Papers with memory module
iPET: An Interactive Emotional Companion Dialogue System with LLM-Powered Virtual Pet World Simulation (2025.acl-demo)
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Zheyong Xie, Shaosheng Cao, Zuozhu Liu, Zheyu Ye, Zihan Niu, Chonggang Lu, Tong Xu, Enhong Chen, Zhe Xu, Yao Hu, Wei Lu
| Challenge: | Existing approaches to role-playing emotional companion products lack sustained personalization and contextual adaptability, limiting their effectiveness in real-world settings. |
| Approach: | They propose a virtual pet agent that can enhance user engagement through rich, dynamic pet behaviors and interactions tailored to individual preferences. |
| Outcome: | The proposed system has been deployed in a real-world, non-commercial product for 200 days and has demonstrated its effectiveness in practical applications. |
GenPilot: A Multi-Agent System for Test-Time Prompt Optimization in Image Generation (2025.findings-emnlp)
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Wen Ye, Zhaocheng Liu, Gui Yuwei, Tingyu Yuan, Yunyue Su, Bowen Fang, Chaoyang Zhao, Qiang Liu, Liang Wang
| Challenge: | Existing methods for text-to-image synthesis lack systematic error analysis and refinement strategies, resulting in limited reliability and effectiveness. |
| Approach: | They propose a plug-and-play multi-agent system called GenPilot that integrates error analysis, clustering-based adaptive exploration, fine-grained verification and a memory module for iterative optimization. |
| Outcome: | The proposed method improves text consistency and structural coherence on images with a plug-and-play system. |
Lifelong Event Detection with Embedding Space Separation and Compaction (2024.naacl-short)
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| Challenge: | Existing methods for event detection are prone to forgetting due to overlap between memory data and the previously learned embedding space. |
| Approach: | They propose a method that embeds feature distributions away from the previous embedding space and mitigates overfitting by a memory calibration mechanism. |
| Outcome: | The proposed method outperforms existing state-of-the-art methods with extensive experiments. |
Stateful Memory-Augmented Transformers for Efficient Dialogue Modeling (2024.findings-eacl)
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| Challenge: | Existing Transformers models are computationally expensive for long context inputs. |
| Approach: | They propose a transformer that can interchange information between memory states and context . they evaluate the efficiency of their model on three dialogue datasets and two language datasets . |
| Outcome: | The proposed model is compatible with existing transformer models and can preserve dialogue history information. |
Recall and Learn: A Memory-augmented Solver for Math Word Problems (2021.findings-emnlp)
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| Challenge: | Existing methods for solving math word problems are based on template-based generation which results in limited generalization capability. |
| Approach: | They propose a human-like analogical learning method for the math word problem . it uses modules of memory, representation, analogy, and reasoning to make a new exercise . |
| Outcome: | The proposed method outperforms state-of-the-art models on two well-known datasets. |
Improving Continual Relation Extraction through Prototypical Contrastive Learning (2022.coling-1)
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| Challenge: | Continual relation extraction (CRE) aims to extract relations towards the continuous and iterative arrival of new data, of which the major challenge is the catastrophic forgetting of old tasks. |
| Approach: | They propose a Continual Relation Extraction framework with Contrastive Learning which is built with a classification network and a prototypical contrastive network to achieve incremental-class learning of CRE. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods on two public datasets and proves its effectiveness on improving performance. |
An Iterative Associative Memory Model for Empathetic Response Generation (2024.acl-long)
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| Challenge: | Existing methods for empathetic response generation ignore the associated words between dialogue utterances. |
| Approach: | They propose an iterative associative memory model to capture associated words between dialogue utterances and situations, dialogue history, and a memory module for storing associated words. |
| Outcome: | The proposed model captures key words between dialogue utterances and situations, dialogue history, and a memory module, thereby accurately and nuancedly comprehending the utterables. |
Lifelong Learning of Hate Speech Classification on Social Media (2021.naacl-main)
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| Challenge: | Existing work on automated hate speech classification assumes that the dataset is fixed and the classes are pre-defined. |
| Approach: | They propose to use Variational Representation Learning and a load-balancing self-organizing inductive neural network to learn hate speech classification on social media. |
| Outcome: | The proposed model improves on the lifelong learning techniques on social media. |
Incremental Prompting: Episodic Memory Prompt for Lifelong Event Detection (2022.coling-1)
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| Challenge: | Existing methods to improve lifelong event detection performance are limited by the limited stored examples. |
| Approach: | They propose to use Episodic Memory Prompts to explicitly retain the learned task-specific knowledge. |
| Outcome: | The proposed method can be used to update a model with new event types while retaining the capability on previously learned types. |
CHIME: Cross-passage Hierarchical Memory Network for Generative Review Question Answering (2020.coling-main)
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| Challenge: | Existing QA systems deal with factoid questions and assume a simplified setup such as multiple-choice questions, retrieving spans of text from given documents, and filling in the blanks. |
| Approach: | They propose a cross-passage hierarchical memory network for question answering via text generation that extends XLNet by adding an auxiliary memory module to the context memory and answer memory. |
