Papers by Jun Feng
Static Models, Dynamic World: A Unified Perspective on Temporal Perception in Large Language Models (2026.findings-acl)
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Chenhao Li, Dandan Song, Changzhi Zhou, Jun Yang, Yuhang Tian, Huipeng Ma, Guangyuan Feng, Luan Zhang, Xudong Li, Ke Duan
| Challenge: | Large language models are trained on static corpora but deployed in a dynamic world . a foundational tension remains between time and the ability to understand it . |
| Approach: | They formalize temporal queries in an information-theoretic framework based on parametric reachability of temporal premises and answers. |
| Outcome: | The proposed framework formalizes temporal queries in an information-theoretic framework based on parametric reachability of temporal premises and answers . the framework induces four temporal information regimes corresponding to internal reasoning, answer recency, premise anchoring, and genuine world indeterminacy . |
CateEA: Enhancing Entity Alignment via Implicit Category Supervision (2025.coling-main)
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| Challenge: | Existing Entity Alignment methods neglect the inherent semantic information of entities, limiting alignment precision and robustness. |
| Approach: | They propose to combine implicit category information into multi-modal representations by generating pseudo-category labels from entity embeddings and integrating them into a multi-task learning framework. |
| Outcome: | Experiments on benchmark datasets show that CateEA outperforms state-of-the-art methods in various settings. |
SwiftPrune: Hessian-Free Weight Pruning for Large Language Models (2025.findings-emnlp)
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| Challenge: | a novel post-training pruning method relies on the Hessian matrix to perform pruning . current pruning methods are computationally intensive and lack performance due to second-order derivative calculations. |
| Approach: | They propose a Hessian-free weight pruning method that reduces computational burden . they use an Exponentially Weighted Moving Average technique to bypass weight sorting . |
| Outcome: | The proposed method achieves hardware-efficient model compression by eliminating computational intensive calculations. |
Zero-shot Text Classification via Reinforced Self-training (2020.acl-main)
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Zhiquan Ye, Yuxia Geng, Jiaoyan Chen, Jingmin Chen, Xiaoxiao Xu, SuHang Zheng, Feng Wang, Jun Zhang, Huajun Chen
| Challenge: | Existing methods to learn from unlabeled data are difficult for zero-shot text classification tasks. |
| Approach: | They propose a self-training based method to efficiently leverage unlabeled data. |
| Outcome: | The proposed method significantly outperforms existing methods in zero-shot text classification tasks on benchmarks and a real-world e-commerce dataset. |
Adversary-Aware DPO: Enhancing Safety Alignment in Vision Language Models via Adversarial Training (2025.findings-emnlp)
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| Challenge: | Adversary-aware DPO (ADPO) is a training framework that explicitly considers adversary. |
| Approach: | a new framework integrates adversarial training into a pre-trained large language model to enhance safety alignment . adversary-aware DPO provides a framework that explicitly considers adversary . |
| Outcome: | a new training framework outperforms baselines in safety alignment and general utility of large language models. |
TEaR: Improving LLM-based Machine Translation with Systematic Self-Refinement (2025.findings-naacl)
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Zhaopeng Feng, Yan Zhang, Hao Li, Bei Wu, Jiayu Liao, Wenqiang Liu, Jun Lang, Yang Feng, Jian Wu, Zuozhu Liu
| Challenge: | Large Language Models (LLMs) have achieved impressive results in Machine Translation (MT). human evaluations reveal that LLM-generated translations still contain various errors. |
| Approach: | They propose a LLM-based self-refinement framework that feeds error information back into LLMs to facilitate self-finement, leading to enhanced translation quality. |
| Outcome: | The proposed framework outperforms internal refinement and feedback methods while ensuring a robust translation quality baseline. |
D-Artemis: A Deliberative Cognitive Framework for Mobile GUI Multi-Agents (2026.findings-acl)
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Hongze Mi, Yibo Feng, WenJie Lu, Yuqi Wang, Jinyuan Li, Song Cao, He Cui, Tengfei Tian, Xuelin Zhang, Haotian Luo, Di Sun, Jun Fang, Hua Chai, Naiqiang Tan, Gang Pan
| Challenge: | Graphical User Interface (GUI) agents aim to automate a wide spectrum of human tasks by emulating user interaction. |
| Approach: | They propose a deliberative framework that leverages a fine-grained tip retrieval mechanism to inform its decision-making process. |
| Outcome: | The proposed framework achieves SOTA among open-source general models on AndroidWorld and ScreenSpot-V2 . it leverages a fine-grained, app-specific tip retrieval mechanism to inform its decision-making process . |
ToolDreamer: Instilling LLM Reasoning Into Tool Retrievers (2026.eacl-long)
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| Challenge: | Existing retrieval models rank tools based on similarity between query and tool description (TD) Existing tools are not conditioned to learn tool-to-tool relationships (middle). |
