Papers by You Li
A-TIP: Attribute-aware Text Infilling via Pre-trained Language Model (2022.coling-1)
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| Challenge: | Existing methods for text infilling focus on the infill length of blanks and attribute relevance, but attribute-aware content can be more useful. |
| Approach: | They propose an attribute-aware text infilling method via a Pre-trained language model which contains a text in filling component and a plug-and-play discriminator. |
| Outcome: | The proposed method improves attribute relevance without decreasing text fluency on three open-source datasets. |
DentalGPT: Incentivizing Multimodal Reasoning in Dentistry (2026.findings-acl)
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Zhenyang Cai, Jiaming Zhang, Junjie Zhao, Ziyi Zeng, Yanchao Li, Liang Jingyi, Junying Chen, Yunjin Yang, Jiajun You, Shuzhi Deng, null Xieruiqiii, Yuanting Chen, Xiangyi Feng, Jianquan Li, Liangyi Chen, Junwen Wang, Shan Jiang, Benyou Wang
| Challenge: | Current multimodal large language models (MLLMs) show limited understanding of dental images. |
| Approach: | They propose a dental-specialized multimodal large language model trained via staged multimodal alignment and reinforcement learning. |
| Outcome: | The proposed model outperforms state-of-the-art models on disease classification and dental VQA tasks. |
GroupToM-Bench: Benchmarking Group Theory of Mind and Nonlinear Social Emergence in MLLMs (2026.acl-long)
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Weidong Tang, Jierui Li, Yueling Hou, Zihan Mei, Can Zhang, Xinyan Wan, Zhiyuan Liang, Pengfei Zhou, Yang You, Wangbo Zhao
| Challenge: | Existing models for general intelligence fail to model how mental states interact and crystallize into group-level outcomes. |
| Approach: | They propose a multimodal benchmark for group-level Theory of Mind (ToM) to probe nonlinear collective behavior. |
| Outcome: | The proposed model performs significantly below human levels, exposing blind spots in modeling social structures and nonlinear collective behavior. |
Revealing and Mitigating the Local Pattern Shortcuts of Mamba (2025.findings-acl)
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| Challenge: | Recent studies show that Mamba excels in tasks that involve localized key information but faces challenges with tasks that require handling distributed key information. |
| Approach: | They propose to introduce a global gate module into Mamba to address this problem by adding 4M extra parameters to the model. |
| Outcome: | The proposed model outperforms attention-based models on synthetic and synthetic tasks with only 4M extra parameters. |
Knowledge-Guided Dynamic Modality Attention Fusion Framework for Multimodal Sentiment Analysis (2024.findings-emnlp)
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| Challenge: | Existing methods focus on equally treating the contribution of each modality or statically using text as the dominant modality to conduct interaction, which neglects the situation where each modal may become dominant. |
| Approach: | They propose a Knowledge-Guided Dynamic Modality Attention Fusion Framework (KuDA) that uses sentiment knowledge to guide the model dynamically selecting the dominant modality and adjusting the contributions of each modality. |
| Outcome: | The proposed model can be used to highlight the contribution of dominant modality through the correlation evaluation loss. |
Sequence Parallelism: Long Sequence Training from System Perspective (2023.acl-long)
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| Challenge: | Existing work on memory-efficient parallelisms to reduce time and space complexity focuses on reducing time and complexity from system perspective. |
| Approach: | They propose a memory-efficient parallelism to reduce time and space complexity . they split input sequence into multiple chunks and feed each chunk into GPU . |
| Outcome: | The proposed approach is compatible with most existing parallelisms and makes 4D parallelismal possible. |
Superpose Task-specific Features for Model Merging (2025.emnlp-main)
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| Challenge: | Existing methods for model merging are limited by resource demands . recent studies validate the linear representation hypothesis . |
| Approach: | They propose a method that superposes task-specific features from individual models into a merged model. |
| Outcome: | The proposed method outperforms existing methods on multiple benchmarks and models. |
Are AI-Generated Text Detectors Robust to Adversarial Perturbations? (2024.acl-long)
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| Challenge: | Existing detectors for AI-generated text lack robustness against adversarial perturbations, with even minor changes in characters or words causing a reversal in distinguishing between human-created and AI-generated text. |
| Approach: | They propose a siamese calibration technique to train the model to make equally confident predictions under different noise, which improves the model’s robustness against adversarial perturbations. |
