GenAI4ELab/papercli-papers huggingface dataset:
TRACE-CS: A Synergistic Approach to Explainable Course Scheduling Using LLMs and Logic
Authors: Stylianos Loukas Vasileiou; William Yeoh
Venue: AAAI 2025 (main)

Abstract: We present TRACE-cs, a novel hybrid system that combines symbolic reasoning with large language models (LLMs) to address contrastive queries in scheduling problems. TRACE-cs leverages SAT solving techniques to encode scheduling constraints and generate explanations for user queries, while utilizing an LLM to process the user queries into logical clauses as well as refine the explanations generated by the symbolic solver to natural language sentences. By integrating these components, our approach demonstrates the potential of combining symbolic methods with LLMs to create explainable AI agents with correctness guarantees.
PRIORITY2REWARD: Incorporating Healthworker Preferences for Resource Allocation Planning
Authors: Shresth Verma; Alayna Nguyen; Niclas Boehmer; Lingkai Kong; Milind Tambe
Venue: AAAI 2025 (main)

Abstract: In this paper, we present PRIORITY2REWARD a Large Language Model (LLM) based application which incorporates health worker preferences for resource allocation planning in public health programs. LLMs are increasingly used to design reward functions based on human preferences in Reinforcement Learning problems. We focus on LLM-designed rewards for Restless Multi-Armed Bandits, a framework for allocating limited resources among agents. In the context of public health, our approach empowers grassroots health workers to tailor automated allocation decisions to community needs. We showcase a simulated application of PRIORITY2REWARD for a large-scale mobile health program in India. The tool allows health workers to enter natural language preferences and leverages LLMs to search for reward functions aligned with the preference. Our tool then dynamically showcases how the LLM generated reward function modifies the policy outcomes with respect to different demographic groups in the population. This can help inform policy implementation at a community level.
Real-Time Object Detection and Skeletonization for Motion Prediction in Video Streaming
Authors: Gavin Wong; Yulia Kumar; J.Jenny Li; Dov Kruger
Venue: AAAI 2025 (main)

Abstract: The increasing demand for real-time analysis in video streaming has driven significant advancements in object detection and motion prediction. This paper presents SkelAI, an innovative application that combines YOLOv8, OpenCV, OpenAI
API, and our own innovative algorithms to achieve real-time object detection and medial axis skeletonization tailored explicitly for live video streaming environments. In addition, SkelAI integrates AI-generated image capabilities through
the DALL-E 3 model, enabling the extraction of skeletons from synthetic content that simulates streaming scenarios. The application supports exporting skeleton data in PyTorch-compatible formats, facilitating the training of sequence predicting deep learning models. Comprehensive evaluations demonstrate SkelAI’s enhanced accuracy, efficiency, and versatility compared to existing tools, underscoring its potential
applications in digital animation, biomechanical research and robotics, human-computer interaction, and video compression within streaming platforms.
AutoMV: An Autonomous Agent Framework for Real Estate Marketing Video Generation
Authors: Kuizong Wu; Shaozu Yuan; Chang Shen; Long Xu; Meng Chen
Venue: AAAI 2025 (main)

Abstract: In this paper, we introduce AutoMV, an autonomous agent framework designed for generating real estate marketing videos. The framework integrates a diverse set of existing models into a tool library, allowing the agent to intelligently select and execute the appropriate tools. Given property images and text, the agent decomposes the task into manageable subtasks, generating storyline directives and corresponding camera movement trajectories to guide the video production process. By automatically applying video synthesis techniques and incorporating multimedia elements such as subtitles and background music, the agent transforms static real estate images into dynamic, visually appealing videos, thereby optimizing their impact for digital marketing purposes.
RLLTE: Long-Term Evolution Project of Reinforcement Learning
Authors: Mingqi Yuan; Zequn Zhang; Yang Xu; Shihao Luo; Bo Li; Xin Jin; Wenjun Zeng
Venue: AAAI 2025 (main)

