Datasets:

ai-conferences
/

AAAI2026

paper_id string	title string	paper_url string	pdf_url string	authors list	abstract large_string	track string	primary_area string	doi string	volume string	issue string	pages string	abstract_source string	arxiv_id string	arxiv_id_source string
10.1609/aaai.v40i26.39321	Posterior Label Smoothing for Node Classification	https://ojs.aaai.org/index.php/AAAI/article/view/39321	https://ojs.aaai.org/index.php/AAAI/article/download/39321/43282	[ "Jaeseung Heo", "MoonJeong Park", "Dongwoo Kim" ]	Label smoothing is a widely studied regularization technique in machine learning. However, its potential for node classification in graph-structured data, spanning homophilic to heterophilic graphs, remains largely unexplored. We introduce posterior label smoothing, a novel method for transductive node classification t...	main	Machine Learning	10.1609/aaai.v40i26.39321	40	26	21708-21716	official	2406.00410	title_snapshot
10.1609/aaai.v40i2.37144	Tree-Based Stochastic Optimization for Solving Large-Scale Urban Network Security Games	https://ojs.aaai.org/index.php/AAAI/article/view/37144	https://ojs.aaai.org/index.php/AAAI/article/download/37144/41106	[ "Shuxin Zhuang", "Linjian Meng", "Shuxin Li", "Minming Li", "Youzhi Zhang" ]	Urban Network Security Games (UNSGs), which model the strategic allocation of limited security resources on city road networks, are critical for urban safety. However, finding a Nash Equilibrium (NE) in large-scale UNSGs is challenging due to their massive and combinatorial action spaces. One common approach to address...	main	Application Domains	10.1609/aaai.v40i2.37144	40	2	1668-1675	official	2511.10072	title_snapshot
10.1609/aaai.v40i1.37011	HyperLoad: A Cross-Modality Enhanced Large Language Model-Based Framework for Green Data Center Cooling Load Prediction	https://ojs.aaai.org/index.php/AAAI/article/view/37011	https://ojs.aaai.org/index.php/AAAI/article/download/37011/40973	[ "Haoyu Jiang", "Boan Qu", "Junjie Zhu", "Fanjie Zeng", "Xiaojie Lin", "Wei Zhong" ]	The explosive growth of artificial intelligence is exponentially escalating computational demand, inflating data center energy use and carbon emissions, and spurring rapid deployment of green data centers to relieve resource and environmental stress. Achieving sub-minute orchestration of renewables, storage, and loads,...	main	Application Domains	10.1609/aaai.v40i1.37011	40	1	480-488	official	2512.19114	title_snapshot
10.1609/aaai.v40i14.38182	OW-DAR: Dual-Granularity Adaptive Reconstruction-Error Modeling for Open-World Object Detection	https://ojs.aaai.org/index.php/AAAI/article/view/38182	https://ojs.aaai.org/index.php/AAAI/article/download/38182/42144	[ "Linhua Ye", "Xing Xi", "Ronghua Luo" ]	Open-world object detection (OWOD) aims to detect known and unknown objects in dynamic environments. However, only known classes are labeled during training, making it challenging for detectors to recognize unknown objects during inference. Existing methods typically rely on supervision from known categories, leading m...	main	Computer Vision	10.1609/aaai.v40i14.38182	40	14	11946-11954	official	null	null
10.1609/aaai.v40i15.38280	MolSight: Optical Chemical Structure Recognition with SMILES Pretraining, Multi-Granularity Learning and Reinforcement Learning	https://ojs.aaai.org/index.php/AAAI/article/view/38280	https://ojs.aaai.org/index.php/AAAI/article/download/38280/42242	[ "Wenrui Zhang", "Xinggang Wang", "Bin Feng", "Wenyu Liu" ]	Optical Chemical Structure Recognition (OCSR) plays a pivotal role in modern chemical informatics, enabling the automated conversion of chemical structure images from scientific literature, patents, and educational materials into machine-readable molecular representations. This capability is essential for large-scale c...	main	Computer Vision	10.1609/aaai.v40i15.38280	40	15	12825-12833	official	2511.17300	title_snapshot
10.1609/aaai.v40i16.38359	Empowering Sparse-Input Neural Radiance Fields with Dual-Level Semantic Guidance from Dense Novel Views	https://ojs.aaai.org/index.php/AAAI/article/view/38359	https://ojs.aaai.org/index.php/AAAI/article/download/38359/42321	[ "Yingji Zhong", "Kaichen Zhou", "Zhihao Li", "Lanqing Hong", "Zhenguo Li", "Dan Xu" ]	Neural Radiance Fields (NeRF) have shown remarkable capabilities for photorealistic novel view synthesis. One major deficiency of NeRF is that dense inputs are typically required, and the rendering quality will drop drastically given sparse inputs. In this paper, we highlight the effectiveness of rendered semantics fro...	main	Computer Vision	10.1609/aaai.v40i16.38359	40	16	13539-13547	official	2503.02230	title_snapshot
10.1609/aaai.v40i16.38398	Content Diversity-guided Ambiguity Mitigation for Open-Set Noisy Label Learning	https://ojs.aaai.org/index.php/AAAI/article/view/38398	https://ojs.aaai.org/index.php/AAAI/article/download/38398/42360	[ "Zhihao Zhou", "Rui Li", "Xueying Li" ]	Open-set noisy label learning faces a critical challenge in maintaining robust DNN performance when training data contain both in-distribution noisy (IDN) and out-of-distribution (OOD) samples. These noisy samples induce overconfident but erroneous predictions due to their ambiguous positions relative to category bound...	main	Computer Vision	10.1609/aaai.v40i16.38398	40	16	13889-13897	official	null	null
10.1609/aaai.v40i15.38304	RPE-PAD: Relative Pose Estimation for Pose-agnostic Anomaly Detection	https://ojs.aaai.org/index.php/AAAI/article/view/38304	https://ojs.aaai.org/index.php/AAAI/article/download/38304/42266	[ "Zhipeng Zhang", "Mengzan Qi", "Rongkang Ma", "Yingying Fang", "Guixu Zhang", "Tieyong Zeng", "Zhi Li" ]	Pose-agnostic Anomaly Detection (PAD) aims to detect anomalies when the poses of query images are unknown and differ from those in the training set. Therefore, accurately estimating the camera poses for the query images in the test set is critical for this task. Existing query-specific framework methods require re-opti...	main	Computer Vision	10.1609/aaai.v40i15.38304	40	15	13043-13051	official	null	null
10.1609/aaai.v40i15.38276	Beyond Illumination: Fine-Grained Detail Preservation in Extreme Dark Image Restoration	https://ojs.aaai.org/index.php/AAAI/article/view/38276	https://ojs.aaai.org/index.php/AAAI/article/download/38276/42238	[ "Tongshun Zhang", "Pingping Liu", "Zixuan Zhong", "Zijian Zhang", "Qiuzhan Zhou" ]	Recovering fine-grained details in extremely dark images remains challenging due to severe structural information loss and noise corruption. Existing enhancement methods often fail to preserve intricate details and sharp edges, limiting their effectiveness in downstream applications like text and edge detection. To add...	main	Computer Vision	10.1609/aaai.v40i15.38276	40	15	12789-12797	official	2508.03336	title_snapshot
10.1609/aaai.v40i37.40350	Multi-granularity Interactive Attention Framework for Residual Hierarchical Pronunciation Assessment	https://ojs.aaai.org/index.php/AAAI/article/view/40350	https://ojs.aaai.org/index.php/AAAI/article/download/40350/44311	[ "Hong Han", "Hao-Chen Pei", "Zhao-Zheng Nie", "Xin Luo", "Xin-Shun Xu" ]	Automatic pronunciation assessment plays a crucial role in computer-assisted pronunciation training systems. Due to the ability to perform multiple pronunciation tasks simultaneously, multi-aspect multi-granularity pronunciation assessment methods are gradually receiving more attention and achieving better performance ...	main	Natural Language Processing	10.1609/aaai.v40i37.40350	40	37	30916-30924	official	2601.01745	title_snapshot
10.1609/aaai.v40i37.40431	Causal Tracing of Object Representations in Large Vision Language Models: Mechanistic Interpretability and Hallucination Mitigation	https://ojs.aaai.org/index.php/AAAI/article/view/40431	https://ojs.aaai.org/index.php/AAAI/article/download/40431/44392	[ "Qiming Li", "Zekai Ye", "Xiaocheng Feng", "Weihong Zhong", "Weitao Ma", "Xiachong Feng" ]	Despite the remarkable advancements of Large Vision-Language Models (LVLMs), the mechanistic interpretability remains underexplored. Existing analyses are insufficiently comprehensive and lack examination covering visual and textual tokens, model components, and the full range of layers. This limitation restricts actio...	main	Natural Language Processing	10.1609/aaai.v40i37.40431	40	37	31645-31653	official	2511.05923	title_snapshot
10.1609/aaai.v40i38.40461	Bias-Restrained Prefix Representation Finetuning for Mathematical Reasoning	https://ojs.aaai.org/index.php/AAAI/article/view/40461	https://ojs.aaai.org/index.php/AAAI/article/download/40461/44422	[ "Sirui Liang", "Pengfei Cao", "Jian Zhao", "Cong Huang", "Jun Zhao", "Kang Liu" ]	Parameter-Efficient finetuning (PEFT) enhances model performance on downstream tasks by updating a minimal subset of parameters. Representation finetuning (ReFT) methods further improve efficiency by freezing model weights and optimizing internal representations with fewer parameters than PEFT, outperforming PEFT on se...	main	Natural Language Processing	10.1609/aaai.v40i38.40461	40	38	31916-31924	official	2511.10707	title_snapshot
