Papers by Kai Han
A Unified Temporal Knowledge Graph Reasoning Model Towards Interpolation and Extrapolation (2024.acl-long)
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| Challenge: | Existing methods for temporal knowledge graphs de-emphasize temporal correlations between facts sequences and ignore inferring clues from missing facts. |
| Approach: | They propose a Temporal PAth-based reasoning model that is robust to ambiguous temporal data. |
| Outcome: | The proposed model outperforms SOTA methods on the link prediction task. |
Empowering GraphRAG with Knowledge Filtering and Integration (2025.emnlp-main)
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| Challenge: | Large language models suffer from knowledge gaps and hallucinations, resulting in incorrect or poor reasoning. |
| Approach: | They propose Graph retrieval-augmented generation (GraphRAG) which integrates structured knowledge from external graphs to enhance model's reasoning. |
| Outcome: | Experiments on knowledge graph QA tasks show that GraphRAG significantly improves reasoning performance across multiple backbone models. |
ARM2: Adaptive Reasoning Model with Vision Understanding and Executable Code (2026.findings-acl)
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| Challenge: | Large Reasoning Models suffer from the "over-thinking" problem, causing performance degradation. |
| Approach: | They propose a unified model that balances reasoning performance and efficiency across multiple formats through a reinforcement learning framework augmented with length-aware optimization. |
| Outcome: | The proposed model reduces token costs while preserving performance compared to traditional models. |
Corrupted but Not Broken: Understanding and Mitigating the Negative Impacts of Corrupted Data in Visual Instruction Tuning (2025.emnlp-main)
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Yunhao Gou, Hansi Yang, Zhili Liu, Kai Chen, Yihan Zeng, Lanqing Hong, Zhenguo Li, Qun Liu, Bo Han, James Kwok, Yu Zhang
| Challenge: | Visual Instruction Tuning (VIT) aims to enhance Multimodal Large Language Models (MLLMs), but its effectiveness is often compromised by corrupted datasets with issues such as hallucinated content and poor OCR quality. |
| Approach: | They propose a corruption-robust training paradigm that surpasses existing strategies for mitigating the effects of corrupted data. |
| Outcome: | The proposed training paradigm surpasses existing strategies for mitigating the effects of corrupted data. |
BERT-QE: Contextualized Query Expansion for Document Re-ranking (2020.findings-emnlp)
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| Challenge: | Existing methods to expand query use pseudo relevance feedback (PRF) but they are under-equipped to evaluate the relevance of information pieces used for expansion. |
| Approach: | They propose a query expansion model that leverages the BERT model to select relevant document chunks for expansion. |
| Outcome: | The proposed model significantly outperforms existing models on the TREC Robust04 and GOV2 test collections. |
Open Hierarchical Relation Extraction (2021.naacl-main)
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| Challenge: | Existing OpenRE methods cast different relation types in isolation without considering their hierarchical dependency. |
| Approach: | They propose a framework to establish bidirectional connections between OpenRE and relation hierarchies by integrating hierarchy information into relation representations. |
| Outcome: | The proposed framework outperforms state-of-the-art models on relation clustering and hierarchy expansion. |
GAMEBoT: Transparent Assessment of LLM Reasoning in Games (2025.acl-long)
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| Challenge: | Existing efforts to create benchmarks that move beyond superficial pattern recognition to delve into the profound reasoning skills required for problemsolving face challenges such as insufficient interpretability, performance saturation or data contamination. |
| Approach: | They propose a gaming arena designed for rigorous assessment of LLM reasoning capabilities. |
| Outcome: | The proposed framework decomposes complex reasoning into predefined modular subproblems and generates ground truth for these subproblem types. |
Ascending the Infinite Ladder: Benchmarking Spatial Deformation Reasoning in Vision-Language Models (2026.acl-long)
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Jiahuan Zhang, Shunwen Bai, Tianheng Wang, KaiWen Guo, Zijia Song, Hanqing WU, Guozheng Rao, Kai Han, Kaicheng Yu
| Challenge: | Existing benchmarks explore aspects of threedimensional spatial reasoning and visual-language reasoning in dynamic environments, but they are unable to perform well on 3D spatial deformation reasoning. |