Abstract: We present RLLTE: a long-term evolution, extremely modular, and open-source framework for reinforcement learning (RL) research and application. Beyond delivering top-notch algorithm implementations, RLLTE also serves as a toolkit for developing algorithms. More specifically, RLLTE decouples the RL algorithms completely from the exploitation-exploration perspective, providing a large number of components to accelerate algorithm development and evolution. In particular, RLLTE is the first RL framework to build a comprehensive ecosystem, which includes model training, evaluation, deployment, benchmark hub, and large language model (LLM)-empowered copilot. RLLTE is expected to set standards for RL engineering practice and be highly stimulative for industry and academia. Our documentation, examples, and source code are available at https://github.com/RLE-Foundation/rllte.
Incident Diagnosing and Reporting System Based on Retrieval Augmented Large Language Model
Authors: Peng Yuan; LuAn Tang; Yanchi Liu; Kobayashi Yuji; Moto Sato; Haifeng Chen
Venue: AAAI 2025 (main)

Abstract: The Internet of Things (IoT) is widely used in many applications such as smart city, transportation, healthcare, and environment monitoring. A key task of IoT maintenance is to analyze the abnormal sensor records and generate incident report. Traditionally, domain experts engage in such labor intensive tasks. Recent advances in Large Language Model (LLM) have sparked interests in developing AI based systems to automate these labor intensive processes. However, two critical problems hinder the effective application of LLM in IoTs: (1) LLM lacks background knowledge of deployed IoTs; and (2) the incidents are complex events involving many sensors and components. LLM needs to understand the sensor relationships for accurate diagnosis. In this study, we propose a Retrieval Augmented language model based Incident Diagnosing and Reporting system (RAIDR) for IoT applications. RAIDR retrieves related system documents based on the incident features and leverages LLM to analyze anomalies, identify root causes, and automatically generate incident reports. The automated incident reporting process streamlines end users’ decision making for system maintenance and troubleshooting.
SummPilot: Bridging Efficiency and Customization for Interactive Summarization System
Authors: JungMin Yun; Juhwan Choi; Kyohoon Jin; Soojin Jang; Jinhee Jang; YoungBin Kim
Venue: AAAI 2025 (main)

Abstract: This paper incorporates the efficiency of automatic summarization and addresses the challenge of generating personalized summaries tailored to individual users' interests and requirements. To tackle this challenge, we introduce SummPilot, an interaction-based customizable summarization system. SummPilot leverages a large language model to facilitate both automatic and interactive summarization. Users can engage with the system to understand document content and personalize summaries through interactive components such as semantic graphs, entity clustering, and explainable evaluation. Our demo and user studies demonstrate SummPilot's adaptability and usefulness for customizable summarization.
A Multi-Style Chinese Characters Writing Intelligent Tool Based on Small-scale Training Data
Authors: Zhen Zeng; Jie Wang; Xi Lyu
Venue: AAAI 2025 (main)

Abstract: Chinese characters are a unique blend of language and art, featuring diverse artistic styles. Mastering these styles requires extensive practice and limits public participation. To encourage broader participation, we developed a real-time, interactive tool that supports multiple Chinese character art styles. This tool uses a diffusion model and several LoRA models to capture the diversity of Chinese character art. It generates personalized, visually striking Chinese character artworks in real-time by utilizing handwritten input, allowing users to adjust various stylistic parameters.
MathMistake Checker: A Comprehensive Demonstration for Step-by-Step Math Problem Mistake Finding by Prompt-Guided LLMs
Authors: Tianyang Zhang; Zhuoxuan Jiang; Haotian Zhang; Lin Lin; Shaohua Zhang
Venue: AAAI 2025 (main)

Abstract: We propose a novel system, MathMistake Checker, designed to automate step-by-step mistake finding in mathematical problems with lengthy answers through a two-stage process. The system aims to simplify grading, increase efficiency, and enhance learning experiences from a pedagogical perspective. It integrates advanced technologies, including computer vision and the chain-of-thought capabilities of the latest large language models (LLMs). Our system supports open-ended grading without reference answers and promotes personalized learning by providing targeted feedback. We demonstrate its effectiveness across various types of math problems, such as calculation and word problems.
SWIFT: A Scalable Lightweight Infrastructure for Fine-Tuning
Authors: Yuze Zhao; Jintao Huang; Jinghan Hu; Xingjun Wang; Yunlin Mao; Daoze Zhang; Zeyinzi Jiang; Zhikai Wu; Baole Ai; Ang Wang; Wenmeng Zhou; Yingda Chen
Venue: AAAI 2025 (main)