10.1609/aaai.v40i12.37985	When Person Re-Identification Meets Event Camera: A Benchmark Dataset and an Attribute-Guided Re-Identification Framework	https://ojs.aaai.org/index.php/AAAI/article/view/37985	https://ojs.aaai.org/index.php/AAAI/article/download/37985/41947	[ "Xiao Wang", "Qian Zhu", "Shujuan Wu", "Bo Jiang", "Shiliang Zhang" ]	Recent researchers have proposed using event cameras for person re-identification (ReID) due to their promising performance and better balance in terms of privacy protection, event camera-based person ReID has attracted significant attention. Currently, mainstream event-based person ReID algorithms primarily focus on f...	main	Computer Vision	10.1609/aaai.v40i12.37985	40	12	10172-10180	official	2507.13659	title_snapshot
10.1609/aaai.v40i13.38028	Asymmetric Cross-Modal Knowledge Distillation: Bridging Modalities with Weak Semantic Consistency	https://ojs.aaai.org/index.php/AAAI/article/view/38028	https://ojs.aaai.org/index.php/AAAI/article/download/38028/41990	[ "Riling Wei", "Kelu Yao", "Chuanguang Yang", "Jin Wang", "Zhuoyan Gao", "Chao Li" ]	Cross-modal Knowledge Distillation has demonstrated promising performance on paired modalities with strong semantic connections, referred to as Symmetric Cross-modal Knowledge Distillation (SCKD). However, implementing SCKD becomes exceedingly constrained in real-world scenarios due to the limited availability of paire...	main	Computer Vision	10.1609/aaai.v40i13.38028	40	13	10557-10565	official	2511.08901	title_snapshot
10.1609/aaai.v40i22.38958	H-GAR: A Hierarchical Interaction Framework via Goal-Driven Observation-Action Refinement for Robotic Manipulation	https://ojs.aaai.org/index.php/AAAI/article/view/38958	https://ojs.aaai.org/index.php/AAAI/article/download/38958/42920	[ "Yijie Zhu", "Rui Shao", "Ziyang Liu", "Jie He", "Jizhihui Liu", "Jiuru Wang", "Zitong Yu" ]	Unified video and action prediction models hold great potential for robotic manipulation, as future observations offer contextual cues for planning, while actions reveal how interactions shape the environment. However, most existing approaches treat observation and action generation in a monolithic and goal-agnostic ma...	main	Intelligent Robotics	10.1609/aaai.v40i22.38958	40	22	18882-18890	official	2511.17079	title_snapshot
10.1609/aaai.v40i30.39745	MultiTab: A Scalable Foundation for Multitask Learning on Tabular Data	https://ojs.aaai.org/index.php/AAAI/article/view/39745	https://ojs.aaai.org/index.php/AAAI/article/download/39745/43706	[ "Dimitrios Sinodinos", "Jack Yi Wei", "Narges Armanfard" ]	Tabular data is the most abundant data type in the world, powering systems in finance, healthcare, e‑commerce, and beyond. As tabular datasets grow and span multiple related targets, there is an increasing need to exploit shared task information for improved multitask generalization. Multitask learning (MTL) has emerge...	main	Machine Learning	10.1609/aaai.v40i30.39745	40	30	25499-25507	official	2511.09970	title_snapshot
10.1609/aaai.v40i30.39758	Improving Stochastic Action-Constrained Reinforcement Learning via Truncated Distributions	https://ojs.aaai.org/index.php/AAAI/article/view/39758	https://ojs.aaai.org/index.php/AAAI/article/download/39758/43719	[ "Roland Stolz", "Michael Eichelbeck", "Matthias Althoff" ]	In reinforcement learning (RL), it is often advantageous to consider additional constraints on the action space to ensure safety or action relevance. Existing work on such action-constrained RL faces challenges regarding effective policy updates, computational efficiency, and predictable runtime. Recent work proposes t...	main	Machine Learning	10.1609/aaai.v40i30.39758	40	30	25617-25626	official	2511.22406	title_snapshot
10.1609/aaai.v40i40.40721	HeartLLM: Discretized ECG Tokenization for LLM-Based Diagnostic Reasoning	https://ojs.aaai.org/index.php/AAAI/article/view/40721	https://ojs.aaai.org/index.php/AAAI/article/download/40721/44682	[ "Jinning Yang", "Wenjie Sun", "Wen Shi" ]	Electrocardiography (ECG) plays a central role in cardiovascular diagnostics, yet existing automated approaches often struggle to generalize across clinical tasks and offer limited support for open-ended reasoning. We present HeartLLM, a novel framework that integrates time-series (TS) and language modeling by enabling...	main	Natural Language Processing	10.1609/aaai.v40i40.40721	40	40	34250-34258	official	2508.15338	title_snapshot
10.1609/aaai.v40i29.39591	SkipCat: Rank-Maximized Low-Rank Compression of Large Language Models via Shared Projection and Block Skipping	https://ojs.aaai.org/index.php/AAAI/article/view/39591	https://ojs.aaai.org/index.php/AAAI/article/download/39591/43552	[ "Yu-Chen Lu", "Sheng-Feng Yu", "Hui-Hsien Weng", "Pei-Shuo Wang", "Yu-Fang Hu", "Liang Hung-Chun", "Hung-Yueh Chiang", "Kai-Chiang Wu" ]	Large language models (LLM) have achieved remarkable performance across a wide range of tasks. However, their substantial parameter sizes pose significant challenges for deployment on edge devices with limited computational and memory resources. Low-rank compression is a promising approach to address this issue, as it ...	main	Machine Learning	10.1609/aaai.v40i29.39591	40	29	24124-24132	official	2512.13494	title_snapshot
10.1609/aaai.v40i29.39596	Supervised Dynamic Dimension Reduction with Deep Neural Network	https://ojs.aaai.org/index.php/AAAI/article/view/39596	https://ojs.aaai.org/index.php/AAAI/article/download/39596/43557	[ "Zhanye Luo", "Yuefeng Han", "Xiufan Yu" ]	This paper studies the problem of dimension reduction, tailored to improving time series forecasting with high-dimensional predictors. We propose a novel Supervised Deep Dynamic Principal component analysis (SDDP) framework that incorporates the target variable and lagged observations into the factor extraction process...	main	Machine Learning	10.1609/aaai.v40i29.39596	40	29	24169-24177	official	2508.03546	title_snapshot
10.1609/aaai.v40i29.39584	Adapt Before Continual Learning	https://ojs.aaai.org/index.php/AAAI/article/view/39584	https://ojs.aaai.org/index.php/AAAI/article/download/39584/43545	[ "Aojun Lu", "Tao Feng", "Hangjie Yuan", "Chunhui Ding", "Yanan Sun" ]	Continual Learning (CL) seeks to enable neural networks to incrementally acquire new knowledge (plasticity) while retaining existing knowledge (stability). Although pre-trained models (PTMs) have provided a strong foundation for CL, existing approaches face a fundamental challenge in balancing these two competing objec...	main	Machine Learning	10.1609/aaai.v40i29.39584	40	29	24061-24069	official	2506.03956	title_snapshot
10.1609/aaai.v40i28.39483	MIDILM: A Dual-Path Model for Controllable Text-to-MIDI Generation	https://ojs.aaai.org/index.php/AAAI/article/view/39483	https://ojs.aaai.org/index.php/AAAI/article/download/39483/43444	[ "Shuyu Li", "Dooho Choi", "Yunsick Sung" ]	Text-to-MIDI generation offers editable and hierarchical control over symbolic music generation. Previous approaches either convert text into a limited set of musical attributes and generate music based on these attributes, which limits semantic controllability, or use end-to-end models that map text directly to music ...	main	Machine Learning	10.1609/aaai.v40i28.39483	40	28	23160-23168	official	null	null
10.1609/aaai.v40i28.39561	DeLo: Dual Decomposed Low-Rank Experts Collaboration for Continual Missing Modality Learning	https://ojs.aaai.org/index.php/AAAI/article/view/39561	https://ojs.aaai.org/index.php/AAAI/article/download/39561/43522	[ "Xiwei Liu", "Yulong Li", "Feilong Tang", "Imran Razzak" ]	Adapting Large Multimodal Models (LMMs) to real-world scenarios poses the dual challenges of learning from sequential data streams while handling frequent modality incompleteness, a task known as Continual Missing Modality Learning (CMML). However, existing works on CMML have predominantly relied on prompt tuning, a te...	main	Machine Learning	10.1609/aaai.v40i28.39561	40	28	23855-23863	official	2603.01632	title_snapshot
10.1609/aaai.v40i5.37341	Rethinking Long-tailed Dataset Distillation: A Uni-Level Framework with Unbiased Recovery and Relabeling	https://ojs.aaai.org/index.php/AAAI/article/view/37341	https://ojs.aaai.org/index.php/AAAI/article/download/37341/41303	[ "Xiao Cui", "Yulei Qin", "Xinyue Li", "Wengang Zhou", "Hongsheng Li", "Houqiang Li" ]	Dataset distillation creates a small distilled set that enables efficient training by capturing key information from the full dataset. While existing dataset distillation methods perform well on balanced datasets, they struggle under long-tailed distributions, where imbalanced class frequencies induce biased model repr...	main	Computer Vision	10.1609/aaai.v40i5.37341	40	5	3443-3451	official	2511.18858	title_snapshot
