Large Language and Reasoning Models are Shallow Disjunctive Reasoners (2025.acl-long)
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| Challenge: | Large Language Models (LLMs) struggle with systematic reasoning on out-of-distribution (OOD) tasks. |
| Approach: | They propose to use a set of constraints to measure OOD generalization to create large reasoning models that can be leveraged to solve real-world problems. |
| Outcome: | The proposed models outperform their LLM counterparts in single-path reasoning tasks but struggle in multi-path setting. |
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| Challenge: | Recent literature has shown that large language models are excellent few-shot reasoners to solve text reasoning tasks. |
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| Challenge: | Recent work shows that large reasoning models arrive at the correct answer before completing textual reasoning steps, indicating the presence of latent reasoning. |
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There’s No Such Thing as Simple Reasoning for LLMs (2025.findings-acl)
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| Challenge: | Existing work has focused on relatively complex “many-hop” reasoning problems. |
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| Challenge: | syllogistic reasoning is a deductive reasoning skill that is crucial in everyday problem-solving and decision-making experiences. |
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| Challenge: | a lightweight technique trains only new token embeddings on axioms and evaluates them on unseen tasks. |
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| Challenge: | Recent attempts at prompt decomposition toward solving complex, multi-step reasoning problems depend on the ability of the LLM to simultaneously decompose and solve the problem. |
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Complex Reasoning in Natural Language (2023.acl-tutorials)
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| Challenge: | Recent research shows that pretrained language models are often brittle for complex reasoning tasks. |
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Large Language Models for Mathematical Reasoning: Progresses and Challenges (2024.eacl-srw)
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| Challenge: | a survey examines the landscape of mathematical problem-solving techniques . large language models have proven to be potent assets in unraveling nuances of mathematical reasoning . |
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Concise and Organized Perception Facilitates Reasoning in Large Language Models (2025.findings-naacl)
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Over-Reasoning and Redundant Calculation of Large Language Models (2024.eacl-short)
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| Challenge: | Large language models (LLMs) can solve problems step-by-step, but it is unclear whether they know when to use CoT and whether they are always necessary. |
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