Papers with reasoning paradigms
POSITION BIAS MITIGATES POSITION BIAS: Mitigate Position Bias Through Inter-Position Knowledge Distillation (2025.emnlp-main)
Copied to clipboard
| Challenge: | Positional bias (PB) manifests as non-uniform sensitivity across contextual locations . previous studies have addressed PB by modifying the underlying architectures or employing extensive contextual awareness training. |
| Approach: | They propose a position-to-position knowledge distillation framework that leverages position-induced disparities to counteract PB. |
| Outcome: | The proposed framework reduces positional bias and improves performance on retrieval and reasoning tasks. |
Chain-of-Reasoning: Towards Unified Mathematical Reasoning in Large Language Models via a Multi-Paradigm Perspective (2025.acl-long)
Copied to clipboard
Yiyao Yu, Yuxiang Zhang, Dongdong Zhang, Xiao Liang, Hengyuan Zhang, Xingxing Zhang, Mahmoud Khademi, Hany Hassan Awadalla, Junjie Wang, Yujiu Yang, Furu Wei
| Challenge: | Existing work shows that LLMs rely on single-paradigm reasoning that limits their effectiveness across diverse tasks. |
| Approach: | They propose a new framework that integrates multiple reasoning paradigms to enable synergistic collaboration. |
| Outcome: | The proposed model outperforms current SOTA models in theorem proving tasks and the MATH benchmark in arithmetic tasks. |
Reason from Future: Reverse Thought Chain Enhances LLM Reasoning (2025.findings-acl)
Copied to clipboard
Yinlong Xu, Yanzhao Zheng, Shuoshuo Sun, Shuaihan Huang, Baohua Dong, Zhu Hangcheng, Ruohui Huang, Gang Yu, Hongxia Xu, Jian Wu
| Challenge: | Existing reasoning paradigms that focus on local optimum reasoning lack global perspective. |
| Approach: | They propose a bidirectional reasoning paradigm that generates reasoning paths by bidirectional planning and bottom-up reasoning accumulation. |
| Outcome: | The proposed reasoning paradigm outperforms conventional paradigms with higher accuracy and less searching space to solve complex tasks. |
A Survey of Inductive Reasoning for Large Language Models (2026.acl-long)
Copied to clipboard
Kedi Chen, Dezhao Ruan, Yuhao Dan, Yaoting Wang, Siyu Yan, Xuecheng Wu, Yinqi Zhang, Qin Chen, Jie Zhou, Liang He, Biqing Qi, Linyang Li, Qipeng Guo, Xiaoming Shi, Wei Zhang
| Challenge: | Inductive reasoning is an important task for large language models (LLMs). |
| Approach: | They propose a survey of inductive reasoning for large language models . they categorize methods into three main areas: post-training enhancement, test-time exploration, and data augmentation. |
| Outcome: | The proposed method improves inductive reasoning in large language models. |
Fundamental Reasoning Paradigms Induce Out-of-Domain Generalization in Language Models (2026.findings-acl)
Copied to clipboard
Mingzi Cao, Xingwei Tan, Mahmud Elahi Akhter, Marco Valentino, Maria Liakata, Xi Wang, Nikolaos Aletras
| Challenge: | Deduction, induction, and abduction are fundamental reasoning paradigms, core for human logical thinking. |
| Approach: | They propose to use a dataset of symbolic tasks to induce deductive skills into large language models (LLMs) they then use FT to fine-tune models to improve OOD generalization . |
| Outcome: | The proposed approach yields strong generalizability with substantial performance gains (up to 14.60) across realistic out-of-domain tasks. |
Programming over Thinking: Efficient and Robust Multi-Constraint Planning (2026.acl-long)
Copied to clipboard
| Challenge: | Existing large language model approaches lack flexibility in multi-constraint planning . SCOPE achieves state-of-the-art performance while lowering cost and latency . |
| Approach: | They propose a framework that disentangles query-specific problem reasoning from generic code execution. |
| Outcome: | The Scalable Code Planning Engine achieves state-of-the-art performance while lowering cost and latency. |