| Outcome: | The proposed detector outperforms baseline methods on four datasets and is more generalizable in cross-domain, cross-genre, and mixed-source scenarios. |
Diagnosing Memorization in Chain-of-Thought Reasoning, One Token at a Time (2025.emnlp-main)
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| Challenge: | Large Language Models (LLMs) perform well on reasoning benchmarks but often fail when inputs alter slightly, raising concerns about overreliance on memorization. |
| Approach: | They propose a framework for Source-aware Token-level Identification of Memorization which attributes each token in a reasoning chain to one of multiple memorization sources based on their statistical co-occurrence with the token in the pretraining corpus. |
| Outcome: | The proposed framework attributes each token in a reasoning chain to one of multiple memorization sources based on their statistical co-occurrence with the token in the pretraining corpus. |
Exploring Reversal Mathematical Reasoning Ability for Large Language Models (2024.findings-acl)
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| Challenge: | Large language models (LLMs) have been a success in the wide range of natural language understanding and reasoning tasks. |
| Approach: | They propose a training method to improve general and reversal reasoning abilities by using a reversed dataset. |
| Outcome: | The proposed method improves general and reversal reasoning abilities and alleviates the reverse curse. |
TinyScientist: An Interactive, Extensible, and Controllable Framework for Building Research Agents (2025.emnlp-demos)
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Haofei Yu, Keyang Xuan, Fenghai Li, Kunlun Zhu, Zijie Lei, Jiaxun Zhang, Ziheng Qi, Kyle Richardson, Jiaxuan You
| Challenge: | Existing research systems often design and use agentic workflows to perform research tasks such as ideation, scientific coding, review writing, and tree-based search. |
| Approach: | They propose an open-source codebase, an interactive web demonstration, and a PyPI Python package to make state-of-the-art auto-research pipelines broadly accessible to every researcher and developer. |
| Outcome: | The proposed framework adapts easily to new tools and supports iterative growth. |
IEvoAgent: Evolving Conversational Agent based on User Implicit Feedback (2026.acl-long)
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| Challenge: | Existing approaches to optimize conversational agents often rely on explicit preference pairs and expert evaluations. |
| Approach: | They propose a conversational agent framework that leverages the structured dependency between agent responses and user reactions to extract implicit feedback. |
| Outcome: | The proposed framework improves on MT-Bench-101, WildBench, and FB-Bech, and shows that mining implicit feedback supports better multi-turn alignment under evolving user preferences. |
Efficient Domain Adaptation for Non-Autoregressive Machine Translation (2024.findings-acl)
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| Challenge: | Existing non-parametric approaches like nearest neighbor machine translation have made small Autoregressive translation models less efficient . despite their impressive generalization and task performance, LLMs suffer from prohibitive inference cost when confronted with specific domains. |
| Approach: | They propose a domain adaptation approach that tailors a k-nearest-neighbor algorithm for NAT models that incorporates the parallel nature of NAT. |
| Outcome: | The proposed approach achieves significant improvements over the Base-NAT model and exhibits enhanced efficiency. |
RouteMoA: Dynamic Routing without Pre-Inference Boosts Efficient Mixture-of-Agents (2026.acl-long)
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Jize Wang, Han Wu, Zhiyuan You, Yiming Song, Yijun Wang, Zifei Shan, Yining Li, Songyang Zhang, Xinyi Le, Cailian Chen, Xinping Guan, Dacheng Tao
| Challenge: | Existing methods for mixing-of-agents (MoA) lack model selection criteria and struggle with large model pools. |
| Approach: | They propose a mixture-of-agents framework with dynamic routing that uses a lightweight scorer to perform initial screening and refines the model scores through self- and cross-assessment. |
| Outcome: | The proposed framework outperforms existing methods for large model pools and tasks . it reduces cost by 89.8% and latency by 63.6% in the large-scale model pool. |
Multi-Stage LLM Fine-Tuning with a Continual Learning Setting (2025.findings-naacl)
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Changhao Guan, Chao Huang, Hongliang Li, You Li, Ning Cheng, Zihe Liu, Yufeng Chen, Jinan Xu, Jian Liu
| Challenge: | Large language models (LLMs) have made significant progress in knowledge-intensive applications, but they may face a multi-stage continuous learning scenario. |