Abstract: Recent development in Large Language Models (LLMs) and Multi-modal Large Language Models (MLLMs) have achieved superior performance and generalization capabilities, covered extensive areas of traditional tasks. However, existing large model training frameworks support only a limited number of models and techniques, particularly lacking in support for new models, which makes fine-tuning LLMs challenging for most developers. Therefore, we develop SWIFT, a customizable one-stop infrastructure for large models. With support of over 350+ LLMs and 80+ MLLMs, SWIFT stands as the open-source framework that provide the most comprehensive support for fine-tuning large models. In particular, it is the first training framework that provides systematic support for MLLMs. Moreover, SWIFT integrates post-training processes such as inference, evaluation, and quantization, to facilitate fast adoptions of large models in various application scenarios, offering helpful utilities like benchmark comparisons among different training techniques.
ClinicalRAG: Automating Pharmaceutical Label Quality Control with Hierarchical RAG and Large Language Models
Authors: Qiaohui Zhou; Zhongliang Zhou; Michael Johnson; Michelle Ngo; Federico Ferrari; Junshui Ma
Venue: AAAI 2025 (main)

Abstract: Every pharmaceutical product must be accompanied by a comprehensive label that delineates its indications, usage, dosages, and side effects, essential for safe medication practices. Traditionally, creating drug labels is labor-intensive and dependent on manual quality checks. Recent advancements in Large Language Models (LLMs) offer a promising avenue to streamline this process. In this paper we introduce ClinicalRAG, an automated labeling quality control pipeline that integrates LLM with hierarchical Retrieval Augmented Generation that allows to cross-check every statement in the drug label document. ClinicalRAG enhances the reliability of automated drug labeling by systematically reducing hallucination risks, achieving an accuracy of 96.1% in internal validation. With user-friendly interface, our pipeline aims to support pharmaceutical company in drug approval and expedite patients' access to new treatments.
Resource Efficient Sleep Staging via Multi-Level Masking and Prompt Learning
Authors: Lejun Ai; Yulong Li; Haodong Yi; Jixuan Xie; Yue Wang; Jia Liu; Min Chen; Rui Wang
Venue: AAAI 2026 (main)

Abstract: Automatic sleep staging plays a vital role in assessing sleep quality and diagnosing sleep disorders. Most existing methods rely heavily on long and continuous EEG recordings, which poses significant challenges for data acquisition in resource-constrained systems, such as wearable or home-based monitoring systems. In this paper, we propose the task of resource-efficient sleep staging, which aims to reduce the amount of signal collected per sleep epoch while maintaining reliable classification performance. To solve this task, we adopt the masking and prompt learning strategy and propose a novel framework called Mask-Aware Sleep Staging (MASS). Specifically, we design a multi-level masking strategy to promote effective feature modeling under partial and irregular observations. To mitigate the loss of contextual information introduced by masking, we further propose a hierarchical prompt learning mechanism that aggregates unmasked data into a global prompt, serving as a semantic anchor for guiding both patch-level and epoch-level feature modeling. MASS is evalutaed on four datasets, demonstrating state-of-the-art performance, especially when the amount of data is very limited. This result highlights its potential for efficient and scalable deployment in real-world low-resource sleep monitoring environments.
AutoMalDesc: Large-Scale Script Analysis for Cyber Threat Research
Authors: Alexandru-Mihai Apostu; Andrei Preda; Alexandra Daniela Damir; Diana Bolocan; Radu Tudor Ionescu; Ioana Croitoru; Mihaela Gaman
Venue: AAAI 2026 (main)

