Papers with reasoning mechanisms
Working Memory Networks: Augmenting Memory Networks with a Relational Reasoning Module (P18-1)
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| Challenge: | Recent advances in deep neural networks have enabled complex reasoning tasks. |
| Approach: | They propose a MemNN architecture with a working memory storage and reasoning module that retains relational reasoning abilities of relation networks while reducing computational complexity. |
| Outcome: | The proposed model retains the relational reasoning abilities of the RN while reducing its computational complexity from quadratic to linear. |
Can LLMs Reason Like Doctors? Exploring the Limits of Large Language Models in Complex Medical Reasoning (2026.findings-eacl)
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| Challenge: | Large language models (LLMs) have shown remarkable progress in reasoning across multiple domains, but it remains unclear whether their abilities reflect genuine reasoning or sophisticated pattern matching. |
| Approach: | They conduct one of the largest evaluations to date, assessing 77 LLMs . they select three medical question answering (QA) benchmarks targeting reasoning processes . |
| Outcome: | The results highlight the need to improve specific reasoning strategies to better reflect medical decision-making. |
Mining Complex Patterns of Argumentative Reasoning in Natural Language Dialogue (2025.acl-long)
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| Challenge: | Argumentation scheme mining is the task of automatically identifying reasoning mechanisms behind argument inferences. |
| Approach: | They propose to create a corpus of 441 arguments annotated with 24 argumentation schemes and leverage the capabilities of LLMs and Transformer-based models to validate their applicability in real-world scenarios. |
| Outcome: | The proposed corpus of arguments is pre-trained on a large corpus containing textbook-like argumentation schemes and validates their applicability in real-world scenarios. |
ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering (2022.emnlp-main)
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| Challenge: | Recent advances in large pre-trained language models have brought the NLP field into a new era. |
| Approach: | They propose a large-scale dataset to study the chain of numerical reasoning in conversational question answering. |
| Outcome: | The proposed dataset should push forward the exploration of real-world, complex reasoning tasks as the next research focus. |
BBA: Bi-Modal Behavioral Alignment for Reasoning with Large Vision-Language Models (2024.findings-acl)
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Xueliang Zhao, Xinting Huang, Tingchen Fu, Qintong Li, Shansan Gong, Lemao Liu, Wei Bi, Lingpeng Kong
| Challenge: | Multimodal reasoning is a key capability for large vision-language models . however, the vanilla Chain-of-Thought method fails to address critical steps in multi-step reasoning tasks. |
| Approach: | They propose a bi-modal Behavioral Alignment method to augment multimodal reasoning . they use domain-specific language to integrate multimodal information into a precise alternative form . |
| Outcome: | The proposed method significantly improves GPT-4V(ision) on geometry problem solving, chess positional advantage prediction and molecular property prediction. |
Reason to Rote: Rethinking Memorization in Reasoning (2025.emnlp-main)
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| Challenge: | Large language models readily memorize arbitrary training instances, such as label noise . however, such memorization does not affect generalizable reasoning abilities . |
| Approach: | They investigate how large language models memorize label noise and why it affects generalizability. |
| Outcome: | The proposed model performs well on reasoning tasks even when memorized labels are missing . the proposed model is able to generalize to correctly answer "87+19=106" |
Beyond Length Scaling: Synergizing Breadth and Depth for Generative Reward Models (2026.findings-acl)
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| Challenge: | Recent advances in Generative Reward Models have demonstrated that scaling the length of Chain-of-Thought reasoning enhances reliability of evaluation. |
| Approach: | They propose a framework that reconfigures raw rationales into structured Breadth-CoT and Depth-Co T through a modular synthesis pipeline. |
| Outcome: | The proposed framework surpasses open-source RMs by an average of 8.2%. |
Towards a Mechanistic Understanding of Large Reasoning Models: A Survey of Training, Inference, and Failures (2026.acl-long)
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Yi Hu, Jiaqi Gu, Ruxin Wang, Zijun Yao, Hao Peng, Xiaobao Wu, Jianhui Chen, Muhan Zhang, Liangming Pan
| Challenge: | Recent research has shown that reinforcement learning can elicit intriguing emergent reasoning behaviors. |
| Approach: | They propose a comprehensive survey of the mechanistic understanding of large reasoning models . they organize findings into three core dimensions: 1) training dynamics, 2) reasoning mechanisms, and 3) unintended behaviors. |
| Outcome: | This paper synthesizes the mechanistic understanding of large reasoning models into three dimensions . authors outline a roadmap for future studies including improved interpretability and methodologies . |