10.1609/aaai.v40i7.37419	From Pixels to Logic: A Perception-Reasoning Decomposition Framework for Open-World Referring Expression Comprehension	https://ojs.aaai.org/index.php/AAAI/article/view/37419	https://ojs.aaai.org/index.php/AAAI/article/download/37419/41381	[ "Lihong Huang", "Sheng-hua Zhong", "Zhi Zhang", "Yan Liu" ]	Recent advances in Referring Expression Comprehension (REC) have been largely driven by supervised learning on curated datasets, where each expression is assumed to refer to exactly one known object. However, such assumptions rarely hold in real-world scenarios, where expressions can refer to multiple objects, fail to ...	main	Computer Vision	10.1609/aaai.v40i7.37419	40	7	5058-5066	official	null	null
10.1609/aaai.v40i8.37523	DigimonGPT: An Evolvable Agent with Hierarchical Human-like Memory for Video Question Answering	https://ojs.aaai.org/index.php/AAAI/article/view/37523	https://ojs.aaai.org/index.php/AAAI/article/download/37523/41485	[ "Borui Li", "Xingcai Zhang", "Tianen Liu", "Shuai Wang", "Yun Cheng", "Shuai Wang" ]	Video question answering (VideoQA), whose goal is to produce answers through the integration of linguistic and visual understanding, has emerged as a significant research focus. Although Large Multimodal Models (LMMs) and autonomous agent methods have achieved notable advances in VideoQA, excessive computational overhe...	main	Computer Vision	10.1609/aaai.v40i8.37523	40	8	6001-6009	official	null	null
10.1609/aaai.v40i8.37604	Mono3DVG-EnSD: Enhanced Spatial-aware and Dimension-decoupled Text Encoding for Monocular 3D Visual Grounding	https://ojs.aaai.org/index.php/AAAI/article/view/37604	https://ojs.aaai.org/index.php/AAAI/article/download/37604/41566	[ "Yuzhen Li", "Min Liu", "Zhaoyang Li", "Yuan Bian", "Xueping Wang", "Erbo Zhai", "Yaonan Wang" ]	Monocular 3D Visual Grounding (Mono3DVG) is an emerging task that locates 3D objects in RGB images using text descriptions with geometric cues. However, existing methods face two key limitations. Firstly, they often over-rely on high-certainty keywords that explicitly identify the target object while neglecting critica...	main	Computer Vision	10.1609/aaai.v40i8.37604	40	8	6726-6734	official	2511.06908	title_snapshot
10.1609/aaai.v40i8.37531	Refine3D: Scene-Adaptive Reference Point Refinement for Sparse 3D Object Detection	https://ojs.aaai.org/index.php/AAAI/article/view/37531	https://ojs.aaai.org/index.php/AAAI/article/download/37531/41493	[ "Fan Li", "Jing Lu", "Yunlu Xu", "Changhong Wu", "Tao Xu", "Zhaoyi Xiang", "Yi Niu" ]	Sparse query-based detectors have emerged as the dominant paradigm in camera-only 3D object detection, owing to their exceptional performance and computational efficiency. A central component of these approaches is the use of reference points, which serve as learnable spatial anchors to guide queries in localizing targ...	main	Computer Vision	10.1609/aaai.v40i8.37531	40	8	6073-6081	official	null	null
10.1609/aaai.v40i5.37329	Adaptive Agent Selection and Interaction Network for Image-to-Point Cloud Registration	https://ojs.aaai.org/index.php/AAAI/article/view/37329	https://ojs.aaai.org/index.php/AAAI/article/download/37329/41291	[ "Zhixin Cheng", "Xiaotian Yin", "Jiacheng Deng", "Bohao Liao", "Yujia Chen", "Xu Zhou", "Baoqun Yin", "Tianzhu Zhang" ]	Typical detection-free methods for image-to-point cloud registration leverage transformer-based architectures to aggregate cross-modal features and establish correspondences. However, they often struggle under challenging conditions, where noise disrupts similarity computation and leads to incorrect correspondences. Mo...	main	Computer Vision	10.1609/aaai.v40i5.37329	40	5	3335-3343	official	2511.05965	title_snapshot
10.1609/aaai.v40i12.37963	Modality-Aware Bias Mitigation and Invariance Learning for Unsupervised Visible-Infrared Person Re-Identification	https://ojs.aaai.org/index.php/AAAI/article/view/37963	https://ojs.aaai.org/index.php/AAAI/article/download/37963/41925	[ "Menglin Wang", "Xiaojin Gong", "Jiachen Li", "Genlin Ji" ]	Unsupervised visible-infrared person re-identification (USVI-ReID) aims to match individuals across visible and infrared cameras without relying on any annotation. Given the significant gap across visible and infrared modality, estimating reliable cross-modality association becomes a major challenge in USVI-ReID. Exist...	main	Computer Vision	10.1609/aaai.v40i12.37963	40	12	9975-9983	official	2512.07760	title_snapshot
10.1609/aaai.v40i11.37856	Causality Matters: How Temporal Information Emerges in Video Language Models	https://ojs.aaai.org/index.php/AAAI/article/view/37856	https://ojs.aaai.org/index.php/AAAI/article/download/37856/41818	[ "Yumeng Shi", "Quanyu Long", "Yin Wu", "Wenya Wang" ]	Video language models (VideoLMs) have made significant progress in multimodal understanding. However, temporal understanding, which involves identifying event order, duration, and relationships across time, still remains a core challenge. Prior works emphasize positional encodings (PEs) as a key mechanism for encoding ...	main	Computer Vision	10.1609/aaai.v40i11.37856	40	11	9006-9014	official	2508.11576	title_snapshot
10.1609/aaai.v40i11.37834	NeuS-QA: Grounding Long-Form Video Understanding in Temporal Logic and Neuro-Symbolic Reasoning	https://ojs.aaai.org/index.php/AAAI/article/view/37834	https://ojs.aaai.org/index.php/AAAI/article/download/37834/41796	[ "Sahil Shah", "S P Sharan", "Harsh Goel", "Minkyu Choi", "Mustafa Munir", "Manvik Pasula", "Radu Marculescu", "Sandeep Chinchali" ]	While vision-language models (VLMs) excel at tasks involving single images or short videos, they still struggle with Long Video Question Answering (LVQA) due to its demand for complex multi-step temporal reasoning. Vanilla approaches, which simply sample frames uniformly and feed them to a VLM along with the question, ...	main	Computer Vision	10.1609/aaai.v40i11.37834	40	11	8805-8813	official	2509.18041	title_snapshot
10.1609/aaai.v40i12.37984	Noisy Correspondence Learning with Modality Gap Direction Correction	https://ojs.aaai.org/index.php/AAAI/article/view/37984	https://ojs.aaai.org/index.php/AAAI/article/download/37984/41946	[ "Wuyuqing Wang", "Zeyuan Gu", "Erkun Yang" ]	Cross-modal retrieval is crucial for discovering latent correspondences across different modalities. However, existing methods typically assume that training data are well-aligned, an unrealistic assumption since real-world datasets inevitably contain noisy correspondences. Many current approaches attempt to handle noi...	main	Computer Vision	10.1609/aaai.v40i12.37984	40	12	10163-10171	official	null	null
10.1609/aaai.v40i11.37881	SwiftVideo: A Unified Framework for Few-Step Video Generation Through Trajectory-Distribution Alignment	https://ojs.aaai.org/index.php/AAAI/article/view/37881	https://ojs.aaai.org/index.php/AAAI/article/download/37881/41843	[ "Yanxiao Sun", "Jiafu Wu", "Yun Cao", "Chengming Xu", "Yabiao Wang", "Weijian Cao", "Donghao Luo", "Chengjie Wang", "Yanwei Fu" ]	Diffusion-based or flow-based models have achieved significant progress in video synthesis but require multiple iterative sampling steps, which incurs substantial computational overhead. While many distillation methods that are solely based on trajectory-preserving or distribution-matching have been developed to accele...	main	Computer Vision	10.1609/aaai.v40i11.37881	40	11	9233-9241	official	2508.06082	title_snapshot
10.1609/aaai.v40i11.37864	Sim4Seg: Boosting Multimodal Multi-disease Medical Diagnosis Segmentation with Region-Aware Vision-Language Similarity Masks	https://ojs.aaai.org/index.php/AAAI/article/view/37864	https://ojs.aaai.org/index.php/AAAI/article/download/37864/41826	[ "Lingran Song", "Yucheng Zhou", "Jianbing Shen" ]	Despite significant progress in pixel-level medical image analysis, existing medical image segmentation models rarely explore medical segmentation and diagnosis tasks jointly. However, it is crucial for patients that models can provide explainable diagnoses along with medical segmentation results. In this paper, we int...	main	Computer Vision	10.1609/aaai.v40i11.37864	40	11	9079-9087	official	2511.06665	title_snapshot
10.1609/aaai.v40i35.40172	A Phase Transition for Opinion Dynamics with Competing Biases	https://ojs.aaai.org/index.php/AAAI/article/view/40172	https://ojs.aaai.org/index.php/AAAI/article/download/40172/44133	[ "Federico Capannoli", "Emilio Cruciani", "Hlafo Alfie Mimun", "Matteo Quattropani" ]	We study the nonlinear evolution of binary opinions in a population of agents connected by a directed network, influenced by two competing forces. On the one hand agents are stubborn, i.e., have a tendency for one of the two opinions; on the other hand there is a disruptive bias that drives the agents toward the opposi...	main	Multiagent Systems	10.1609/aaai.v40i35.40172	40	35	29323-29331	official	2511.09434	title_snapshot