| Approach: | They propose a multi-stage continuous learning paradigm that includes a preference-based learning bias to identify potential knowledge conflicts and a self-distillation-based data augmentation strategy to expand and enrich the training corpus. |
| Outcome: | The proposed learning paradigm achieves a significant improvement in accuracy after 7 stages of fine-tuning compared to previous methods while preserving general knowledge. |
Learning Embeddings for Rare Words Leveraging Internet Search Engine and Spatial Location Relationships (2021.starsem-1)
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| Challenge: | Existing word embedding techniques depend heavily on the frequencies of words in the corpus, and fail to provide reliable representations for rare words. |
| Approach: | They propose an algorithm to learn embeddings for rare words based on an Internet search engine and the spatial location relationships. |
| Outcome: | The proposed algorithm can learn more accurate representations for a wider range of vocabulary. |
Audio MultiChallenge: A Multi-Turn Evaluation of Spoken Dialogue Systems on Natural Human Interaction (2026.acl-long)
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Advait Gosai, Tyler Vuong, Utkarsh Tyagi, Steven Li, Wenjia You, Miheer Bavare, Arda Uçar, Zhongwang Fang, Brian Jang, Bing Liu, Yunzhong He
| Challenge: | End-to-end (E2E) spoken dialogue systems are replacing cascaded pipelines for voice-based human-AI interaction. Existing benchmarks evaluate these systems on synthetic speech and single-turn tasks, leaving multi-turn conversational ability underexplored. |
| Approach: | They propose an open-source benchmark to evaluate spoken dialogue systems under natural multi-turn interaction patterns. |
| Outcome: | The proposed model fails on the highest-performing model with 54.65% pass rate. |
LitVISTA: A Benchmark for Narrative Orchestration in Literary Text (2026.acl-long)
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Mingzhe Lu, Yiwen Wang, Yanbing Liu, Qi You, Chong Liu, Ruize Qin, Haoyu Dong, Wenyu Zhang, JiaRui Zhang, Yue Hu, Yunpeng Li
| Challenge: | Existing large language models focus on causal coherence, neglecting the complex story arcs and orchestration inherent in human narratives. |
| Approach: | They propose a high-dimensional framework for narrative orchestration that unifies human and model perspectives while jointly characterizing narrative function and structure in a common space. |
| Outcome: | The proposed framework unifies human and model perspectives while jointly characterizing narrative function and structure in a common space. |
History-Aware Hierarchical Transformer for Multi-session Open-domain Dialogue System (2022.findings-emnlp)
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| Challenge: | Existing open-domain dialogue systems conduct one-session conversations, but multi-session MSCs are under-investigated. |
| Approach: | They propose a History-Aware Hierarchical Transformer for multi-session open-domain dialogue . they propose to encode history conversations into a history memory and leverage historical information to generate well-informed responses. |
| Outcome: | The proposed model outperforms baseline models on a large-scale MSC dataset. |
JPG - Jointly Learn to Align: Automated Disease Prediction and Radiology Report Generation (2022.coling-1)
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| Challenge: | Existing methods rarely consider cross-modal alignment between textual and visual features and ignore disease tags as auxiliary for report generation. |
| Approach: | They propose a "Jointly learning framework for automated disease Prediction and radiology report Generation" the framework integrates cross-modal alignment between textual and visual features and disease tags to improve the quality of reports. |
| Outcome: | The proposed framework improves the quality of radiology reports by combining the main task and auxiliary tasks. |
Multimodal Prompt Learning for Product Title Generation with Extremely Limited Labels (2023.findings-acl)
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| Challenge: | Existing approaches to generate informative titles for products with limited labels are inadequate for novel products. |
| Approach: | They propose a prompt-based approach to generate attractive titles for novel products . they use multimodal prompts to preserve characteristics and writing styles of novel products. |
| Outcome: | The proposed approach achieves state-of-the-art results on novel product categories with limited labels. |
INFORM : Information eNtropy based multi-step reasoning FOR large language Models (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) have demonstrated exceptional performance with dedicated Chain-of-Thought (CoT) prompts. |
| Approach: | They propose a new method by introducing information entropy as a criteria on for CoT prompt selection. |
| Outcome: | The proposed model outperforms existing models on seven reasoning benchmarks using two language models. |
Unsupervised Vision-and-Language Pre-training Without Parallel Images and Captions (2021.naacl-main)