| Approach: | They propose a framework that conditions retrieval models to fetch tools based on hypothetical (synthetic) TD generated using an LLM. |
| Outcome: | The proposed framework improves the performance of sparse and dense retrievers with and without training, showcasing its flexibility. |
Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering (2020.emnlp-main)
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| Challenge: | Existing work on augmenting question answering models with external knowledge (e.g., knowledge graphs) lacks transparency into the model’s prediction rationale. |
| Approach: | They propose a knowledge-aware approach that equips pre-trained language models with a multi-hop relational reasoning module that performs multi-relational reasoning over subgraphs extracted from external knowledge graphs. |
| Outcome: | The proposed model performs multi-hop, multi-relational reasoning over subgraphs extracted from external knowledge graphs. |
DelucionQA: Detecting Hallucinations in Domain-specific Question Answering (2023.findings-emnlp)
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Mobashir Sadat, Zhengyu Zhou, Lukas Lange, Jun Araki, Arsalan Gundroo, Bingqing Wang, Rakesh Menon, Md Parvez, Zhe Feng
| Challenge: | Hallucination is a well-known phenomenon in text generated by large language models . state-of-the-art LLMs still have a number of weaknesses, including the tendency to generate hallucinatory statements without considering the factuality . |
| Approach: | They propose a dataset that captures hallucinations made by retrieval-augmented LLMs . they propose to use these methods to help detect hallucinosity in QA tasks . |
| Outcome: | The proposed method captures hallucinations made by retrieval-augmented LLMs for QA tasks. |
Towards Interactivity and Interpretability: A Rationale-based Legal Judgment Prediction Framework (2022.emnlp-main)
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| Challenge: | Existing models lack interpretability due to the neglect of rationale in the prediction process. |
| Approach: | They propose a rationale-based legal judgment prediction framework that follows the judge's real trial logic and provides good interactivity and interpretability. |
| Outcome: | The proposed framework provides good interactivity and interpretability which enables practical use. |
Transfer Learning for Context-Aware Question Matching in Information-seeking Conversations in E-commerce (P18-2)
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| Challenge: | Recent researches focus on deep learning and reinforcement learning for multi-turn information seeking conversation systems. |
| Approach: | They propose an efficient and effective multi-turn conversation model based on convolutional neural networks and extend it to adapt the knowledge learned from a resource-rich domain to enhance the performance. |
| Outcome: | The proposed model performs better than the existing model on an industrial chatbot called AliMe Assist. |
Fine-Mem: Fine-Grained Feedback Alignment for Long-Horizon Memory Management (2026.acl-long)
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Weitao Ma, Xiaocheng Feng, Lei Huang, Xiachong Feng, Zhanyu Ma, Jun Xu, Jiuchong Gao, Jinghua Hao, Renqing He, Bing Qin
| Challenge: | Existing approaches to memory management rely on final task performance as the primary reward, resulting in severe reward sparsity and ineffective credit assignment. |
| Approach: | They propose a framework for fine-grained feedback alignment using a Chunk-level step reward and Evidence-Anchored Reward Attribution to redistribute global rewards based on memory items utilized as evidence in reasoning. |
| Outcome: | The proposed framework outperforms baselines and supports generalization across different model configurations and backbones. |
FrontCoder: Scaling Visual Fidelity in Front-End Code Generation (2026.findings-acl)
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Jun Feng, Jian Yang, Wei Zhang, Jing Wang, Keyi Chen, Xiaokun Yang, Weicheng Gu, Yihang Lou, Yan Bai, Xianglong Liu
| Challenge: | Existing work on front-end code generation fails to provide visual fidelity and rendering quality for front- end developers. |
| Approach: | They propose a three-stage pipeline to enhance front-end code generation capabilities in LLMs . they use synthetic data, quality-controlled supervised fine-tuning, and reinforcement learning . |
| Outcome: | The proposed model achieves competitive performance with frontier models while maintaining generation efficiency. |
Efficient Paths and Dense Rewards: Probabilistic Flow Reasoning for Large Language Models (2026.acl-long)
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Yan Liu, Feng Zhang, Zhanyu Ma, Jun Xu, Jiuchong Gao, Jinghua Hao, Renqing He, Han Liu, Yangdong Deng
| Challenge: | Existing approaches to mitigate inference inefficiency and optimization difficulty are fragmented and constrained by inherent trade-offs. |
| Approach: | They propose a framework that reconceptualizes discrete reasoning steps as a continuous probabilistic flow, quantifying the contribution of each step toward the ground-truth answer. |