10.1609/aaai.v40i33.40066	MLLM Enriched Explainable Multiple Clustering	https://ojs.aaai.org/index.php/AAAI/article/view/40066	https://ojs.aaai.org/index.php/AAAI/article/download/40066/44027	[ "Shan Zhang", "Liangrui Ren", "Qiaoyu Tan", "Carlotta Domeniconi", "Wei Du", "Jun Wang", "Guoxian Yu" ]	Multiple clustering aims to uncover diverse latent structures within the data, enabling a more comprehensive understanding of complex datasets. However, existing approaches either heavily rely on user-supplied keywords or disregard user-interested clustering types, limiting the ability to discover the full range of exp...	main	Machine Learning	10.1609/aaai.v40i33.40066	40	33	28373-28381	official	null	null
10.1609/aaai.v40i33.40055	Breaking Down Market Barriers: Distilled Prompt-Tuning Approach for Cross-Market Recommendation	https://ojs.aaai.org/index.php/AAAI/article/view/40055	https://ojs.aaai.org/index.php/AAAI/article/download/40055/44016	[ "Leqi Zhang", "Wayne Lu", "Haiyang Zhang", "Elliott Wen", "Zhixuan Liang", "Jia Wang" ]	Cross-market recommendation (CMR) faces severe challenges from distribution shifts between data-rich source markets and sparse target markets. Existing methods rely on a pre-training and fine-tuning paradigm for knowledge transfer, yet suffer from two key limitations: i) the objective gap between pre-training and full-...	main	Machine Learning	10.1609/aaai.v40i33.40055	40	33	28274-28282	official	null	null
10.1609/aaai.v40i37.40422	KVmix: Gradient-Based Layer Importance-Aware Mixed-Precision Quantization for KV Cache	https://ojs.aaai.org/index.php/AAAI/article/view/40422	https://ojs.aaai.org/index.php/AAAI/article/download/40422/44383	[ "Fei Li", "Song Liu", "Weiguo Wu", "Shiqiang Nie", "Jinyu Wang" ]	The high memory demands of the Key-Value (KV) Cache during the inference of Large Language Models (LLMs) severely restrict their deployment in resource-constrained platforms. Quantization can effectively alleviate the memory pressure caused by KV Cache. However, existing methods either rely on static one-size-fits-all ...	main	Natural Language Processing	10.1609/aaai.v40i37.40422	40	37	31563-31572	official	2506.08018	title_snapshot
10.1609/aaai.v40i36.40321	GlitchCleaner: Lightweight Glitch Tokens Repairing by Lossless Gated LoRA in Large Language Models	https://ojs.aaai.org/index.php/AAAI/article/view/40321	https://ojs.aaai.org/index.php/AAAI/article/download/40321/44282	[ "Yibo Fan", "Jingru Li", "Huan Li" ]	Large language models (LLMs) have been increasingly applied across a wide range of domains. However, recent studies have identified the presence of certain glitch tokens in their vocabularies, which can trigger hallucinations and lead to unpredictable or even harmful outputs. While various methods have been proposed to...	main	Natural Language Processing	10.1609/aaai.v40i36.40321	40	36	30656-30664	official	null	null
10.1609/aaai.v40i26.39309	Forgetting by Pruning: Data Deletion in Join Cardinality Estimation	https://ojs.aaai.org/index.php/AAAI/article/view/39309	https://ojs.aaai.org/index.php/AAAI/article/download/39309/43270	[ "Chaowei He", "Yuanjun Liu", "Qingzhi Ma", "Shenyuan Ren", "Xizhao Luo", "Lei Zhao", "An Liu" ]	Machine unlearning in learned cardinality estimation (CE) systems presents unique challenges due to the complex distributional dependencies in multi-table relational data. Specifically, data deletion, a core component of machine unlearning, faces three critical challenges in learned CE models: attribute-level sensitivi...	main	Machine Learning	10.1609/aaai.v40i26.39309	40	26	21602-21609	official	2511.20293	title_snapshot
10.1609/aaai.v40i26.39314	Harnessing Textual Semantic Priors for Knowledge Transfer and Refinement in CLIP-Driven Continual Learning	https://ojs.aaai.org/index.php/AAAI/article/view/39314	https://ojs.aaai.org/index.php/AAAI/article/download/39314/43275	[ "Lingfeng He", "De Cheng", "Di Xu", "Huaijie Wang", "Nannan Wang" ]	Continual learning (CL) aims to equip models with the ability to learn from a stream of tasks without forgetting previous knowledge. With the progress of vision-language models like Contrastive Language-Image Pre-training (CLIP), their promise for CL has attracted increasing attention due to their strong generalizabili...	main	Machine Learning	10.1609/aaai.v40i26.39314	40	26	21645-21653	official	2508.01579	title_snapshot
10.1609/aaai.v40i26.39324	From Attribution to Action: Jointly ALIGNing Predictions and Explanations	https://ojs.aaai.org/index.php/AAAI/article/view/39324	https://ojs.aaai.org/index.php/AAAI/article/download/39324/43285	[ "Dongsheng Hong", "Chao Chen", "Yanhui Chen", "Shanshan Lin", "Zhihao Chen", "Xiangwen Liao" ]	Explanation-guided learning (EGL) has shown promise in aligning model predictions with interpretable reasoning, particularly in computer vision tasks. However, most approaches rely on external annotations or heuristic-based segmentation to supervise model explanations, which can be noisy, imprecise and difficult to sca...	main	Machine Learning	10.1609/aaai.v40i26.39324	40	26	21735-21742	official	2511.06944	title_snapshot
10.1609/aaai.v40i19.38629	Dual-Perspective Disentanglement: Learning Symmetric Group-Aware Representations for Cross-Domain Recommendation	https://ojs.aaai.org/index.php/AAAI/article/view/38629	https://ojs.aaai.org/index.php/AAAI/article/download/38629/42591	[ "Borui Wu", "Yuanbo Xu" ]	Cross-Domain Recommendation (CDR) transfers user preferences from a source domain to alleviate data sparsity in a target domain. While disentangling representations into domain-specific and shared components is a common method, existing methods overlook user preference heterogeneity and item appeal heterogeneity. To th...	main	Data Mining & Knowledge Management	10.1609/aaai.v40i19.38629	40	19	15959-15967	official	null	null
10.1609/aaai.v40i25.39243	MoETTA: Test-Time Adaptation Under Mixed Distribution Shifts with MoE-LayerNorm	https://ojs.aaai.org/index.php/AAAI/article/view/39243	https://ojs.aaai.org/index.php/AAAI/article/download/39243/43204	[ "Xiao Fan", "Jingyan Jiang", "Zhaoru Chen", "Fanding Huang", "Xiao Chen", "Qinting Jiang", "Bowen Zhang", "Xing Tang", "Zhi Wang" ]	Test-time adaptation (TTA) has proven effective in mitigating performance drops under single-domain distribution shifts by updating model parameters during inference. However, real-world deployments often involve mixed distribution shifts---where test samples are affected by diverse and potentially conflicting domain f...	main	Machine Learning	10.1609/aaai.v40i25.39243	40	25	21011-21019	official	2511.13760	title_snapshot
10.1609/aaai.v40i23.39000	PCoKG: Personality-aware Commonsense Reasoning with Debate	https://ojs.aaai.org/index.php/AAAI/article/view/39000	https://ojs.aaai.org/index.php/AAAI/article/download/39000/42962	[ "Weijie Li", "Zhongqing Wang", "Guodong Zhou" ]	Most commonsense reasoning models overlook the influence of personality traits, limiting their effectiveness in personalized systems such as dialogue generation. To address this limitation, we introduce the Personality-aware Commonsense Knowledge Graph (PCoKG), a structured dataset comprising 521,316 quadruples. We beg...	main	Knowledge Representation and Reasoning	10.1609/aaai.v40i23.39000	40	23	19251-19258	official	2601.06234	title_snapshot
10.1609/aaai.v40i23.38969	VSPO: Validating Semantic Pitfalls in Ontology via LLM-Based CQ Generation	https://ojs.aaai.org/index.php/AAAI/article/view/38969	https://ojs.aaai.org/index.php/AAAI/article/download/38969/42931	[ "Hyojun Choi", "Seokju Hwang", "Kyong-Ho Lee" ]	Competency Questions (CQs) play a crucial role in validating ontology design. While manually crafting CQs can be highly time-consuming and costly for ontology engineers, recent studies have explored the use of large language models (LLMs) to automate this process. However, prior approaches have largely evaluated genera...	main	Knowledge Representation and Reasoning	10.1609/aaai.v40i23.38969	40	23	18977-18984	official	2511.07991	title_snapshot
10.1609/aaai.v40i39.40575	Perturb Your Data: Paraphrase-Guided Training Data Watermarking	https://ojs.aaai.org/index.php/AAAI/article/view/40575	https://ojs.aaai.org/index.php/AAAI/article/download/40575/44536	[ "Pranav Shetty", "Mirazul Haque", "Petr Babkin", "Zhiqiang Ma", "Xiaomo Liu", "Manuela Veloso" ]	Training data detection is critical for enforcing copyright and data licensing, as Large Language Models (LLM) are trained on massive text corpora scraped from the internet. We present SPECTRA, a watermarking approach that makes training data reliably detectable even when it comprises less than 0.001% of the training c...	main	Natural Language Processing	10.1609/aaai.v40i39.40575	40	39	32938-32946	official	2512.17075	title_snapshot