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| Challenge: | Existing models require large amounts of image-caption data for pre-training . existing models require expensive data collection and curation . |
| Approach: | They propose to conduct "mask-and-predict" pre-training on text-only and image-only corpora and introduce the object tags detected by an object recognition model as anchor points to bridge two modalities. |
| Outcome: | The proposed approach achieves performance close to a model pre-trained with aligned data, on four English benchmarks. |
MMSearch-R1: Incentivizing LMMs to Search (2026.acl-long)
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| Challenge: | Existing approaches to deploying large multimodal models rely on rigid pipelines . Existing methods such as retrieval-augmented generation and prompt engineered search rely only on rigid knowledge sources. |
| Approach: | They propose a framework that enables LMMs to perform on-demand, multi-turn search in real-world Internet environments. |
| Outcome: | The proposed model outperforms existing models while reducing search calls by over 30%. |
Causal Reasoning of Entities and Events in Procedural Texts (2023.findings-eacl)
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| Challenge: | Existing work on entity state tracking or event reasoning is limited to procedural texts. |
| Approach: | They propose a benchmark for causal reasoning of event plausibility and entity states . they represent entities as programming languages while prompting language models . |
| Outcome: | The proposed model outperforms existing models on human reasoning and event reasoning. |
GEMv2: Multilingual NLG Benchmarking in a Single Line of Code (2022.emnlp-demos)
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Sebastian Gehrmann, Abhik Bhattacharjee, Abinaya Mahendiran, Alex Wang, Alexandros Papangelis, Aman Madaan, Angelina Mcmillan-major, Anna Shvets, Ashish Upadhyay, Bernd Bohnet, Bingsheng Yao, Bryan Wilie, Chandra Bhagavatula, Chaobin You, Craig Thomson, Cristina Garbacea, Dakuo Wang, Daniel Deutsch, Deyi Xiong, Di Jin, Dimitra Gkatzia, Dragomir Radev, Elizabeth Clark, Esin Durmus, Faisal Ladhak, Filip Ginter, Genta Indra Winata, Hendrik Strobelt, Hiroaki Hayashi, Jekaterina Novikova, Jenna Kanerva, Jenny Chim, Jiawei Zhou, Jordan Clive, Joshua Maynez, João Sedoc, Juraj Juraska, Kaustubh Dhole, Khyathi Raghavi Chandu, Laura Perez Beltrachini, Leonardo F . R. Ribeiro, Lewis Tunstall, Li Zhang, Mahim Pushkarna, Mathias Creutz, Michael White, Mihir Sanjay Kale, Moussa Kamal Eddine, Nico Daheim, Nishant Subramani, Ondrej Dusek, Paul Pu Liang, Pawan Sasanka Ammanamanchi, Qi Zhu, Ratish Puduppully, Reno Kriz, Rifat Shahriyar, Ronald Cardenas, Saad Mahamood, Salomey Osei, Samuel Cahyawijaya, Sanja Štajner, Sebastien Montella, Shailza Jolly, Simon Mille, Tahmid Hasan, Tianhao Shen, Tosin Adewumi, Vikas Raunak, Vipul Raheja, Vitaly Nikolaev, Vivian Tsai, Yacine Jernite, Ying Xu, Yisi Sang, Yixin Liu, Yufang Hou
| Challenge: | Evaluations in machine learning rarely use the latest metrics, datasets, or human evaluation in favor of remaining compatible with prior work. |
| Approach: | They propose to use the Generation, Evaluation, and Metrics Benchmark to integrate new evaluation methods into existing evaluations. |
| Outcome: | The proposed evaluation infrastructure bridges the gap between the advantages of leaderboards and in-depth and evolving evaluations by allowing model developers to benefit from each other's work. |
Joint Learning-based Heterogeneous Graph Attention Network for Timeline Summarization (2022.naacl-main)
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| Challenge: | Existing studies on timeline summarization ignore the information interaction between sentences and dates, and combine them as two separate tasks. |
| Approach: | They propose a joint learning-based heterogeneous graph attention network for timeline summarization (HeterTls) they combine date selection and event detection into a unified framework to improve extraction accuracy . |
| Outcome: | The proposed model outperforms state-of-the-art models on four datasets . it significantly outperformed the baseline models on ROUGE scores and date selection metrics . |
Thinking about GPT-3 In-Context Learning for Biomedical IE? Think Again (2022.findings-emnlp)
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| Challenge: | Large pre-trained language models (PLMs) such as GPT-3 have shown strong in-context learning capabilities, which are appealing for domains such as biomedicine that feature high and diverse demands of language technologies but also high data annotation costs. |
| Approach: | They propose to compare the few-shot performance of GPT-3 in-context learning with fine-tuning smaller (i.e., BERT-sized) PLMs on two representative biomedical information extraction tasks: named entity recognition and relation extraction. |
| Outcome: | The proposed model underperforms on two representative biomedical information extraction tasks. |