| Approach: | They propose to use a ladder competition format to assess the model's spatial deformation reasoning abilities to determine its performance. |
| Outcome: | The proposed framework assesses the performance of Vision-Language Models in spatial deformation reasoning tasks. |
LawBench: Benchmarking Legal Knowledge of Large Language Models (2024.emnlp-main)
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Zhiwei Fei, Xiaoyu Shen, Dawei Zhu, Fengzhe Zhou, Zhuo Han, Alan Huang, Songyang Zhang, Kai Chen, Zhixin Yin, Zongwen Shen, Jidong Ge, Vincent Ng
| Challenge: | LegalBench evaluated 20 LLMs in 162 legal tasks in 20 countries and jurisdictions. |
| Approach: | They present a comprehensive evaluation of 21 popular Large Language Models and the first comparative analysis of the empirical results. |
| Outcome: | The proposed benchmarks are based on the Bloom’s cognitive taxonomy and are compared to 21 popular LLMs. |
From Generalist to Specialist: A Survey of Large Language Models for Chemistry (2025.coling-main)
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| Challenge: | Existing studies on pretraining of LLMs on extensive web-based texts are insufficient for advanced scientific discovery, especially in chemistry. |
| Approach: | They outline methodologies for incorporating domain-specific chemistry knowledge and multi-modal information into LLMs and conceptualize chemistry LLM agents using chemistry tools. |
| Outcome: | The proposed models are based on domain-specific chemistry knowledge and multi-modal information and are capable of accelerating scientific research. |
MM-Doc-R1: Training Agents for Long Document Visual Question Answering through Multi-turn Reinforcement Learning (2026.findings-acl)
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Jiahang Lin, Kai Hu, Binghai Wang, Yuhao Zhou, Zhiheng Xi, Honglin Guo, Shichun Liu, Junzhe Wang, Shihan Dou, Enyu Zhou, Hang Yan, Zhenhua Han, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing work on long document visual question answering is based on Retrieval-Augmented Generation (RAG) where textual or visual content is encoded into embeddings and relevance is determined by similarity scores with respect to the original query. |
| Approach: | They propose a framework that employs an agentic, vision-aware workflow to address long document visual question answering through iterative information discovery and synthesis. |
| Outcome: | The proposed framework outperforms existing RL systems by 10.4% on the MMLongbench-Doc benchmark and demonstrates superior training performance over GRPO. |
SoftDedup: an Efficient Data Reweighting Method for Speeding Up Language Model Pre-training (2024.acl-long)
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| Challenge: | Current methods focus on detecting and removing duplicates, which risks the loss of valuable information and neglects the varying degrees of duplication. |
| Approach: | They propose a method that maintains dataset integrity while selectively reducing the sampling weight of data with high commonness. |
| Outcome: | The proposed method significantly improves training efficiency on deduplicated datasets and improves downstream accuracy by 1.77%. |
CodeBind: Decoupled Representation Learning for Multimodal Alignment with Unified Compositional Codebook (2026.findings-acl)
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| Challenge: | Multimodal representation alignment is crucial for large language models and robotics. |
| Approach: | They propose a framework that optimizes multimodal representation spaces through a modality-shared-specific codebook design. |
| Outcome: | The proposed framework achieves state-of-the-art performance in multimodal classification and retrieval tasks. |
DenseSSM: State Space Models with Dense Hidden Connection for Efficient Large Language Models (2025.naacl-long)
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| Challenge: | Large language models (LLMs) face excessive computational and memory requirements due to the commonly used Transformer architecture. |
| Approach: | They propose a method to enhance the flow of hidden information between layers in large language models by selectively integrating shallow-layer hidden states into deeper layers. |
| Outcome: | The proposed method maintains parallelizability and inference efficiency of SSMs while significantly boosting performance on public benchmarks. |
EMS-SD: Efficient Multi-sample Speculative Decoding for Accelerating Large Language Models (2025.naacl-long)
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| Challenge: | Speculative decoding is a key technique for enhancing the inference speed of Large Language Models. |
| Approach: | They propose a method that adds padding tokens to ensure that the number of new tokens remains consistent across samples. |