10.1609/aaai.v40i24.39122	Scaling Law Analysis in Federated Learning: How to Select the Optimal Model Size?	https://ojs.aaai.org/index.php/AAAI/article/view/39122	https://ojs.aaai.org/index.php/AAAI/article/download/39122/43084	[ "Xuanyu Chen", "Nan Yang", "Shuai Wang", "Dong Yuan" ]	The recent success of large language models (LLMs) has sparked a growing interest in training large-scale models. As the model size continues to scale, concerns are growing about the depletion of high-quality, well-curated training data. This has led practitioners to explore training approaches like Federated Learning ...	main	Machine Learning	10.1609/aaai.v40i24.39122	40	24	20336-20344	official	2511.12188	title_snapshot
10.1609/aaai.v40i20.38769	Fairness in the Multi-Secretary Problem	https://ojs.aaai.org/index.php/AAAI/article/view/38769	https://ojs.aaai.org/index.php/AAAI/article/download/38769/42731	[ "Georgios Papasotiropoulos", "Zein Pishbin" ]	This paper bridges two perspectives: it studies the multi-secretary problem through the fairness lens of social choice, and examines multi-winner elections from the viewpoint of online decision making. After identifying the limitations of the prominent proportionality notion of Extended Justified Representation (EJR) i...	main	Game Theory and Economic Paradigms	10.1609/aaai.v40i20.38769	40	20	17188-17196	official	2511.23097	title_snapshot
10.1609/aaai.v40i30.39749	KSS-MoE: Knowledge Space Synergy Framework in Mixture of Experts for Continual Visual Instruction Tuning	https://ojs.aaai.org/index.php/AAAI/article/view/39749	https://ojs.aaai.org/index.php/AAAI/article/download/39749/43710	[ "Lingyun Song", "Ziyao Chen", "Kang Pan", "Xiaolin Han", "Xinbiao Gan", "Yudai Pan", "Xiaofan Sun", "Xiaoqi Wang", "Xuequn Shang" ]	Multimodal Large Language Models (MLLMs) employing the Mixture-of-Experts (MoE) structure exhibit encouraging results in visual language tasks. However, they struggle with catastrophic forgetting due to a lack of effective collaboration among experts and negative transfer across tasks. This happens because the router t...	main	Machine Learning	10.1609/aaai.v40i30.39749	40	30	25536-25544	official	null	null
10.1609/aaai.v40i31.39865	Cross-view Anchor Graph Learning and Factorization for Incomplete Multi-view Clustering	https://ojs.aaai.org/index.php/AAAI/article/view/39865	https://ojs.aaai.org/index.php/AAAI/article/download/39865/43826	[ "Xinxin Wang", "Yongshan Zhang", "Xiaochen Yuan", "Yicong Zhou" ]	Graph-based incomplete multi-view clustering algorithms have gathered much attention due to their impressive clustering performance. However, existing methods primarily leverage intra-view correlation from observed views, while ignoring the exploration of explicit compensation relationships between different views. Mor...	main	Machine Learning	10.1609/aaai.v40i31.39865	40	31	26570-26578	official	null	null
10.1609/aaai.v40i31.39877	DSAP: Enhancing Generalization in Goal-Conditioned Reinforcement Learning	https://ojs.aaai.org/index.php/AAAI/article/view/39877	https://ojs.aaai.org/index.php/AAAI/article/download/39877/43838	[ "Yiming Wang", "Kaiyan Zhao", "Ming Yang", "Yan Li", "Furui Liu", "Jiayu Chen", "Leong Hou U" ]	Goal-conditioned Reinforcement Learning (RL) is a promising direction for training agents capable of tackling a variety of tasks. However, generalizing to new goals in different environments remains a central challenge for goal-conditioned RL agents. Existing methods often rely on state abstraction, which involves lear...	main	Machine Learning	10.1609/aaai.v40i31.39877	40	31	26679-26687	official	null	null
10.1609/aaai.v40i31.39815	Reward Model Evaluation via Automatically-Ranked Policy Alignment	https://ojs.aaai.org/index.php/AAAI/article/view/39815	https://ojs.aaai.org/index.php/AAAI/article/download/39815/43776	[ "Aoran Wang", "Lei Ou", "Yang Yu", "Zongzhang Zhang" ]	Evaluating reward models is a fundamental challenge in Reinforcement Learning (RL), particularly in settings where the reward model is learned or manually designed. The standard paradigm for Reward Model Evaluation (RME) involves training an optimal policy via RL on the given reward model and assessing model quality th...	main	Machine Learning	10.1609/aaai.v40i31.39815	40	31	26124-26132	official	null	null
10.1609/aaai.v40i43.40931	Qualitative Analysis of ω-Regular Objectives on Robust MDPs	https://ojs.aaai.org/index.php/AAAI/article/view/40931	https://ojs.aaai.org/index.php/AAAI/article/download/40931/44892	[ "Ali Asadi", "Krishnendu Chatterjee", "Ehsan Kafshdar Goharshady", "Mehrdad Karrabi", "Ali Shafiee" ]	Robust Markov Decision Processes (RMDPs) generalize classical MDPs that consider uncertainties in transition probabilities by defining a set of possible transition functions. An objective is a set of runs (or infinite trajectories) of the RMDP, and the value for an objective is the maximal probability that the agent ca...	main	null	10.1609/aaai.v40i43.40931	40	43	36137-36145	official	2505.04539	title_snapshot
10.1609/aaai.v40i43.41022	Improved Streaming Algorithm for Fair k-Center Clustering	https://ojs.aaai.org/index.php/AAAI/article/view/41022	https://ojs.aaai.org/index.php/AAAI/article/download/41022/44983	[ "Longkun Guo", "Zeyu Lin", "Chaoqi Jia", "Chao Chen" ]	Many real-world applications call for incorporating fairness constraints into the k-center clustering problem, where the dataset is partitioned into m demographic groups, each with a specified upper bound on the number of centers to ensure fairness. Focusing on big data scenarios, this paper addresses the problem in a ...	main	null	10.1609/aaai.v40i43.41022	40	43	36946-36954	official	2510.05937	title_snapshot
10.1609/aaai.v40i43.41037	CRAF: A Clinical Reasoning-Adaptive Framework via Reinforcement Learning for Similar Case Retrieval	https://ojs.aaai.org/index.php/AAAI/article/view/41037	https://ojs.aaai.org/index.php/AAAI/article/download/41037/44998	[ "Jie Lin", "Lei Jiang", "Zongyi Chen", "Liansheng Wang" ]	With the advancement of information retrieval (IR) technologies toward deep semantic understanding, reasoning-based methods—featuring explicit chain-of-thought generation—have demonstrated significant advantages in multi-hop and causal reasoning tasks. However, in complex clinical case retrieval scenarios, implicit rea...	main	null	10.1609/aaai.v40i43.41037	40	43	37081-37089	official	null	null
10.1609/aaai.v40i43.40984	Universal Learning of Stochastic Dynamics for Exact Belief Propagation Using Bernstein Normalizing Flows	https://ojs.aaai.org/index.php/AAAI/article/view/40984	https://ojs.aaai.org/index.php/AAAI/article/download/40984/44945	[ "Peter Amorese", "Morteza Lahijanian" ]	Predicting the distribution of future states in a stochastic system, known as belief propagation, is fundamental to reasoning under uncertainty. However, nonlinear dynamics often make analytical belief propagation intractable, requiring approximate methods. When the system model is unknown and must be learned from data...	main	null	10.1609/aaai.v40i43.40984	40	43	36610-36617	official	2509.15533	title_snapshot
10.1609/aaai.v40i43.41046	Bidirectional Bounded-Suboptimal Heuristic Search with Consistent Heuristics	https://ojs.aaai.org/index.php/AAAI/article/view/41046	https://ojs.aaai.org/index.php/AAAI/article/download/41046/45007	[ "Shahaf S. Shperberg", "Natalie Morad", "Lior Siag", "Ariel Felner", "Dor Atzmon" ]	Recent advancements in bidirectional heuristic search have yielded significant theoretical insights and novel algorithms. While most previous work has concentrated on optimal search methods, this paper focuses on bounded-suboptimal bidirectional search, where a bound on the suboptimality of the solution cost is specifi...	main	null	10.1609/aaai.v40i43.41046	40	43	37161-37169	official	2511.10272	title_snapshot
10.1609/aaai.v40i43.40956	Incremental Data-Driven Policy Synthesis via Game Abstractions	https://ojs.aaai.org/index.php/AAAI/article/view/40956	https://ojs.aaai.org/index.php/AAAI/article/download/40956/44917	[ "Irmak Sağlam", "Mahdi Nazeri", "Alessandro Abate", "Sadegh Soudjani", "Anne-Kathrin Schmuck" ]	We address the synthesis of control policies for unknown discrete-time stochastic dynamical systems to satisfy temporal logic objectives. We present a data-driven, abstraction-based control framework that integrates online learning with novel incremental game-solving. Under appropriate continuity assumptions, our metho...	main	null	10.1609/aaai.v40i43.40956	40	43	36360-36368	official	2511.11545	title_snapshot
10.1609/aaai.v40i43.40961	Inapproximability of STRIPS Planning	https://ojs.aaai.org/index.php/AAAI/article/view/40961	https://ojs.aaai.org/index.php/AAAI/article/download/40961/44922	[ "Xing Tan", "Alban Grastien" ]	Automated planning involves finding a sequence of actions that changes the world from an initial state to a final state with goals satisfied. The general problem is PSPACE-hard. Nevertheless, many restricted variants are NP-complete or even in P. Existing complexity work focuses mostly on plan existence, or plan with m...	main	null	10.1609/aaai.v40i43.40961	40	43	36403-36411	official	null	null
