| Challenge: | syllogistic reasoning models often mistake what sounds true for what is formally valid . content effect is a limitation of sluggish reasoning, which can lead to invalid conclusions . eisape et al., 2024: a key open problem for formal inference in natural language. |
| Approach: | They propose a schema-guided framework that disentangles semantic plausibility from logical validity. |
| Outcome: | The proposed framework outperforms existing frameworks while reducing CE. |
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| Challenge: | a number of theories have been proposed to account for content effects in large language models, including the dual-process theory of reasoning, but the mechanisms behind content effects remain unclear. |
| Approach: | They propose to encode validity and plausibility concepts in LLMs by aligning them in representational geometry. |
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Reasoning Circuits in Language Models: A Mechanistic Interpretation of Syllogistic Inference (2025.findings-acl)
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| Challenge: | Recent studies on reasoning in language models have sparked a debate on whether they can learn systematic inferential principles or merely exploit superficial patterns in the training data. |
| Approach: | They propose a method for circuit discovery aimed at interpreting syllogistic inference . they uncover a circuit involving middle-term suppression that elucidates how LMs transfer information to derive valid conclusions from premises. |
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A Systematic Analysis of Large Language Models as Soft Reasoners: The Case of Syllogistic Inferences (2024.emnlp-main)
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| Challenge: | syllogistic reasoning is a deductive reasoning skill that is crucial in everyday problem-solving and decision-making experiences. |
| Approach: | They propose to study the reasoning abilities of Large Language Models (LLMs) they propose to use supervised fine-tuning and chain-of-thought reasoning to investigate their results. |
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Disentangling Logic: The Role of Context in Large Language Model Reasoning Capabilities (2025.findings-acl)
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Wenyue Hua, Kaijie Zhu, Lingyao Li, Lizhou Fan, Mingyu Jin, Shuhang Lin, Haochen Xue, Zelong Li, Jindong Wang, Yongfeng Zhang
| Challenge: | Using large language models, large language model models can be used to evaluate reasoning abilities in context-rich scenarios. |
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SylloBio-NLI: Evaluating Large Language Models on Biomedical Syllogistic Reasoning (2025.naacl-long)
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| Challenge: | Existing models are far from achieving the robustness and consistency required for safe biomedical NLI applications. |
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| Challenge: | Scaling LLM-based agents to long-horizon deep research is constrained by context-noise trade-off . solving a single query may require hundreds of interactions with noisy environments . |
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Dissecting Logical Reasoning in LLMs: A Fine-Grained Evaluation and Supervision Study (2025.findings-emnlp)
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Yujun Zhou, Jiayi Ye, Zipeng Ling, Yufei Han, Yue Huang, Haomin Zhuang, Zhenwen Liang, Kehan Guo, Taicheng Guo, Xiangqi Wang, Xiangliang Zhang
| Challenge: | Existing benchmarks that rely on final-answer accuracy fail to capture the quality of the reasoning process. |
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Understanding and Patching Compositional Reasoning in LLMs (2024.findings-acl)
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| Challenge: | LLMs have marked a revolutonary shift, yet they falter when faced with compositional reasoning tasks. |
| Approach: | They propose a lightweight method to patch compositional reasoning errors via editing the located MHSA modules in LLMs. |
| Outcome: | The proposed method can be used to patch compositional reasoning errors using MHSA modules located within the layers of the LLMs. |
ReportLogic: Evaluating Logical Quality in Deep Research Reports (2026.acl-long)
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| Challenge: | Existing evaluation frameworks that evaluate large language models for Deep Research largely ignore this requirement. |
| Approach: | They propose a benchmark that quantifies report-level logical quality through a reader-centric lens of auditability. |
| Outcome: | The proposed model quantifies logical quality through a reader-centric lens of auditability. |
Verifiable, Debuggable, and Repairable Commonsense Logical Reasoning via LLM-based Theory Resolution (2024.emnlp-main)
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| Challenge: | Recent advances in Large Language Models (LLMs) have led to substantial interest in their application to commonsense reasoning tasks. |
| Approach: | They propose a logical reasoning framework that integrates commonsense knowledge with a verifiable logical framework that mitigates hallucinations and facilitates debugging. |
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