| Outcome: | The proposed architecture outperforms the state-of-the-art generative QA framework with better syntactically well-formed answers and increased precision on the AmazonQA review dataset. |
MART: Memory-Augmented Recurrent Transformer for Coherent Video Paragraph Captioning (2020.acl-main)
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| Challenge: | Existing methods for generating paragraph descriptions for videos require a coherent paragraph and a higher level of coherence. |
| Approach: | They propose a new method that generates a summarized memory state from video segments and sentence history to help better predict the next sentence. |
| Outcome: | The proposed method generates more coherent and less repetitive paragraph captions while maintaining relevance to the input video events. |
MANNER: A Variational Memory-Augmented Model for Cross Domain Few-Shot Named Entity Recognition (2023.acl-long)
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| Challenge: | Named Entity Recognition (NER) is a fundamental NLP task that aims at classifying mention spans into entity types. |
| Approach: | They propose a variational memory-augmented few-shot named entity recognition model that uses a memory module to store information from source domain and retrieve relevant information from the memory to augment few-shot task in target domain. |
| Outcome: | The proposed model can adapt the learned knowledge from source domain to target domain and achieve superior performance on English and Chinese cross domain few-shot NER datasets. |
PEAR: Planner-Executor Agent Robustness Benchmark (2026.findings-eacl)
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| Challenge: | Existing studies examine isolated attack surfaces or specific scenarios, leaving a lack of holistic understanding of MAS vulnerabilities. |
| Approach: | They propose a benchmark to evaluate the utility and vulnerability of planner–executor MAS. |
| Outcome: | The proposed benchmark evaluates planner–executor MAS on a widely adopted design. |
Learning Multimodal Contrast with Cross-modal Memory and Reinforced Contrast Recognition (2024.findings-acl)
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| Challenge: | Using a memory module, we learn multimodal contrast using encoding-decoding paradigm . multimodal information are used in many applications, including news feeding, social media, etc. |
| Approach: | They propose an LLM-based approach for learning multimodal contrast following the encoding-decoding paradigm . they use a memory module with reinforced contrast recognition to enhance learning . |
| Outcome: | The proposed approach outperforms baseline and state-of-the-art studies on four English and Chinese benchmark datasets. |
PeTra: A Sparsely Supervised Memory Model for People Tracking (2020.acl-main)
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| Challenge: | Existing methods for coreference resolution scale quadratically with length of text, and are cognitively implausible. |
| Approach: | They propose a memory-augmented neural network called PeTra that tracks entities in its memory slots. |
| Outcome: | The proposed model outperforms a previous memory model while maintaining strong performance. |
Memory-Augmented LLM-based Multi-Agent System for Automated Feature Generation on Tabular Data (2026.acl-long)
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| Challenge: | Existing methods for automated feature generation rely on predefined operator libraries and do not incorporate feature semantics, limiting their ability to produce high-quality features. |
| Approach: | They propose a Memory-Augmented LLM-based Multi-Agent System (MALMAS) that decomposes the generation process into agents with distinct responsibilities. |
| Outcome: | The proposed method extracts informative features from raw tabular data without manual intervention and is crucial for accurate, generalizable machine learning. |
Attractive Storyteller: Stylized Visual Storytelling with Unpaired Text (2023.acl-long)
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| Challenge: | Xu et al., 2015; Guo e t al, 2022a) focus on generating objective and neutral descriptions of image content without considering style characteristics. |
| Approach: | They propose a task of Stylized Visual Storytelling to generate attractive stylized stories for a photo stream. |
| Outcome: | The proposed framework can generate attractive stories with different styles . it surpasses state-of-the-art methods on automatic and human evaluation metrics. |
Unveiling Privacy Risks in LLM Agent Memory (2025.acl-long)
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| Challenge: | Large Language Model (LLM) agents store private user-agent interactions in memory for demonstrations, introducing new privacy risks for LLM agents. |
| Approach: | They propose an attack that extracts private information from memory under a black-box setting and propose a method that can be used to attack the agent. |
| Outcome: | The proposed attack is effective under a black-box setting and it is demonstrated on two representative agents. |
Memory-augmented Query Reconstruction for LLM-based Knowledge Graph Reasoning (2025.findings-acl)
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| Challenge: | Existing methods that confuse tool utilization with knowledge reasoning harm readability and give rise to tool invocation hallucinations. |
| Approach: | They propose to decouple LLM from tool invocation tasks by establishing a memory module with explicit descriptions of query statements and a query memory module to facilitate the KGQA process. |
| Outcome: | The proposed method achieves state-of-the-art on WebQSP and CWQ benchmarks. |
FairGen: Controlling Sensitive Attributes for Fair Generations in Diffusion Models via Adaptive Latent Guidance (2025.emnlp-main)
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Mintong Kang, Vinayshekhar Bannihatti Kumar, Shamik Roy, Abhishek Kumar, Sopan Khosla, Balakrishnan Murali Narayanaswamy, Rashmi Gangadharaiah
| Challenge: | Text-to-image diffusion models often exhibit generation biases toward specific demographic groups, raising ethical concerns and limiting their adoption. |
| Approach: | They propose an adaptive latent guidance mechanism which controls the generation distribution during inference by dynamically adjusting the diffusion process to enforce specific attributes. |
| Outcome: | The proposed model outperforms existing models on HBE and Stable Bias datasets and achieves substantial bias reduction. |