Papers with generalized reasoning
Learning to Explain: Datasets and Models for Identifying Valid Reasoning Chains in Multihop Question-Answering (2020.emnlp-main)
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| Challenge: | despite rapid progress in multihop question-answering, models still have trouble explaining why an answer is correct. |
| Approach: | They propose three explanation datasets in which explanations from corpus facts are annotated . they first annotate multiple candidate explanations for each answer, then use crowd-sourcing perturbations to test generalization . |
| Outcome: | The proposed datasets improve explanation quality but still behind the upper bound . the proposed dataset can be used to improve explanations using a BERT-based classifier . |
Reasoning Beyond Literal: Cross-style Multimodal Reasoning for Figurative Language Understanding (2026.findings-eacl)
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| Challenge: | figurative language is essential for expressing intent, emotion, and perspective . figural language is often dependent on Styles Reasoning, causing incongruities between expressions . |
| Approach: | They propose a framework that induces reasoning capabilities to compact vision–language models . figurative language is essential in expressing intent, emotion, and perspective . |
| Outcome: | The proposed framework can interpret multimodal figurative language, provide transparent reasoning traces, and generalize across multiple figurativ styles. |
Warm Up Before You Train: Unlocking General Reasoning in Resource-Constrained Settings (2025.emnlp-main)
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| Challenge: | Reasoning-capable large language models (LLMs) have driven a major shift in artificial intelligence . these models generate long CoTs, capturing reasoning behaviors such as self-reflection, self-correction, and hypothesis testing. |
| Approach: | They propose a sample-efficient, two-stage training strategy to build reasoning LLMs . they "warm up" a model by distilling Long CoTs from a toy domain to acquire general reasoning skills . |
| Outcome: | The proposed training strategy outperforms existing models on a range of tasks. |
Synapse: Empowering LLM Agents with Episodic-Semantic Memory via Spreading Activation (2026.findings-acl)
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Hanqi Jiang, Junhao Chen, Yi Pan, Ling Chen, Weihang You, Yifan Zhou, Ruidong Zhang, Yohannes Abate, Tianming Liu
| Challenge: | Large Language Models excel at generalized reasoning, but lack the ability to accumulate experiences and maintain narrative coherence over long horizons. |
| Approach: | They propose a unified memory architecture that transcends static vector similarity. |
| Outcome: | The proposed model outperforms state-of-the-art methods in temporal and multihop reasoning tasks. |