| Outcome: | The proposed method can handle the issue of inconsistent prediction tokens without adding padding tokens. |
Converging to a Lingua Franca: Evolution of Linguistic Regions and Semantics Alignment in Multilingual Large Language Models (2025.coling-main)
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| Challenge: | Recent studies suggest that large language models can transfer skills learned in one language to others, but internal mechanisms behind this ability remain unclear. |
| Approach: | They find that LLMs map semantically identical inputs from different languages into a common semantic latent space that allows for consistent processing across languages. |
| Outcome: | The findings highlight the structural evolution of multilingual models during training and scaling up. |
IBSEN: Director-Actor Agent Collaboration for Controllable and Interactive Drama Script Generation (2024.acl-long)
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| Challenge: | Language models have demonstrated their capabilities in storyline creation and human-like character role-playing. |
| Approach: | They propose a director-actor coordinate agent framework that generates drama scripts . framework allows actors to role-play their characters while maintaining plot development . |
| Outcome: | The proposed framework generates drama scripts from a drama plot outline and human actors can play their characters. |
Experience-Driven Multi-Agent Optimization for Black-Box Jailbreak Attacks on Large Language Models (2026.findings-acl)
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| Challenge: | Existing methods for jailbreak have poor transferability and high sensitivity to preprocessing . EMJO provides an effective and scalable paradigm for systematic jailbreak optimization . |
| Approach: | They propose a model that couples agents into a closed-loop "probe–evaluate–revise” process . they propose EMJO, which can be query-efficient and transferable, under black-box access. |
| Outcome: | a new approach outperforms existing jailbreak baselines on diverse LLMs . it achieves up to 11% improvement in attack success rate while reducing query cost . |
PruneVid: Visual Token Pruning for Efficient Video Large Language Models (2025.findings-acl)
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| Challenge: | Existing approaches to video token pruning face significant computational challenges due to the redundancy inherent in video data. |
| Approach: | They propose a training-free visual token pruning method that reduces the redundancy inherent in video data and leverages LLMs’ inherent ability to selectively prune visual tokens irrelevant to specific queries. |
| Outcome: | The proposed method can prune over 80% of tokens while maintaining competitive performance when combined with different video LLMs. |
AlignSum: Data Pyramid Hierarchical Fine-tuning for Aligning with Human Summarization Preference (2024.findings-emnlp)
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| Challenge: | Text summarization tasks employ Pre-trained Language Models (PLMs) to fit diverse datasets. |
| Approach: | They propose a human summarization preference alignment framework to align PLMs with human preferences. |
| Outcome: | The proposed framework narrows the gap between automatic and human evaluations by integrating three components. |
Mitigating Coordinate Prediction Bias from Positional Encoding Failures (2026.findings-acl)
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| Challenge: | Multimodal Large Language Models (MLLMs) excel at general vision-language tasks, but precise coordinate prediction remains a challenge. |
| Approach: | They propose a training-free, inference-time correction method to correct VPEs . they isolate position-unconditioned tendencies by shuffling VPE and use it to steer digit decoding . |
| Outcome: | The proposed method is training-free, inference-time correction method . it effectively rectifies coordinate drift, yielding consistent improvements without retraining . |
MATCH: Modulating Attention via In-Context Retrieval for Long-Context Transformers (2026.acl-long)
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Linrui Ma, Chun Hei Lo, Xinyu Wang, Peng Lu, Xihao Yuan, Hanting Chen, Kai Han, Xinghao Chen, Chengjun Zhan, Hanlin xu, Yichun Yin, Lifeng Shang, Feng Wen, Boxing Chen, Yufei Cui
| Challenge: | Existing approaches to improve efficiency often enforce rigid structural constraints such as local attention windows. |
| Approach: | They propose a framework that augments sparse-attention mechanisms with dynamically integrated in-context information through an efficient retrieval system. |
| Outcome: | Empirical results show that MATCH significantly improves the performance of sparse-attention models on synthetic and real-world natural-language tasks. |