GenAI4ELab/papercli-papers huggingface dataset:
CIP-Net: Continual Interpretable Prototype-based Network
Authors: Federico Di Valerio; Michela Proietti; Alessio Ragno; Roberto Capobianco
Venue: AAAI 2026 (main)

Abstract: Continual learning constrains models to learn new tasks over time without forgetting what they have already learned. A key challenge in this setting is catastrophic forgetting, where learning new information causes the model to lose its performance on previous tasks. Recently, explainable AI has been proposed as a promising way to better understand and reduce forgetting. In particular, self-explainable models are useful because they generate explanations during prediction, which can help preserve knowledge. However, most existing explainable approaches use post-hoc explanations or require additional memory for each new task, resulting in limited scalability. In this work, we introduce CIP-Net, an exemplar-free self-explainable prototype-based model designed for continual learning. CIP-Net avoids storing past examples and maintains a simple architecture, while still providing useful explanations and strong performance. We demonstrate that CIP-Net achieves state-of-the-art performances compared to previous exemplar-free and self-explainable methods in both task- and class-incremental settings, while bearing significantly lower memory-related overhead. This makes it a practical and interpretable solution for continual learning.
SimDiff: Simpler Yet Better Diffusion Model for Time Series Point Forecasting
Authors: Hang Ding; Xue Wang; Tian Zhou; Tao Yao
Venue: AAAI 2026 (main)

Abstract: Diffusion models have recently shown promise in time series forecasting, particularly for probabilistic predictions. However, they often fail to achieve state-of-the-art point estimation performance compared to regression-based methods. This limitation stems from difficulties in providing sufficient contextual bias to track distribution shifts and in balancing output diversity with the stability and precision required for point forecasts. Existing diffusion-based approaches mainly focus on full-distribution modeling under probabilistic frameworks, often with likelihood maximization objectives, while paying little attention to dedicated strategies for high-accuracy point estimation. Moreover, other existing point prediction diffusion methods frequently rely on pre-trained or jointly trained mature models for contextual bias, sacrificing the generative flexibility of diffusion models.

To address these challenges, we propose SimDiff, a single-stage, end-to-end framework. SimDiff employs a single unified Transformer network carefully tailored to serve as both denoiser and predictor, eliminating the need for external pre-trained or jointly trained regressors. It achieves state-of-the-art point estimation performance by leveraging intrinsic output diversity and improving mean squared error accuracy through multiple inference ensembling. Key innovations, including normalization independence and the median-of-means estimator, further enhance adaptability and stability. Extensive experiments demonstrate that SimDiff significantly outperforms existing methods in time series point forecasting.
TimeMosaic: Temporal Heterogeneity Guided Time Series Forecasting via Adaptive Granularity Patch and Segment-wise Decoding
Authors: Kuiye Ding; Fanda Fan; Chunyi Hou; Zheya Wang; Lei Wang; Zhengxin Yang; Jianfeng Zhan
Venue: AAAI 2026 (main)

Abstract: Multivariate time series forecasting is essential in domains such as finance, transportation, climate, and energy. However, existing patch-based methods typically adopt fixed-length segmentation, overlooking the heterogeneity of local temporal dynamics and the decoding heterogeneity of forecasting. Such designs lose details in information-dense regions, introduce redundancy in stable segments, and fail to capture the distinct complexities of short-term and long-term horizons. We propose TimeMosaic, a forecasting framework that aims to address temporal heterogeneity. TimeMosaic employs adaptive patch embedding to dynamically adjust granularity according to local information density, balancing motif reuse with structural clarity while preserving temporal continuity. In addition, it introduces segment-wise decoding that treats each prediction horizon as a related subtask and adapts to horizon-specific difficulty and information requirements, rather than applying a single uniform decoder. Extensive evaluations on benchmark datasets demonstrate that TimeMosaic delivers consistent improvements over existing methods, and our model trained on the large-scale corpus with 321 billion observations achieves performance competitive with state-of-the-art TSFMs.
Communication-efficient Multi-Agent Reinforcement Learning with Spatiotemporal Information Hub
Authors: Ling Ding; Tianbai Lyu; Zhiliang Bi; Hao Wang; Shanshan Feng; Wei Yu
Venue: AAAI 2026 (main)