Abstract: Generating thorough natural language explanations for threat detections remains an open problem in cybersecurity research, despite significant advances in automated malware detection systems. In this work, we present AutoMalDesc, an automated static analysis summarization framework that, following initial training on a small set of expert-curated examples, operates independently at scale. This approach leverages an iterative self-paced learning pipeline to progressively enhance output quality through synthetic data generation and validation cycles, eliminating the need for extensive manual data annotation. Evaluation across 3,600 diverse samples in five scripting languages demonstrates statistically significant improvements between iterations, showing consistent gains in both summary quality and classification accuracy. Our comprehensive validation approach combines quantitative metrics based on established malware labels with qualitative assessment from both human experts and LLM-based judges, confirming both technical precision and linguistic coherence of generated summaries. To facilitate reproducibility and advance research in this domain, we publish our complete dataset of more than 100K script samples, including annotated seed (900) and test (3.6K) datasets, along with our methodology and evaluation framework.
Beyond Content: A Comprehensive Speech Toxicity Dataset and Detection Framework Incorporating Paralinguistic Cues
Authors: Zhongjie Ba; Liang Yi; Peng Cheng; Qingcao Li; Qinglong Wang; Li Lu
Venue: AAAI 2026 (main)

Abstract: Toxic speech detection has become a crucial challenge in maintaining safe online communication environments. However, existing approaches to toxic speech detection often neglect the contribution of paralinguistic cues, such as emotion, intonation, and speech rate, which are key to detecting speech toxicity. Moreover, current toxic speech datasets are predominantly text-based, limiting the development of models that can capture paralinguistic cues. To address these challenges, we present ToxiAlert-Bench, a large-scale audio dataset comprising over 30,000 audio clips annotated with seven major toxic categories and twenty fine-grained toxic labels. Uniquely, our dataset annotates toxicity sources—distinguishing between textual content and paralinguistic origins—for comprehensive toxic speech analysis. Furthermore, we propose a dual-head neural network with a multi-stage training strategy tailored for toxic speech detection. This architecture features two task-specific classification headers: one for identifying the source of sensitivity (textual or paralinguistic), and the other for categorizing the specific toxic type. The training process involves independent head training followed by joint fine-tuning to reduce task interference. To mitigate data class imbalance, we incorporate class-balanced sampling and weighted loss functions. Our experimental results show that leveraging paralinguistic features significantly improves detection performance. Our method consistently outperforms existing baselines across multiple evaluation metrics, with a 21.1% relative improvement in Macro-F1 score and a 13.0% relative gain in accuracy over the strongest baseline, highlighting its enhanced effectiveness and practical applicability.
Modulation-Based Backdoors: Leveraging Amplitude and Frequency Patterns to Attack Speaker Recognition
Authors: Hanbo Cai; Pengcheng Zhang; Yan Xiao; De Li; Hanting Chu; Ying Luo
Venue: AAAI 2026 (main)

Abstract: Deep neural networks (DNNs) are widely and successfully applied in the field of speaker recognition. However, recent studies reveal that these models are vulnerable to backdoor attacks, where adversaries inject malicious behaviors into victim models by poisoning the training process. Existing attack methods often rely on environmental noise or complex voice transformations, which are typically difficult to implement and exhibit poor stealthiness. To address these issues, this paper proposes two modulation-based backdoor attacks that leverage frequency modulation (FM) and amplitude modulation (AM) to construct audio triggers. In real-world scenarios, regular variations in frequency and amplitude are often imperceptible to human listeners, making the proposed attacks more covert. Experimental results show that our methods achieve high attack success rates in both digital and physical settings, while also demonstrating strong resistance to various state-of-the-art backdoor defenses.
Learning Structurally Stabilized Representations for Lossless DNA Storage
Authors: Ben Cao; Xue Li; Tiantian He; Bin Wang; Shihua Zhou; Xiaohu Wu; Qiang Zhang
Venue: AAAI 2026 (main)