Prompt-Singer: Controllable Singing-Voice-Synthesis with Natural Language Prompt (2024.naacl-long)
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Yongqi Wang, Ruofan Hu, Rongjie Huang, Zhiqing Hong, Ruiqi Li, Wenrui Liu, Fuming You, Tao Jin, Zhou Zhao
| Challenge: | Recent singing-voice-synthesis methods lack ability to control style attributes of synthesized singing. |
| Approach: | They propose a singing-voice-synthesis method that enables attribute controlling on singer gender, vocal range and volume with natural language. |
| Outcome: | The proposed method achieves favorable control ability and audio quality. |
Beyond Read-Only: Crafting a Comprehensive Chinese Text-to-SQL Dataset for Database Manipulation and Query (2024.findings-naacl)
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| Challenge: | Current research focuses mainly on read operations and ignores other aspects of database operations such as create, update, and delete operations. |
| Approach: | They propose a large-scale cross-domain single-table CRUD operations Chinese Text-to-SQL dataset . the dataset contains 10,000 question/SQl pairs involving 625 tables from different domains . |
| Outcome: | The proposed method achieves 67.08% and 83.8% exact set matching accuracy under read and delete operations, but only 49.6% and 61.8% under create and update operations. |
Evaluating the Expressive Appropriateness of Speech in Rich Contexts (2026.acl-long)
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Tianrui Wang, Ziyang Ma, Yizhou Peng, Haoyu Wang, Zhikang Niu, Zikang Huang, Yihao Wu, Yi-Wen Chao, Yu Jiang, Yuheng Lu, Guanrou Yang, Xuanchen Li, Hexin Liu, Chunyu Qiang, Cheng Gong, Yifan Yang, Tianchi Liu, Junyu Wang, Nana Hou, Meng Ge, Fuming You, Yang Wei, Zhongqian Sun, Hu Haifeng, Xiaobao Wang, Eng Siong Chng, Xie Chen, Longbiao Wang, Jianwu Dang
| Challenge: | Existing methods for evaluating expressive speech focus on word accuracy, naturalness, signal quality, or emotional intensity at the utterance level. |
| Approach: | They propose a framework for Evaluating Expressive Appropriateness in speech that assesses whether a speech sample aligns with the underlying communicative intent implied by its discourse-level narrative context. |
| Outcome: | The proposed framework outperforms existing speech evaluation and analysis systems on a human-annotated test set. |
Understanding ME? Multimodal Evaluation for Fine-grained Visual Commonsense (2022.emnlp-main)
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| Challenge: | Existing models that understand image and text but also cross-reference in-between are lacking in evaluation data resources. |
| Approach: | They propose a multimodal evaluation pipeline to automatically generate question-answer pairs to test models’ understanding of the visual scene, text, and related knowledge. |
| Outcome: | The proposed model can answer the highly semantic VCR question correctly but fails to answer related visual question (Q2), textual question (q3), and background knowledge question ( Q4) as shallow mappings with language priors and unbalanced utilization of information between modalities. |
Parallel Test-Time Scaling for Latent Reasoning Models (2026.acl-long)
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| Challenge: | Parallel test-time scaling is a pivotal approach for enhancing large language models. |
| Approach: | They propose two uncertainty-inspired stochastic strategies for parallel test-time scaling for latent reasoning models and a Latent Reward Model for aggregation. |
| Outcome: | The proposed model scales well with compute and enables effective trajectory selection. |
Migician: Revealing the Magic of Free-Form Multi-Image Grounding in Multimodal Large Language Models (2025.findings-acl)
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You Li, Heyu Huang, Chi Chen, Kaiyu Huang, Chao Huang, Zonghao Guo, Zhiyuan Liu, Jinan Xu, Yuhua Li, Ruixuan Li, Maosong Sun
| Challenge: | Existing MLLMs still struggle to achieve precise grounding in multi-image scenarios. |
| Approach: | They propose a Chain-of-Thought framework that integrates single-image grounding with multi-image comprehension to address this challenge. |
| Outcome: | The proposed model outperforms existing models in multi-image grounding tasks by 24.94% and surpasses larger 70B models. |
Dataset Bias Mitigation in Multiple-Choice Visual Question Answering and Beyond (2023.findings-emnlp)
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Zhecan Wang, Long Chen, Haoxuan You, Keyang Xu, Yicheng He, Wenhao Li, Noel Codella, Kai-Wei Chang, Shih-Fu Chang
| Challenge: | Existing studies have examined dataset biases in VQA benchmarks with short-phrase answers Multiple-choice Question with the LONG Answers (VCR, VLEP, etc.) |
| Approach: | They propose to use Adversarial Data Synthesis (ADS) to generate synthetic training and debiased evaluation data and introduce Intra-sample Counterfactual Training (ICT) to assist models in utilizing synthesized training data. |