Abstract: Centralized training with decentralized execution (CTDE) is a framework for MARL with wide applications.
In the CTDE paradigm, agents leverage global state information during training to mitigate the non-stationarity of the MARL environment, but must rely solely on partial observations during execution. 
Recent work has highlighted the growing importance of inter-agent communication for more effective learning and coordination. 
However, most existing methods overlook the fact that real-world communication channels are often bandwidth-constrained and imperfectly reliable.
Toward more communication-efficient and robust MARL, we extend the conventional CTDE framework with an information hub.
The hub collects local observations from the agents to restore the global state, which is then delivered to the agents on demand.
To this end, technical mechanisms are designed to enable effective global reconstruction with incomplete observations, as well as agent-specific attention to the reconstructed global information.
Experiments on multiple cooperative MARL benchmarks demonstrate that our method achieves state-of-the-art performance compared to popular MARL algorithms while substantially reducing communication overhead and exhibiting strong robustness under imperfect communication channels.
PrefixGPT: Prefix Adder Optimization by a Generative Pre-trained Transformer
Authors: Ruogu Ding; Xin Ning; Ulf Schlichtmann; Weikang Qian
Venue: AAAI 2026 (main)

Abstract: Prefix adders are widely used in compute-intensive applications for their high speed. However, designing optimized prefix adders is challenging due to strict design rules and an exponentially large design space. We introduce PrefixGPT, a generative pre-trained Transformer (GPT) that directly generates optimized prefix adders from scratch. Our approach represents an adder's topology as a two-dimensional coordinate sequence and applies a legality mask during generation, ensuring every design is valid by construction. PrefixGPT features a customized decoder-only Transformer architecture. The model is first pre-trained on a corpus of randomly synthesized valid prefix adders to learn design rules and then fine-tuned to navigate the design space for optimized design quality.  Compared with existing works, PrefixGPT not only finds a new optimal design with a 7.7% improved area-delay product (ADP) but exhibits superior exploration quality, lowering the average ADP by up to 79.1%. This demonstrates the potential of GPT-style models to first master complex hardware design principles and then apply them for more efficient design optimization.
Learning Time in Static Classifiers
Authors: Xi Ding; Lei Wang; Piotr Koniusz; Yongsheng Gao
Venue: AAAI 2026 (main)

Abstract: Real-world visual data rarely presents as isolated, static instances. Instead, it often evolves gradually over time through variations in pose, lighting, object state, or scene context. However, conventional classifiers are typically trained under the assumption of temporal independence, limiting their ability to capture such dynamics. We propose a simple yet effective framework that equips standard feedforward classifiers with temporal reasoning, all without modifying model architectures or introducing recurrent modules. At the heart of our approach is a novel Support-Exemplar-Query (SEQ) learning paradigm, which structures training data into temporally coherent trajectories. These trajectories enable the model to learn class-specific temporal prototypes and align prediction sequences via a differentiable soft-DTW loss. A multi-term objective further promotes semantic consistency and temporal smoothness. By interpreting input sequences as evolving feature trajectories, our method introduces a strong temporal inductive bias through loss design alone. This proves highly effective in both static and temporal tasks: it enhances performance on fine-grained and ultra-fine-grained image classification, and delivers precise, temporally consistent predictions in video anomaly detection. Despite its simplicity, our approach bridges static and temporal learning in a modular and data-efficient manner, requiring only a simple classifier on top of pre-extracted features.
Sliding-Window Merging for Compacting Patch-Redundant Layers in LLMs
Authors: Xuan Ding; Rui Sun; Yunjian Zhang; Xiu Yan; Yueqi Zhou; Kaihao Huang; Suzhong Fu; Angelica I Aviles-Rivero; Chuanlong Xie; Yao Zhu
Venue: AAAI 2026 (main)