| Outcome: | The proposed framework achieves a superior balance between inference efficiency and reasoning performance on challenging benchmarks. |
Improving Autoformalization Using Direct Dependency Retrieval (2026.acl-long)
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| Challenge: | Existing methods for hallucinate formal dependencies lack scalability and precision to leverage ever-growing public datasets. |
| Approach: | They propose a retrieval-augmented framework based on Direct Dependency Retrieval to generate formal dependencies from natural-language mathematical descriptions and verify their existence via an efficient Suffix Array Check (SAC). |
| Outcome: | The proposed framework outperforms state-of-the-art methods in retrieval precision and recall and can be used to validate formal representations in a public dataset. |
Machine Reading Comprehension Using Structural Knowledge Graph-aware Network (D19-1)
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| Challenge: | Recent large-scale datasets specify that external knowledge is required to answer questions. |
| Approach: | They propose a model that leverages external knowledge to construct sub-graphs for entities in machine comprehension context. |
| Outcome: | The proposed model achieves state-of-the-art performance on the ReCoRD dataset. |
Focus-aware Response Generation in Inquiry Conversation (2023.findings-acl)
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| Challenge: | Existing studies on response generation focus on relevance and fluency, rarely paying attention to the focus. |
| Approach: | They propose a Focus-aware response generation method that takes the focus into consideration and optimizes a multi-level encoder and focal decoder to generate multiple candidate responses. |
| Outcome: | The proposed method generates candidate responses that correspond to different focuses and performs better on two orthogonal inquiry conversation datasets. |
CoAug: Combining Augmentation of Labels and Labelling Rules (2023.findings-acl)
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| Challenge: | Named Entity Recognition (NER) tasks require large labeled datasets to perform well. |
| Approach: | They propose a co-augmentation framework that bootstraps predictions from each model to improve few-shot models and rule-augmentation models by bootstrapping them. |
| Outcome: | The proposed model outperforms strong weak-supervision-based models by 6.5 F1 points . the proposed model can learn from limited labeled data and perform better on small datasets . |
My Words Imply Your Opinion: Reader Agent-Based Propagation Enhancement for Personalized Implicit Emotion Analysis (2025.acl-long)
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| Challenge: | Current studies personalize emotion analysis by focusing on the author but neglect the impact of the intended reader on implicit emotional feedback. |
| Approach: | They propose a model which incorporates reader feedback into implicit emotion analysis (IEA) they use large language models to create reader agents to simulate reader feedback . |
| Outcome: | The proposed model outperforms state-of-the-art models in a text-centric environment. |
Improving Entity Linking through Semantic Reinforced Entity Embeddings (2020.acl-main)
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| Challenge: | Existing entity embeddings are effective, but too distinctive for linking models to learn contextual commonality. |
| Approach: | They propose a method to inject fine-grained semantic information into entity embeddings . they use word embedds of type words to generate semantic embeddngs based on existing embeddables a sample of semantic information is injected into the embedded entities . |
| Outcome: | The proposed method reduces the distinctiveness of existing embeddings and improves performance. |
HumanLLM: Benchmarking and Improving LLM Anthropomorphism via Human Cognitive Patterns (2026.acl-long)
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Xintao Wang, Jian Yang, Weiyuan Li, Rui Xie, Jen-tse Huang, Jun Gao, Shuai Huang, Yueping Kang, Yuanli Guo, Hongwei Feng, Yanghua Xiao
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable capabilities in reasoning and generation, serving as the foundation for advanced persona simulation and Role-Playing Language Agents (RPLAs). |
| Approach: | They propose a framework that treats psychological patterns as interacting causal forces and synthesizes 113 scenarios where 2-5 patterns reinforce, conflict, or modulate each other. |
| Outcome: | The proposed framework outperforms Qwen3-32B on multi-pattern dynamics despite 4 fewer parameters. |
MathFlow: Enhancing the Perceptual Flow of MLLMs for Visual Mathematical Problems (2026.acl-long)
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| Challenge: | Existing multimodal large language models (MLLMs) exhibit significant limitations when extracting essential information and reasoned properties from diagrams and performing complex reasoning based on these visual inputs. |
| Approach: | They propose a benchmark that provides a fine-grained evaluation of MLLMs’ perception and reasoning capabilities. |
| Outcome: | The proposed benchmark shows that existing MLLMs exhibit limitations when extracting essential information and reasoned properties from diagrams and performing complex reasoning based on these visual inputs. |