10.1609/aaai.v40i42.40894	Enhancing All-to-X Backdoor Attacks with Optimized Target Class Mapping	https://ojs.aaai.org/index.php/AAAI/article/view/40894	https://ojs.aaai.org/index.php/AAAI/article/download/40894/44855	[ "Lei Wang", "Yulong Tian", "Hao Han", "Fengyuan Xu" ]	Backdoor attacks pose severe threats to machine learning systems, prompting extensive research in this area. However, most existing work focuses on single-target All-to-One (A2O) attacks, overlooking the more complex All-to-X (A2X) attacks with multiple target classes, which are often assumed to have low attack success...	main	Philosophy and Ethics of AI	10.1609/aaai.v40i42.40894	40	42	35802-35810	official	2511.13356	title_snapshot
10.1609/aaai.v40i42.40928	DeformTrace: A Deformable State Space Model with Relay Tokens for Temporal Forgery Localization	https://ojs.aaai.org/index.php/AAAI/article/view/40928	https://ojs.aaai.org/index.php/AAAI/article/download/40928/44889	[ "Xiaodong Zhu", "Suting Wang", "Yuanming Zheng", "Junqi Yang", "Yangxu Liao", "Yuhong Yang", "Weiping Tu", "Zhongyuan Wang" ]	Temporal Forgery Localization (TFL) aims to precisely identify manipulated segments in video and audio, offering strong interpretability for security and forensics. While recent State Space Models (SSMs) show promise in precise temporal reasoning, their use in TFL is hindered by ambiguous boundaries, sparse forgeries, ...	main	Philosophy and Ethics of AI	10.1609/aaai.v40i42.40928	40	42	36110-36118	official	2603.04882	title_snapshot
10.1609/aaai.v40i41.40821	Note2Chat: Improving LLMs for Multi-Turn Clinical History Taking Using Medical Notes	https://ojs.aaai.org/index.php/AAAI/article/view/40821	https://ojs.aaai.org/index.php/AAAI/article/download/40821/44782	[ "Yang Zhou", "Zhenting Sheng", "Mingrui Tan", "Yuting Song", "Jun Zhou", "Yu Heng Kwan", "Lian Leng Low", "Yang Bai", "Yong Liu" ]	Effective clinical history taking is a foundational yet underexplored component of clinical reasoning. While large language models (LLMs) have shown promise on static benchmarks, they often fall short in dynamic, multi-turn diagnostic settings that require iterative questioning and hypothesis refinement. To address thi...	main	Natural Language Processing	10.1609/aaai.v40i41.40821	40	41	35149-35157	official	2601.21551	title_snapshot
10.1609/aaai.v40i42.40850	Stability-Aware Reinforcement Learning for Robust Class Integration Test Order Generation	https://ojs.aaai.org/index.php/AAAI/article/view/40850	https://ojs.aaai.org/index.php/AAAI/article/download/40850/44811	[ "Yanru Ding", "Yanmei Zhang", "Guan Yuan", "Shujuan Jiang", "Wei Dai", "Luciano Baresi" ]	Generating a class integration test order (CITO) is essential to reduce the overhead of test stub construction (the primary cost in integration testing) and to ensure system reliability in complex software systems. Although reinforcement learning (RL) has shown promise in automating CITO generation, existing methods su...	main	Philosophy and Ethics of AI	10.1609/aaai.v40i42.40850	40	42	35411-35418	official	null	null
10.1609/aaai.v40i40.40723	Decoupling Knowledge and Reasoning in LLMs: An Exploration Using Cognitive Dual-System Theory	https://ojs.aaai.org/index.php/AAAI/article/view/40723	https://ojs.aaai.org/index.php/AAAI/article/download/40723/44684	[ "Mutian Yang", "Jiandong Gao", "Ji Wu" ]	While large language models (LLMs) leverage both knowledge and reasoning during inference, the capacity to distinguish between them plays a pivotal role in model analysis, interpretability, and development. Inspired by dual-system cognitive theory, we propose a cognition attribution framework to decouple the contributi...	main	Natural Language Processing	10.1609/aaai.v40i40.40723	40	40	34268-34276	official	2507.18178	title_snapshot
10.1609/aaai.v40i37.40438	CoFact: Dynamic Coordination of Attention Heads for Improving Factual Consistency in LLMs	https://ojs.aaai.org/index.php/AAAI/article/view/40438	https://ojs.aaai.org/index.php/AAAI/article/download/40438/44399	[ "Shike Li", "Xiaokai Wang", "Xiaofeng Liu", "Xin Tong", "Hu Zhang" ]	Large language models (LLMs) frequently generate fluent yet factually inaccurate content, a phenomenon known as hallucination. Recent inference-time approaches aim to improve truthfulness by steering model activations toward semantically meaningful directions. While effective to some extent, these methods typically pro...	main	Natural Language Processing	10.1609/aaai.v40i37.40438	40	37	31708-31716	official	null	null
10.1609/aaai.v40i39.40603	RAG-R1:Incentivizing the Search and Reasoning Capabilities of LLMs Through Multi-Query Parallelism	https://ojs.aaai.org/index.php/AAAI/article/view/40603	https://ojs.aaai.org/index.php/AAAI/article/download/40603/44564	[ "Zhiwen Tan", "Jiaming Huang", "Qintong Wu", "Hongxuan Zhang", "Chenyi Zhuang", "Jinjie Gu" ]	Large Language Models (LLMs), despite their remarkable capabilities, are prone to generating hallucinated or outdated content due to their static internal knowledge. While Retrieval-Augmented Generation (RAG) integrated with Reinforcement Learning (RL) offers a solution, these methods are fundamentally constrained by a...	main	Natural Language Processing	10.1609/aaai.v40i39.40603	40	39	33187-33195	official	2507.02962	title_snapshot
10.1609/aaai.v40i38.40471	Towards Better Code Understanding in Decoder-Only Models with Contrastive Learning	https://ojs.aaai.org/index.php/AAAI/article/view/40471	https://ojs.aaai.org/index.php/AAAI/article/download/40471/44432	[ "Jiayi Lin", "Yanlin Wang", "Yibiao Yang", "Lei Zhang", "Yutao Xie" ]	Recent advances in large-scale code generation models have led to remarkable progress in producing high-quality code. These models are trained in a self-supervised manner on extensive unlabeled code corpora using a decoder-only architecture. However, despite their generative strength, decoder-only models often exhibit ...	main	Natural Language Processing	10.1609/aaai.v40i38.40471	40	38	32006-32014	official	2406.12326	title_snapshot
10.1609/aaai.v40i33.40072	Unifying Channel Independence and Mixing: Multi-Scale Patch Recursion for Global–Local Representation Synergy in Multivariate Time Series Forecasting	https://ojs.aaai.org/index.php/AAAI/article/view/40072	https://ojs.aaai.org/index.php/AAAI/article/download/40072/44033	[ "Wenhao Zhang", "Chun Zhang", "Wei Bai", "Ning Zhang", "Changxia Gao", "Yuxin Jia", "Chenhao Shi", "Shaoxiong Pang" ]	Multivariate time series forecasting underpins applications in finance, meteorology, and industrial operations. Yet two persistent hurdles remain: (i) models typically choose between Channel–Independent (CI) and Channel–Mixed (CM) formulations—each with distinct strengths—leading to large performance variance across da...	main	Machine Learning	10.1609/aaai.v40i33.40072	40	33	28427-28436	official	null	null
10.1609/aaai.v40i34.40085	Bi-Spectrum Distillation: Addressing Spectral Mismatch in ANN-SNN Knowledge Transfer	https://ojs.aaai.org/index.php/AAAI/article/view/40085	https://ojs.aaai.org/index.php/AAAI/article/download/40085/44046	[ "Yuxuan Zhang", "Yuhang Sun", "Wen Yao", "Yue Deng", "Hongjue Li" ]	Knowledge distillation from Artificial Neural Networks (ANNs) to Spiking Neural Networks (SNNs) is a prominent training paradigm. However, its efficacy is fundamentally limited by a spectral mismatch: SNNs, with their intrinsic low-pass filtering characteristics, struggle to learn high-frequency details from their ANN ...	main	Machine Learning	10.1609/aaai.v40i34.40085	40	34	28546-28554	official	null	null
10.1609/aaai.v40i31.39814	Explore and Establish Synergistic Effects Between Weight Pruning and Coreset Selection in Neural Network Training	https://ojs.aaai.org/index.php/AAAI/article/view/39814	https://ojs.aaai.org/index.php/AAAI/article/download/39814/43775	[ "Weilin Wan", "Fan Yi", "Weizhong Zhang", "Quan Zhou", "Cheng Jin" ]	Modern deep neural networks rely heavily on massive model weights and training samples, incurring substantial computational costs. Weight pruning and coreset selection are two emerging paradigms proposed to improve computational efficiency. In this paper, we first explore the interplay between redundant weights and tra...	main	Machine Learning	10.1609/aaai.v40i31.39814	40	31	26115-26123	official	2511.09901	title_snapshot