Abstract: Depth-wise pruning accelerates LLM inference in resource-constrained scenarios but suffers from performance degradation due to indiscriminate removal of entire Transformer layers. This paper reveals ``Patch-Like'' redundancy across layers via correlation analysis of the outputs of different layers in reproducing kernel Hilbert space, demonstrating consecutive layers exhibit high functional similarity. Building on this observation, this paper proposes Sliding-Window Merging (SWM) - a dynamic compression method that selects consecutive layers from top to bottom using a pre-defined similarity threshold, and compacts patch-redundant layers through a parameter consolidation, thereby simplifying the model structure while maintaining its performance. Extensive experiments on LLMs with various architectures and different parameter scales show that our method outperforms existing pruning techniques in both zero-shot inference performance and retraining recovery quality after pruning. In particular, in the experiment with 35\% pruning on the Vicuna-7B model, our method achieved a 1.654\% improvement in average performance on zero-shot tasks compared to the existing method. Moreover, we further reveal the potential of combining depth pruning with width pruning to enhance the pruning effect.
RAC-DMVC: Reliability-Aware Contrastive Deep Multi-View Clustering Under Multi-Source Noise
Authors: Shihao Dong; Yue Liu; Xiaotong Zhou; Yuhui Zheng; Huiying Xu; Xinzhong Zhu
Venue: AAAI 2026 (main)

Abstract: Multi-view clustering (MVC), which aims to separate the multi-view data into distinct clusters in an unsupervised manner, is a fundamental yet challenging task. To enhance its applicability in real-world scenarios, this paper addresses a more challenging task: MVC under multi-source noises, including missing noise and observation noise. To this end, we propose a novel framework, Reliability-Aware Contrastive Deep Multi-View Clustering (RAC-DMVC), which constructs a reliability graph to guide robust representation learning under noisy environments. Specifically, to address observation noise, we introduce a cross-view reconstruction to enhances robustness at the data level, and a reliability-aware noise contrastive learning to mitigates bias in positive and negative pairs selection caused by noisy representations. To handle missing noise, we design a dual-attention imputation to capture shared information across views while preserving view-specific features. In addition, a self-supervised cluster distillation module further refines the learned representations and improves the clustering performance. Extensive experiments on five benchmark datasets demonstrate that RAC-DMVC outperforms SOTA methods on multiple evaluation metrics and maintains excellent performance under varying ratios of noise.
Accelerating LLM Inference Throughput via Asynchronous KV Cache Prefetching
Authors: Yanhao Dong; Yubo Miao; Weinan Li; Xiao Zheng; Chao Wang; Jiesheng Wu; Feng Lyu
Venue: AAAI 2026 (main)

Abstract: Large Language Models (LLMs) exhibit pronounced memory-bound characteristics during inference due to High Bandwidth Memory (HBM) bandwidth constraints. In this paper, we propose an L2 Cache-oriented asynchronous KV Cache prefetching method to break through the memory bandwidth bottleneck in LLM inference through computation-load overlap. By strategically scheduling idle memory bandwidth during active computation windows, our method proactively prefetches required KV Cache into GPU L2 cache, enabling high-speed L2 cache hits for subsequent accesses and effectively hiding HBM access latency within computational cycles. Extensive experiments on NVIDIA H20 GPUs demonstrate that the proposed method achieves 2.15× improvement in attention kernel efficiency and up to 1.97× end-to-end throughput enhancement, surpassing state-of-the-art baseline FlashAttention-3. Notably, our solution maintains orthogonality to existing optimization techniques and can be integrated with current inference frameworks, providing a scalable latency-hiding solution for next-generation LLM inference engines.
EnViT: Enhancing the Performance of Early-Exit Vision Transformers via Exit-Aware Structured Dropout-Enabled Self-Distillation
Authors: Yonghao Dong; Qiang He; Penghong Rui; Zhenzhe Zheng; Zhao Li; Feifei Chen; Hai Jin; Yun Yang
Venue: AAAI 2026 (main)