| Outcome: | The proposed approach improves model performance even in domain-shifted scenarios. |
Think in Safety: Unveiling and Mitigating Safety Alignment Collapse in Multimodal Large Reasoning Model (2025.emnlp-main)
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| Challenge: | Existing Large Reasoning Models have demonstrated broad application potential, yet their safety and reliability remain critical concerns. |
| Approach: | They conduct a safety evaluation of 13 MLRMs across 5 benchmarks and examine their safety performance. |
| Outcome: | The proposed model improves safety on jailbreak and safety-awareness benchmarks. |
ECC: An Emotion-Cause Conversation Dataset for Empathy Response (2025.emnlp-main)
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| Challenge: | Existing empathy dialogue datasets focus on emotion labels while cause annotations are added post hoc. |
| Approach: | They propose an emotion-cause conversation dataset with 2.4K dialogues that can be scalable . they use a framework that utilizes knowledge and large language models to automatically generate dialogues . |
| Outcome: | The proposed dataset can achieve comparable or even superior performance to existing empathy dialogue datasets. |
A Survey of Generative Information Extraction (2025.coling-main)
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| Challenge: | Information Extraction (IE) is a popular and fundamental task in natural language processing. |
| Approach: | They first review generative information extraction methods based on pre-trained language models and large language models focusing on their adaptation and generalization capabilities. |
| Outcome: | The proposed methods are based on pre-trained language models and large language models, and emphasize the importance of model collaboration. |
Rethinking the Construction of Effective Metrics for Understanding the Mechanisms of Pretrained Language Models (2023.findings-emnlp)
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| Challenge: | Pretrained language models are expected to map input text to a set of vectors while preserving the inherent relationships within the text. |
| Approach: | They propose a new line to constructing metrics for understanding the mechanisms of pretrained language models. |
| Outcome: | The proposed model is referred to as the tree topological probe and is based on a tree topology probe. |
Text-to-Song: Towards Controllable Music Generation Incorporating Vocal and Accompaniment (2024.acl-long)
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Zhiqing Hong, Rongjie Huang, Xize Cheng, Yongqi Wang, Ruiqi Li, Fuming You, Zhou Zhao, Zhimeng Zhang
| Challenge: | Existing studies focus on singing voice synthesis and music generation independently. |
| Approach: | They propose a novel task called Text-to-Song synthesis which incorporates both vocal and accompaniment generation. |
| Outcome: | The proposed method can synthesize songs with comparable quality and style consistency. |
Latent-Condensed Transformer for Efficient Long Context Modeling (2026.acl-long)
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| Challenge: | Existing approaches address these bottlenecks separately: Multi-head Latent Attention (MLA) reduces the KV cache by projecting tokens into a low-dimensional latent space, while sparse attention reduces computation. |
| Approach: | They propose a Latent-Condensed Attention mechanism that performs structured context condensation directly within MLA's latent space. |
| Outcome: | The proposed approach reduces KV cache size and attention cost without adding parameters. |
Seek-and-Solve: Benchmarking MLLMs for Visual Clue-Driven Reasoning in Daily Scenarios (2026.findings-acl)
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Xiaomin Li, Tala Wang, Zichen Zhong, Ying Zhang, Zirui Zheng, Takashi Isobe, Dezhuang Li, Huchuan Lu, You He, Xu Jia
| Challenge: | Existing benchmarks focus on evaluating MLLMs’ pre-existing knowledge or perceptual understanding, often neglecting the critical capability of reasoning. |
| Approach: | They propose a benchmark designed for visual clue-driven reasoning in daily scenarios that combines rigorous grounding in authentic daily activities and challenging query design that necessitates more than surface-level perception. |
| Outcome: | The proposed benchmark identifies visual clues and their ability to provide robust reasoning in daily scenarios. |
DocAgent: An Agentic Framework for Multi-Modal Long-Context Document Understanding (2025.emnlp-main)
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| Challenge: | Existing approaches to document understanding are limited due to limited context length or fail to fully leverage multi-modal information. |
| Approach: | They propose a multi-agent framework for long-context document understanding that imitates human reading practice. |
| Outcome: | The proposed framework surpasses human-level benchmarks on long-context document understanding while maintaining a short context length. |