Your Agent’s Memories Are Not Its Own: Forged Reasoning Attack on LLM Agent Memory and Its Defense

Karamchandani, N., Nagasubramaniam, P., Zhu, S., & Wu, D. (2026) · IEEE Conference on Communications and Network Security · doi:10.1109/cns69863.2026.11720906 · Cited by 8

Contrastive Chain-of-Thought Decoding for Large Language Models to Mitigate Inference Hallucination

Long, Q., Li, X., Liu, Y., Wu, H., & Lin, L. (2026) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn69854.2026.11729174

RL-Enhanced ProRAG: A Proactive Retrieval-Augmented Generation Framework for Multi-Hop Reasoning

Luo, X., Huo, Y., Qin, Y., Feng, F., Liu, G., Xia, D., & Wong Derek, F. (2026) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn69854.2026.11728741

When to Nudge? Budget-Constrained Test-Time Coordination for Reliable LLM Decision Making

Yu, C.-Y., & Zhang, W. (2026) · International Conference on Machine Learning and Applications · doi:10.1109/icmla69890.2026.00152

Budget-Aware Verifier-Guided Test-Time Reasoning for Efficient Large Language Models

Wang, S., Zhang, B., Gong, Y., & He, C. (2026) · doi:10.1109/iotaiee70863.2026.11749415

The Landscape of Emerging AI Agent Architectures for Reasoning, Planning, and Tool Calling: A Survey

Masterman, T., Besen, S., Sawtell, M., & Chao, A. (2024) · arXiv · doi:10.48550/arxiv.2404.11584 · Cited by 35

BMGQ: A Bottom-up Method for Generating Complex Multi-hop Reasoning Questions from Semi-structured DataPreprint

Qiu, B., Liu, Z., Liu, X., Wang, B., Zhang, F., Qin, Y., Li, C., Yang, H., & Gao, Z. (2025) · arXiv (Cornell University) · doi:10.48550/arxiv.2510.24151 · Cited by 2

Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps

Ho, X., Duong Nguyen, A.-K., Sugawara, S., & Aizawa, A. (2020) · Proceedings of the 28th International Conference on Computational Linguistics · doi:10.18653/v1/2020.coling-main.580 · Cited by 287

Disentangled Retrieval and Reasoning for Implicit Question Answering

Liu, Q., Geng, X., Wang, Y., Cambria, E., & Jiang, D. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3220933 · Cited by 11

Inferring Implicit Relations in Complex Questions with Language Models

Katz, U., Geva, M., & Berant, J. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.188 · Cited by 13

AutoReason: Automatic Few-Shot Reasoning DecompositionPreprint

Sevinc, A., & Gümüş, A. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2412.06975 · Cited by 2