Abstract: Vision Transformers (ViTs) have gained significant attention and widespread adoption due to their impressive performance in various computer vision tasks. However, in practice, their substantial computational overhead often leads to high inference latency and increased overheads when deployed on resource-constrained edge devices like smartphones, autonomous vehicles, and robots. To address these challenges, Early Exit (EE) has emerged as a promising approach for lightweight inference on edge devices. It accelerates inference and reduces computational overhead by adaptively producing predictions through early exits based on sample complexity. Existing EE methods typically suffer from substantial accuracy decreases in late exits while providing only marginal accuracy improvements to early exits. This paper presents EnViT, an exit-aware structured dropout-enabled self-distillation approach that enhances the performance of early exits without compromising late exits. EnViT leverages structured dropout to enable self-distillation, where the full model serves as the teacher and its own virtual sub-models generated by structured dropout as students. This mechanism effectively distills knowledge from the full model to early exits and avoids performance degradation in late exits by mitigating parameter conflicts across exits during training. Evaluation on five datasets shows that our EnViT achieves accuracy improvements ranging from 0.36% to 7.92% while maintaining competitive speed-up ratios of 1.72x to 2.23x.
Learning Dynamics as Feedback: An Adaptive Entropy Flow Dynamics Framework for Long-tailed Human Action Recognition
Authors: Yuan Dong; Zhe Zhao; Liheng Yu; Di Wu; Pengkun Wang
Venue: AAAI 2026 (main)

Abstract: Deep human action recognition models trained on real-world data are often challenged by long-tailed distributions, where performance on rare classes is severely degraded. Current solutions typically apply static or heuristic interventions that are disconnected from the model's evolving internal state. To overcome this limitation, we reconceptualize long-tailed human action recognition as a closed-loop, self-regulating system, inspired by ecological theory. We further introduce an Adaptive Ecological Entropy Dynamics (AEED) framework, which is built upon three synergistic components. First, AEED perceives the learning state through entropy flow, providing a robust and directional signal of learning progress. Second, this signal drives an adaptation mechanism, which dynamically adjusts class-specific loss weights to allocate more learning resources to underperforming classes. Finally, AEED facilitates intelligent knowledge transfer via Confidence-Guided Symbiosis (CS-Mix). Extensive experiments demonstrate that AEED achieves state-of-the-art performance on challenging skeleton-based action recognition benchmarks, including NTU-60-LT and Kinetics-400-LT.
Constrained Particle Seeking: Solving Diffusion Inverse Problems with Just Forward Passes
Authors: Hongkun Dou; Zike Chen; Zeyu Li; Hongjue Li; Lijun Yang; Yue Deng
Venue: AAAI 2026 (main)

Abstract: Diffusion models have gained prominence as powerful generative tools for solving inverse problems due to their ability to model complex data distributions. However, existing methods typically rely on complete knowledge of the forward observation process to compute gradients for guided sampling, limiting their applicability in scenarios where such information is unavailable. In this work, we introduce *Constrained Particle Seeking (CPS)*, a novel gradient-free approach that leverages all candidate particle information to actively search for the optimal particle while incorporating constraints aligned with high-density regions of the unconditional prior. Unlike previous methods that passively select promising candidates, CPS reformulates the inverse problem as a constrained optimization task, enabling more flexible and efficient particle seeking. We demonstrate that CPS can effectively solve both image and scientific inverse problems, achieving results comparable to gradient-based methods while significantly outperforming gradient-free alternatives.
DSCF: Dual-Source Counterfactual Fusion for High-Dimensional Combinatorial Interventions
Authors: Jitong Dou; Lingrui Luo; Bing Zhu; Hengliang Luo; Mingjun Zhong; Yurong Cheng
Venue: AAAI 2026 (main)