10.1609/aaai.v40i31.39797	Efficiently Enhancing Long-term Series Forecasting via Adaptive Lookback with Wavelets	https://ojs.aaai.org/index.php/AAAI/article/view/39797	https://ojs.aaai.org/index.php/AAAI/article/download/39797/43758	[ "Suxin Tong", "Jingling Yuan" ]	Long-term series forecasting leverages historical observations to predict extended future sequences and plays a crucial role across various domains. However, conventional models relying on fixed-length lookback windows struggle with the inherent dynamic dependencies and multi-scale characteristics of time series data. ...	main	Machine Learning	10.1609/aaai.v40i31.39797	40	31	25966-25974	official	null	null
10.1609/aaai.v40i32.39901	TawPipe: Topology-Aware Weight Pipeline Parallelism for Accelerating Long-Context Large Models Training	https://ojs.aaai.org/index.php/AAAI/article/view/39901	https://ojs.aaai.org/index.php/AAAI/article/download/39901/43862	[ "Houming Wu", "Ling Chen" ]	Training large language models (LLMs) is fundamentally constrained by limited device memory and costly inter-device communication. Although pipeline parallelism alleviates memory pressure by partitioning models across devices, it incurs activation communication overhead that scales linearly with sequence length, limiti...	main	Machine Learning	10.1609/aaai.v40i32.39901	40	32	26894-26902	official	2511.09741	title_snapshot
10.1609/aaai.v40i33.39988	Self-Indexing KVCache: Predicting Sparse Attention from Compressed Keys	https://ojs.aaai.org/index.php/AAAI/article/view/39988	https://ojs.aaai.org/index.php/AAAI/article/download/39988/43949	[ "Xu Yang", "Jiapeng Zhang", "Dongyang Zhao", "Guo Chen", "Zhuo Tang" ]	The KV cache in self-attention has emerged as a major bottleneck in long-context and large-batch inference for LLMs. Existing approaches often treat sparsity prediction and compression as separate modules—relying on auxiliary index structures to select relevant tokens, and on complex quantization schemes to reduce memo...	main	Machine Learning	10.1609/aaai.v40i33.39988	40	33	27675-27683	official	2603.14224	title_snapshot
10.1609/aaai.v40i33.40052	GAHMN: A Generative Approach for High-Dimensional Mediation Analysis	https://ojs.aaai.org/index.php/AAAI/article/view/40052	https://ojs.aaai.org/index.php/AAAI/article/download/40052/44013	[ "Jiaming Zhang", "Yiqi Lin", "Rou Zhang", "Xinyuan Song", "Hanwen Ning" ]	High-dimensional mediation analysis (HMA) seeks to uncover complex causal mechanisms involving numerous mediators and plays a crucial role in scientific and social sciences. In this work, we introduce the Generative Adversarial High-dimensional Mediation Network (GAHMN), a novel, scalable structured generative framewor...	main	Machine Learning	10.1609/aaai.v40i33.40052	40	33	28247-28255	official	null	null
10.1609/aaai.v40i33.40025	Demystifying GNN-to-MLP Knowledge Transfer: Theoretical Grounding and Dual-Stream Distillation Method	https://ojs.aaai.org/index.php/AAAI/article/view/40025	https://ojs.aaai.org/index.php/AAAI/article/download/40025/43986	[ "Zhiyuan Yu", "Mingkai Lin", "Wenzhong Li", "Zhangyue Yin", "Shijian Xiao", "Sanglu Lu" ]	Graph Neural Networks (GNNs) have shown remarkable effectiveness across various applications, but their computational complexity poses significant scalability challenges. To this end, GNN-to-MLP Knowledge Distillation (KD) methods transfer relational inductive biases from GNNs to MLPs, equipping MLPs with graph-aware c...	main	Machine Learning	10.1609/aaai.v40i33.40025	40	33	28005-28013	official	null	null
10.1609/aaai.v40i21.38831	AutoGameUI: Constructing High-Fidelity GameUI via Multimodal Correspondence Matching	https://ojs.aaai.org/index.php/AAAI/article/view/38831	https://ojs.aaai.org/index.php/AAAI/article/download/38831/42793	[ "Zhongliang Tang", "Qingrong Cheng", "Mengchen Tan", "Yongxiang Zhang", "Fei Xia" ]	Game UI development is essential to the game industry. However, the traditional workflow requires substantial manual effort to integrate pairwise UI and UX designs into a cohesive game user interface (GameUI). The inconsistency between the aesthetic UI design and the functional UX design typically results in mismatches...	main	Humans and AI	10.1609/aaai.v40i21.38831	40	21	17742-17750	official	2411.03709	title_snapshot
10.1609/aaai.v40i31.39853	Soft Conflict-Resolution Decision Transformer for Offline Multi-Task Reinforcement Learning	https://ojs.aaai.org/index.php/AAAI/article/view/39853	https://ojs.aaai.org/index.php/AAAI/article/download/39853/43814	[ "Shudong Wang", "Xinfei Wang", "Chenhao Zhang", "Shanchen Pang", "Haiyuan Gui", "Wenhao Ji", "Xiaojian Liao" ]	Multi-task reinforcement learning (MTRL) seeks to learn a unified policy for diverse tasks, but often suffers from gradient conflicts across tasks. Existing masking-based methods attempt to mitigate such conflicts by assigning task-specific parameter masks. However, our empirical study shows that coarse-grained binary ...	main	Machine Learning	10.1609/aaai.v40i31.39853	40	31	26462-26470	official	2511.13133	title_snapshot
10.1609/aaai.v40i4.37259	Reconstruction Using the Invisible: Intuition from NIR and Metadata for Enhanced 3D Gaussian Splatting	https://ojs.aaai.org/index.php/AAAI/article/view/37259	https://ojs.aaai.org/index.php/AAAI/article/download/37259/41221	[ "Gyusam Chang", "Tuan-Anh Vu", "Vivek Alumootil", "Harris Song", "Deanna Pham", "Sangpil Kim", "M. Khalid Jawed" ]	While 3D Gaussian Splatting (3DGS) has rapidly advanced, its application in agriculture remains underexplored. Agricultural scenes pose unique challenges for 3D reconstruction methods, notably uneven illumination, occlusions, and limited perspectives. To address these limitations, we introduce NTRPlant, a novel multimo...	main	Computer Vision	10.1609/aaai.v40i4.37259	40	4	2707-2715	official	2508.14443	title_snapshot
10.1609/aaai.v40i19.38673	Evidence-aware Integration and Domain Identification of Spatial Transcriptomics Data	https://ojs.aaai.org/index.php/AAAI/article/view/38673	https://ojs.aaai.org/index.php/AAAI/article/download/38673/42635	[ "Wei Zhang", "Siyu Yi", "Lezhi Chen", "Yifan Wang", "Ziyue Qiao", "Yongdao Zhou", "Wei Ju" ]	Spatial transcriptomics (ST) enables joint profiling of gene expression and spatial positions, thereby revealing spatially resolved biological functions. However, many existing ST analysis methods often fail to explicitly quantify the belief and uncertainty in decisions caused by noisy ST data, making it difficult to h...	main	Data Mining & Knowledge Management	10.1609/aaai.v40i19.38673	40	19	16352-16360	official	null	null
10.1609/aaai.v40i19.38690	IdeFN: Identifying Unclicked Space False Negatives via Relaxed Partial Optimal Transport for Conversion Rate Prediction	https://ojs.aaai.org/index.php/AAAI/article/view/38690	https://ojs.aaai.org/index.php/AAAI/article/download/38690/42652	[ "Weiyi Zhong", "Weiming Liu", "Lianyong Qi", "Xiaoran Zhao", "Xiaolong Xu", "Haolong Xiang", "Yang Cao", "Shichao Pei", "Qiang Ni" ]	Accurate conversion rate (CVR) prediction is critical for recommender systems to capture user conversion intent and increase platform revenues. Traditional CVR models commonly suffer from sample selection bias (SSB) and data sparsity (DS), which has led to the adoption of click-through & conversion rate (CTCVR) multi-t...	main	Data Mining & Knowledge Management	10.1609/aaai.v40i19.38690	40	19	16504-16512	official	null	null
10.1609/aaai.v40i19.38658	Self-Supervised Cross-City Trajectory Representation Learning Based on Meta-Learning	https://ojs.aaai.org/index.php/AAAI/article/view/38658	https://ojs.aaai.org/index.php/AAAI/article/download/38658/42620	[ "Yanwei Yu", "Hong Xia", "Shaoxuan Gu", "Xingyu Zhao", "Dongliang Chen", "Yuan Cao" ]	Trajectory representation learning transforms complex spatio-temporal features of trajectories into dense, low-dimensional embeddings, enabling applications in intelligent transportation systems. With advances in this field and the availability of large-scale traffic data, intelligent urban systems have been widely dep...	main	Data Mining & Knowledge Management	10.1609/aaai.v40i19.38658	40	19	16217-16225	official	null	null
10.1609/aaai.v40i17.38483	Delayed Feedback Modeling with Influence Functions	https://ojs.aaai.org/index.php/AAAI/article/view/38483	https://ojs.aaai.org/index.php/AAAI/article/download/38483/42445	[ "Chenlu Ding", "Jiancan Wu", "Yancheng Yuan", "Cunchun Li", "Xiang Wang", "Dingxian Wang", "Frank Yang", "Andrew Rabinovich" ]	In online advertising under the cost-per-conversion (CPA) model, accurate conversion rate (CVR) prediction is crucial. A major challenge is delayed feedback, where conversions may occur long after user interactions, leading to incomplete recent data and biased model training. Existing solutions partially mitigate this ...	main	null	10.1609/aaai.v40i17.38483	40	17	14648-14656	official	2502.01669	title_snapshot
