Papers with integrated framework
SCRIPTMIND: Crime Script Inference and Cognitive Evaluation for LLM-based Social Engineering Scam Detection System (2026.eacl-industry)
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| Challenge: | Large Language Models (LLMs) have shown promise in identifying deception, but their cognitive assistance potential remains underexplored. |
| Approach: | They propose a framework for LLM-based scam detection that bridges automated reasoning and human cognition. |
| Outcome: | The proposed framework outperforms GPT-4o in the Korean scam detection and phone scam simulations. |
Token Sequence Labeling vs. Clause Classification for English Emotion Stimulus Detection (2020.starsem-1)
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| Challenge: | Emotion stimulus detection is the task of finding the cause of an emotion in a textual description. |
| Approach: | They propose to evaluate whether clause classification or token sequence labeling is better for emotion stimulus detection in English. |
| Outcome: | The proposed framework compares clause classification and token sequence labeling on four English datasets. |
TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Industry Systems (2024.emnlp-industry)
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Yilun Kong, Jingqing Ruan, YiHong Chen, Bin Zhang, Tianpeng Bao, Shi Shiwei, Du Qing, Xiaoru Hu, Hangyu Mao, Ziyue Li, Xingyu Zeng, Rui Zhao, Xueqian Wang
| Challenge: | Large language models have demonstrated proficiency in addressing tasks that necessitate a combination of task planning and the usage of external tools. |
| Approach: | They propose a framework to enhance the task planning and tool usage abilities of LLMs in industrial systems. |
| Outcome: | The proposed framework enhances the task planning and tool usage abilities of LLM-based agents in industrial systems. |
TRANSLATIONCORRECT: A Unified Framework for Machine Translation Post-Editing with Predictive Error Assistance (2025.acl-demo)
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| Challenge: | Current workflows for machine translation (MT) post-editing and research data collection are inefficient and time-consuming. |
| Approach: | They propose a framework that combines MT and error prediction within a single environment. |
| Outcome: | **TranslationCorrect** exports high-quality span-based annotations in the Error Span Annotation format, using an error taxonomy inspired by Multidimensional Quality Metrics (MQM). |
Task Assignment meets Annotator Modeling: Human-LLM Collaborative Annotation with Constraints (2026.acl-srw)
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| Challenge: | Existing approaches to label annotation are labor-intensive and time-consuming. |
| Approach: | They propose a framework that estimates per-task accuracy from task features using a learning from crowds model and incorporates these estimations into a linear programming formulation that assigns tasks under practical constraints. |
| Outcome: | The proposed method achieves comparable accuracy to baseline methods while satisfying given constraints. |
MolTC: Towards Molecular Relational Modeling In Language Models (2024.findings-acl)
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Junfeng Fang, Shuai Zhang, Chang Wu, Zhengyi Yang, Zhiyuan Liu, Sihang Li, Kun Wang, Wenjie Du, Xiang Wang
| Challenge: | Molecular Relational Learning (MRL) is a promising way to understand interactions between molecular pairs. |
| Approach: | They propose a novel LLM-based multi-modal framework for molecular interaction modeling following Chain-of-Thought (CoT) theory which integrates graphical information of two molecules in pair. |
| Outcome: | The proposed framework integrates graphical information of two molecules in pair. |
Regular Expression Guided Entity Mention Mining from Noisy Web Data (D18-1)
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| Challenge: | Named Entity Recognition (NER) is a subtask of the broader problem of Information Extraction (IE) from text. |
| Approach: | They propose a framework that uses Regular Expressions to identify entities from web data . they combine expressive power of REs with ability of deep learning to learn from large data a human expert is asked to label a small set of documents . |
| Outcome: | The proposed framework achieves impressive accuracy while requiring modest human effort. |
PullNet: Open Domain Question Answering with Iterative Retrieval on Knowledge Bases and Text (D19-1)
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| Challenge: | Experimentally PullNet improves over the prior state-of-the-art open domain question answering systems. |
| Approach: | They propose a framework for learning what to retrieve and reasoning with heterogeneous information to find the best answer. |
| Outcome: | The proposed framework improves over the prior state-of-the-art in open domain question answering . it is weakly supervised, requiring question-answer pairs but not gold inference paths . |
VoxMind: An End-to-End Agentic Spoken Dialogue System (2026.acl-long)
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Tianle Liang, Yifu Chen, Shengpeng Ji, Yijun Chen, Zhiyang Jia, Jingyu Lu, Fan Zhuo, Xueyi Pu, Yangzhuo Li, Zhou Zhao
| Challenge: | Existing research on end-to-end spoken dialogue models has focused on core perception and generation, with limited exploration of tool-augmented extensions. |
| Approach: | They propose a framework to equip end-to-end spoken dialogue models with comprehensive agentic abilities by leveraging a 470-hour AgentChat dataset. |
| Outcome: | The proposed framework outperforms Gemini-2.5-Pro on spoken agent tasks while maintaining general conversational quality. |
SDAR-VL: Stable and Efficient Block-wise Diffusion for Vision-Language Understanding (2026.acl-long)
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| Challenge: | Existing block-wise discrete diffusion models lack robust autoregressive (AR) decoders. |
| Approach: | They propose a block-wise discrete diffusion framework for large-scale vision-language understanding with a progressive beta noise curriculum. |
| Outcome: | The proposed framework improves training efficiency, convergence stability, and task performance over conventional block diffusion. |
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. |