Abstract: Estimating counterfactual outcomes from observational data is critical for informed decision-making in domains such as personalized marketing, healthcare, and online platforms. In these contexts, decision processes frequently involve high-dimensional combinatorial interventions, including bundled channel allocation or product set recommendations. For such scenarios, both causal assessment of historical strategies and optimization of novel interventions necessitate models capable of extrapolating to intervention combinations that are underrepresented or entirely absent in observational data. Specifically, in digital marketing, companies often need to evaluate new combinations of channels or target emerging user segments that have not been previously exposed. This challenge is exacerbated by inherent biases in observational datasets, stemming from prior allocation policies and targeting mechanisms, which further aggravate coverage sparsity and compromise off-support counterfactual inference. In this work, we propose Dual-Source Counterfactual Fusion (DSCF), a scalable framework that enables accurate counterfactual prediction under high-dimensional combinatorial interventions, with improved robustness to confounding bias. DSCF jointly models observational data and proxy counterfactual samples through a dual-head mixture-of-experts architecture and domain-guided fusion. This design effectively integrates bias reduction and information diversity while enabling adaptive generalization to counterfactual inputs. Extensive experiments on both synthetic and semi-synthetic datasets demonstrate the effectiveness and robustness of DSCF across diverse scenarios.
Closer to Biological Mechanism: Drug-Drug Interaction Prediction from the Perspective of Pharmacophore
Authors: Mingliang Dou; Linfeng Wen; Jinyang Xie; Jijun Tang; Shiqiang Ma; Fei Guo
Venue: AAAI 2026 (main)

Abstract: Drug combinations are widely used in modern medicine but may cause severe adverse drug reactions. Therefore, making effective drug-drug interactions (DDI) prediction is crucial for pharmacovigilance. Existing DDI prediction models are typically built from a structural perspective, assuming that drugs with similar molecular structures may exhibit similar interactions. However, such approaches overlook the biological mechanisms underlying DDI in the human body. This not only weakens the generalization ability of the model, but also makes its interpretability less convincing. Inspired by this, we propose a new method called PC-DDI. Unlike structure-based models, PC-DDI utilizes pharmacophores as basic unit, and designs a complete pharmacophore feature processing framework. It further constructs a pharmacophore-based bipartite graph to model interactions between pharmacophores. This approach allows us to explore the underlying mechanisms of DDI from a functional perspective.  We also design a spatial attention weight graph convolution module to optimize the message passing process by integrating pharmacophore position features with node features. Furthermore, we apply causal inference to identify key pharmacophores in pharmacophore bipartite graph, enhancing the interpretability.  Compared with the SOTA, PC-DDI achieves an accuracy improvement of 1.84% under the transductive setting and consistently outperforms others in all other experiments.
Predicting the Future by Retrieving the Past
Authors: Dazhao Du; Tao Han; Song Guo
Venue: AAAI 2026 (main)

Abstract: Deep learning models such as MLP, Transformer, and TCN have achieved remarkable success in univariate time series forecasting, typically relying on sliding window samples from historical data for training. However, while these models implicitly compress historical information into their parameters during training, they are unable to explicitly and dynamically access this global knowledge during inference, relying only on the local context within the lookback window. This results in an underutilization of rich patterns from the global history. To bridge this gap, we propose Predicting the Future by Retrieving the Past (PFRP), a novel approach that explicitly integrates global historical data to enhance forecasting accuracy. Specifically, we construct a Global Memory Bank (GMB) to effectively store and manage global historical patterns. A retrieval mechanism is then employed to extract similar patterns from the GMB, enabling the generation of global predictions. By adaptively combining these global predictions with the outputs of any local prediction model, PFRP produces more accurate and interpretable forecasts. Extensive experiments conducted on seven real-world datasets demonstrate that PFRP enhances the average performance of advanced univariate forecasting models by 8.4%.