10.1609/aaai.v40i17.38436	Preference Elicitation for Step-Wise Explanations in Logic Puzzles	https://ojs.aaai.org/index.php/AAAI/article/view/38436	https://ojs.aaai.org/index.php/AAAI/article/download/38436/42398	[ "Marco Foschini", "Marianne Defresne", "Emilio Gamba", "Bart Bogaerts", "Tias Guns" ]	Step-wise explanations can explain logic puzzles and other satisfaction problems by showing how to derive decisions step by step. Each step consists of a set of constraints that derive an assignment to one or more decision variables. However, many candidate explanation steps exist, with different sets of constraints an...	main	null	10.1609/aaai.v40i17.38436	40	17	14225-14233	official	2511.10436	title_snapshot
10.1609/aaai.v40i2.37091	S^2-KD: Semantic-Spectral Knowledge Distillation Spatiotemporal Forecasting	https://ojs.aaai.org/index.php/AAAI/article/view/37091	https://ojs.aaai.org/index.php/AAAI/article/download/37091/41053	[ "Wenshuo Wang", "Yaomin Shen", "Yingjie Tan", "Yihao Chen" ]	Spatiotemporal forecasting often relies on computationally intensive models to capture complex dynamics. Knowledge distillation (KD) has emerged as a key technique for creating lightweight student models, with recent advances like frequency-aware KD successfully preserving spectral properties (i.e., high-frequency deta...	main	Application Domains	10.1609/aaai.v40i2.37091	40	2	1195-1203	official	2512.00366	title_snapshot
10.1609/aaai.v40i5.37380	BCE3S: Binary Cross-Entropy Based Tripartite Synergistic Learning for Long-Tailed Recognition	https://ojs.aaai.org/index.php/AAAI/article/view/37380	https://ojs.aaai.org/index.php/AAAI/article/download/37380/41342	[ "Weijia Fan", "Qiufu Li", "Jiajun Wen", "Xiaoyang Peng" ]	For long-tailed recognition (LTR) tasks, high intra-class compactness and inter-class separability in both head and tail classes, as well as balanced separability among all the classifier vectors, are preferred. The existing LTR methods based on cross-entropy (CE) loss not only struggle to learn features with desirable...	main	Computer Vision	10.1609/aaai.v40i5.37380	40	5	3795-3803	official	2511.14097	title_snapshot
10.1609/aaai.v40i13.38036	HiFusion: Hierarchical Intra-Spot Alignment and Regional Context Fusion for Spatial Gene Expression Prediction from Histopathology	https://ojs.aaai.org/index.php/AAAI/article/view/38036	https://ojs.aaai.org/index.php/AAAI/article/download/38036/41998	[ "Ziqiao Weng", "Yaoyu Fang", "Jiahe Qian", "Xinkun Wang", "Lee A D Cooper", "Weidong Cai", "Bo Zhou" ]	Spatial transcriptomics (ST) bridges gene expression and tissue morphology but faces clinical adoption barriers due to technical complexity and prohibitive costs. While computational methods predict gene expression from H&E-stained whole-slide images (WSIs), existing approaches often fail to capture the intricate biolo...	main	Computer Vision	10.1609/aaai.v40i13.38036	40	13	10630-10637	official	2511.12969	title_snapshot
10.1609/aaai.v40i21.38851	Plug-and-Play Clarifier: A Zero-Shot Multimodal Framework for Egocentric Intent Disambiguation	https://ojs.aaai.org/index.php/AAAI/article/view/38851	https://ojs.aaai.org/index.php/AAAI/article/download/38851/42813	[ "Sicheng Yang", "Yukai Huang", "Weitong Cai", "Shitong Sun", "You He", "Jiankang Deng", "Hang Zhang", "Jifei Song", "Zhensong Zhang" ]	The performance of egocentric AI agents is fundamentally limited by multimodal intent ambiguity. This challenge arises from a combination of underspecified language, imperfect visual data, and deictic gestures, which frequently leads to task failure. Existing monolithic Vision-Language Models (VLMs) struggle to resolve...	main	Humans and AI	10.1609/aaai.v40i21.38851	40	21	17921-17929	official	2511.08971	title_snapshot
10.1609/aaai.v40i38.40454	Mitigating Hallucinations in Large Language Models via Causal Reasoning	https://ojs.aaai.org/index.php/AAAI/article/view/40454	https://ojs.aaai.org/index.php/AAAI/article/download/40454/44415	[ "Yuangang Li", "Yiqing Shen", "Yi Nian", "Jiechao Gao", "Ziyi Wang", "Chenxiao Yu", "Li Li", "Jie Wang", "Xiyang Hu", "Yue Zhao" ]	Large language models (LLMs) exhibit logically inconsistent hallucinations that appear coherent yet violate reasoning principles, with recent research suggesting an inverse relationship between causal reasoning capabilities and such hallucinations. However, existing reasoning approaches in LLMs, such as Chain-of-Though...	main	Natural Language Processing	10.1609/aaai.v40i38.40454	40	38	31852-31860	official	2508.12495	title_snapshot
10.1609/aaai.v40i8.37517	Versatile Vision-Language Model for 3D Computed Tomography	https://ojs.aaai.org/index.php/AAAI/article/view/37517	https://ojs.aaai.org/index.php/AAAI/article/download/37517/41479	[ "Jiayu Lei", "Ziqing Fan", "Yanyong Zhang", "Weidi Xie", "Ya Zhang", "Yanfeng Wang" ]	Representation learning serves as a foundational component of medical vision-language models (MVLMs), enabling cross-modal alignment, semantic consistency, and enhanced generalization capabilities for downstream tasks. As generalist models rapidly evolve, there is a pressing need to unify diverse downstream tasks, such...	main	Computer Vision	10.1609/aaai.v40i8.37517	40	8	5945-5954	official	null	null
10.1609/aaai.v40i19.38688	From Semantics to Spectrum: A New Lens on Graph Augmentation Strategy	https://ojs.aaai.org/index.php/AAAI/article/view/38688	https://ojs.aaai.org/index.php/AAAI/article/download/38688/42650	[ "Xiangping Zheng", "Xiuxin Hao", "Bo Wu", "Wei Li", "Bin Ren", "Bin Tang", "Yuhui Guo", "Xun Liang", "Zhiwen Yu" ]	Graph augmentation is a cornerstone of effective graph contrastive learning, yet existing methods often rely on random designed perturbations, which may distort latent semantics and impair representation quality. In this work, we argue that semantic consistency can be effectively approximated by low-frequency component...	main	Data Mining & Knowledge Management	10.1609/aaai.v40i19.38688	40	19	16486-16494	official	null	null
10.1609/aaai.v40i28.39507	ReTrack: Evidence-Driven Dual-Stream Directional Anchor Calibration Network for Composed Video Retrieval	https://ojs.aaai.org/index.php/AAAI/article/view/39507	https://ojs.aaai.org/index.php/AAAI/article/download/39507/43468	[ "Zixu Li", "Yupeng Hu", "Zhiwei Chen", "Qinlei Huang", "Guozhi Qiu", "Zhiheng Fu", "Meng Liu" ]	With the rapid growth of video data, Composed Video Retrieval (CVR) has emerged as a novel paradigm in video retrieval and is receiving increasing attention from researchers. Unlike unimodal video retrieval methods, the CVR task takes a multi-modal query consisting of a reference video and a piece of modification text ...	main	Machine Learning	10.1609/aaai.v40i28.39507	40	28	23373-23381	official	2604.17898	title_snapshot
10.1609/aaai.v40i17.38494	Towards LLM-Empowered Knowledge Tracing via LLM-Student Hierarchical Behavior Alignment in Hyperbolic Space	https://ojs.aaai.org/index.php/AAAI/article/view/38494	https://ojs.aaai.org/index.php/AAAI/article/download/38494/42456	[ "Xingcheng Fu", "Shengpeng Wang", "Yisen Gao", "Xianxian Li", "Chunpei Li", "Qingyun Sun", "Dongran Yu" ]	Knowledge Tracing (KT) diagnoses students’ concept mas- tery through continuous learning state monitoring in education. Existing methods primarily focus on studying behavioral sequences based on ID or textual information. While existing methods rely on ID-based sequences or shallow textual features, they often fail to ...	main	null	10.1609/aaai.v40i17.38494	40	17	14747-14755	official	2602.22879	title_snapshot
10.1609/aaai.v40i12.37920	Mass Concept Erasure in Diffusion Models with Concept Hierarchy	https://ojs.aaai.org/index.php/AAAI/article/view/37920	https://ojs.aaai.org/index.php/AAAI/article/download/37920/41882	[ "Jiahang Tu", "Ye Li", "Yiming Wu", "Hanbin Zhao", "Chao Zhang", "Hui Qian" ]	The success of diffusion models has raised concerns about the generation of unsafe or harmful content, prompting concept erasure approaches that fine-tune modules to suppress specific concepts while preserving general generative capabilities. However, as the number of erased concepts grows, these methods often become i...	main	Computer Vision	10.1609/aaai.v40i12.37920	40	12	9585-9593	official	2601.03305	title_snapshot
10.1609/aaai.v40i18.38528	SGP4SR: Separated-Modality Guided User Preference Learning for Multimodal Sequential Recommendation	https://ojs.aaai.org/index.php/AAAI/article/view/38528	https://ojs.aaai.org/index.php/AAAI/article/download/38528/42490	[ "Changhong Li", "Zhiqiang Guo", "Guohui Li", "Zhong Yang", "Chuhang Hong" ]	With the booming development of multimodal data (e.g., image, text) on internet platforms, multimodal sequential recommendation methods continue to emerge. Most existing methods incorporate item modal features as auxiliary information, typically concatenating them to learn unified user representations. However, these m...	main	Data Mining & Knowledge Management	10.1609/aaai.v40i18.38528	40	18	15054-15062	official	null	null

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Accepted papers for AAAI (AAAI Conference on Artificial Intelligence), one dataset per year. • 10 items • Updated 2 days ago