Papers by Jiayi Xin
OCR-Memory: Optical Context Retrieval for Long-Horizon Agent Memory (2026.acl-long)
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| Challenge: | Existing LLMs are limited by text-context budgets, resulting in token-expensive storage of raw trajectories . Optical Context Retrieval Memory (OCR-Memory) renders historical tra-jectorios into images annotated with unique visual identifiers. |
| Approach: | They propose a framework that leverages the visual modality as a high-density representation of agent experience. |
| Outcome: | Optical Context Retrieval Memory (OCRM) renders historical trajectories into images annotated with unique visual identifiers. |
RAG-Star: Enhancing Deliberative Reasoning with Retrieval Augmented Verification and Refinement (2025.naacl-long)
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| Challenge: | Existing large language models (LLMs) show exceptional problem-solving capabilities but struggle with complex reasoning tasks. |
| Approach: | They propose a novel RAG approach that integrates retrieved information to guide tree-based reasoning process based on LLMs. |
| Outcome: | The proposed approach outperforms existing methods in large language models . iteratively plans intermediate sub-queries and answers based on the LLM itself . |
UCS: Estimating Unseen Coverage for Improved In-Context Learning (2026.findings-acl)
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| Challenge: | Existing selection methods prioritize heuristic notions of relevance or diversity and provide limited insight into the coverage of a demonstration set. |
| Approach: | They propose a training-free, subset-level coverage prior that is unrevealed by a model-consistent embedding and a Smoothed Good-Turing estimator to estimate the number of unrevelled clusters within a candidate subset. |
| Outcome: | Experiments on multiple intent-classification and reasoning benchmarks show that augmenting strong baselines with UCS improves ICL accuracy by 2-6% under the same selection budget. |
Retrieved Sequence Augmentation for Protein Representation Learning (2024.emnlp-main)
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Chang Ma, Haiteng Zhao, Lin Zheng, Jiayi Xin, Qintong Li, Lijun Wu, Zhihong Deng, Yang Lu, Qi Liu, Sheng Wang, Lingpeng Kong
| Challenge: | Using multiple sequence alignments (MSA) to extract evolutionary knowledge is limited. |
| Approach: | They propose to use multiple sequence alignments to augment protein representations . they propose to employ Retrieved Sequence Augmentation to enhance protein representation learning . |
| Outcome: | The proposed method surpasses MSA Transformer by 5% in structural and property prediction tasks while being 373 times faster. |
The Impact of Language Mixing on Bilingual LLM Reasoning (2025.emnlp-main)
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| Challenge: | Recent studies show multilingual speakers intentionally switch languages during reasoning . enforcing monolingual decoding reduces accuracy by 5.6 percentage points . |
| Approach: | They find that multilingual speakers intentionally switch languages during reasoning . enforcing monolingual decoding reduces accuracy by 5.6 percentage points . authors suggest that language mixing is not merely a byproduct of multilingual training . |
| Outcome: | The proposed model can be used to predict whether a language switch would benefit or harm reasoning. |
Lost in the Context: Insufficient and Distracted Attention to Contexts in Preference Modeling (2025.acl-long)
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Shihan Dou, Jiayi Chen, Chenhao Huang, Feng Chen, Wei Chengzhi, Huiyuan Zheng, Shichun Liu, Yan Liu, Chenxiao Liu, Chao Xin, Lin Yan, Zongzhang Zhang, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing reward models concatenate contexts and responses, but they often ignore crucial segments of the context that are important for evaluating the response quality. |
| Approach: | They propose a reward model that evaluates the response quality based on a given context and assigns a rewards reward. |
| Outcome: | The proposed framework significantly improves preference modeling by increasing attention to relevant information within the context and achieves better generalizability. |
Easy Guided Decoding in Providing Suggestions for Interactive Machine Translation (2023.acl-long)
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| Challenge: | In order to improve translation efficiency, human translators perform post-editing on machine translations to correct errors. |
| Approach: | They propose to use the parameterized objective function of neural machine translation to deal with the TS problem without additional training. |
| Outcome: | The proposed method improves translation quality by 10.6 BLEU and reduces time overhead by 63.4% on benchmark datasets. |