Papers with dynamic reasoning
Hierarchical Representation-based Dynamic Reasoning Network for Biomedical Question Answering (2022.coling-1)
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Jianguo Mao, Jiyuan Zhang, Zengfeng Zeng, Weihua Peng, Wenbin Jiang, Xiangdong Wang, Hong Liu, Yajuan Lyu
| Challenge: | Existing models of biomedical question answering are limited in their ability to predict answers . a new model improves the performance of existing models, but the code will be released after the paper is published. |
| Approach: | They propose a hierarchical representation-based dynamic reasoning network to solve biomedical problems. |
| Outcome: | The proposed model significantly improves on three mainstream biomedical datasets . the code will be released after the paper is published . |
ReKG-MCTS: Reinforcing LLM Reasoning on Knowledge Graphs via Training-Free Monte Carlo Tree Search (2025.findings-acl)
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| Challenge: | Existing approaches to combining knowledge graphs with large language models face limitations in path exploration strategies or excessive computational overhead. |
| Approach: | They propose a training-free framework that synergizes Monte Carlo Tree Search with LLM capabilities to enable dynamic reasoning over KGs. |
| Outcome: | The proposed framework outperforms existing training-free methods and achieves competitive performance compared to fine-tuned baselines. |
Escaping the Sisyphus Dilemma: Experience Replay for Robust Text-to-Optimization Modeling (2026.findings-acl)
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| Challenge: | Existing retrieval-augmented strategies for large language models fail to capture dynamic reasoning required to resolve execution failures. |
| Approach: | They propose a framework that implements Experience Replay to transform transient rectification steps into persistent knowledge. |
| Outcome: | The proposed framework improves model accuracy by 8.45% on complex tasks while reducing token consumption by 28.65% and interaction turns by 25.82%. |
PsyPath: Psychologically-guided Self-Exploration for Personality Detection (2026.findings-acl)
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| Challenge: | Personality detection aims to label traits via identifying linguistic cues from written text. |
| Approach: | They propose a framework that allows large language models to generate and answer psychologically meaningful questions and a hybrid scoring mechanism to evaluate the generated nodes in the reasoning paths. |
| Outcome: | The proposed framework outperforms baselines on two benchmark datasets and significantly improves performance and interpretability in downstream tasks. |