Abstract: This paper presents Reed-Solomon coded single-stranded representation learning (RSRL), a novel end-to-end model for learning representations for lossless DNA data storage. In contrast to existing learning-based methods, RSRL is inspired by both error-correction codec and structural biology. Specifically, RSRL first learns the representations for the subsequent storage from the binary data transformed by the Reed-Solomon codec (RS code). Then, the representations are masked by an RS-code-informed mask to focus on correcting the burst errors occurring in the learning process. The synergy of RS masks and graph attention enables active error localization, breaking through the limitations of traditional passive error correction. With the decoded representations with error corrections, a novel biologically stabilized loss is formulated to regularize the data representations to possess stable single-stranded structures. By incorporating these novel strategies, RSRL can learn highly durable, dense, and lossless representations for subsequent storage tasks in DNA sequences. The proposed RSRL has been compared with a number of baselines in real-world tasks of multi-type data storage. The experimental results obtained demonstrate that RSRL can store diverse types of data with much higher information density and durability, but much lower error rates.
ViG-RAG: Video-aware Graph Retrieval-Augmented Generation via Temporal and Semantic Hybrid Reasoning
Authors: Zongsheng Cao; Anran Liu; Yangfan He; Jing Li; Bo Zhang; Zigan Wang
Venue: AAAI 2026 (main)

Abstract: Retrieval-augmented generation (RAG) has greatly improved Large Language Models (LLMs) by adding external knowledge. However, current RAG-based methods face difficulties with long-context video understanding due to two main challenges. First, Current RAG-based methods for long-context video understanding struggle to effectively integrate multimodal and long-range temporal information, resulting in fragmented and context-insensitive knowledge representations. Furthermore, their retrieval mechanisms often rely on static textual matching, failing to dynamically align user queries with the most relevant video segments and leading to suboptimal downstream performance. To overcome these issues, we introduce ViG-RAG, a new framework to enhance long-context video understanding through structured textual knowledge grounding and multi-modal retrieval. Specifically, we segment video transcripts into structured units, extract key entities, form temporal connections, and assign confidence for evidence, enabling coherent long-range reasoning. In this way, it utilizes a knowledge-aware grounding mechanism and a context-aware retrieval process that dynamically builds a probabilistic temporal knowledge graph to organize multi-video content. To improve retrieval accuracy, we propose a hybrid retrieval strategy for semantic and temporal features, with an adaptive distribution modeling the relevance. In this way, it achieves the optimal retrieval distribution for each query, enhancing generation efficiency by reducing unnecessary computations. On top of this, ViG-RAG uses a vision-language model to integrate semantic anchors, expanded contextual fields, and selected video frames, generating an accurate response. We evaluate ViG-RAG on several benchmarks, demonstrating that it significantly surpasses current RAG-based methods.
Transferable Backdoor Attacks for Code Models via Sharpness-Aware Adversarial Perturbation
Authors: Shuyu Chang; Haiping Huang; Yanjun Zhang; Yujin Huang; Fu Xiao; Leo Yu Zhang
Venue: AAAI 2026 (main)

Abstract: Code models are increasingly adopted in software development but remain vulnerable to backdoor attacks via poisoned training data. Existing backdoor attacks on code models face a fundamental trade-off between transferability and stealthiness. Static trigger-based attacks insert fixed dead code patterns that transfer well across models and datasets but are easily detected by code-specific defenses. In contrast, dynamic trigger-based attacks adaptively generate context-aware triggers to evade detection but suffer from poor cross-dataset transferability. Moreover, they rely on unrealistic assumptions of identical data distributions between poisoned and victim training data, limiting their practicality. To overcome these limitations, we propose Sharpness-aware Transferable Adversarial Backdoor (STAB), a novel attack that achieves both transferability and stealthiness without requiring complete victim data. STAB is motivated by the observation that adversarial perturbations in flat regions of the loss landscape transfer more effectively across datasets than those in sharp minima. To this end, we train a surrogate model using Sharpness-Aware Minimization to guide model parameters toward flat loss regions, and employ Gumbel-Softmax optimization to enable differentiable search over discrete trigger tokens for generating context-aware adversarial triggers. Experiments across three datasets and two code models show that STAB outperforms prior attacks in terms of transferability and stealthiness. It achieves a 73.2% average attack success rate after defense, outperforming static trigger–based attacks that fail under defense. STAB also surpasses the best dynamic trigger–based attack by 12.4% in cross-dataset attack success rate and maintains performance on clean inputs.