GenAI4ELab/papercli-papers huggingface dataset:
Cross-Sample Augmented Test-Time Adaptation for Personalized Intraoperative Hypotension Prediction
Authors: Kanxue Li; Yibing Zhan; Hua Jin; Chongchong Qi; Xu Lin; Baosheng Yu
Venue: AAAI 2026 (main)

Abstract: Intraoperative hypotension (IOH) poses significant surgical risks, but accurate prediction remains challenging due to patient-specific variability. While test-time adaptation (TTA) offers a promising approach for personalized prediction, the rarity of IOH events often leads to unreliable test-time training. To address this, we propose CSA-TTA, a novel cross-sample augmented test-time adaptation framework that enhances training by incorporating hypotension events from other individuals. Specifically, we first construct a cross-sample bank by segmenting historical data into hypotensive and non-hypotensive samples. Then, we introduce a coarse-to-fine retrieval strategy for building test-time training data: we initially apply K-Shape clustering to identify representative cluster centers and subsequently retrieve the top-K semantically similar samples based on the current patient signal. Additionally, we integrate both self-supervised masked reconstruction and retrospective sequence forecasting signals during training to enhance model adaptability to rapid and subtle intraoperative dynamics. We evaluate the proposed CSA-TTA on both the VitalDB dataset and a real-world in-hospital dataset by integrating it with state-of-the-art time series forecasting models, including TimesFM and UniTS. CSA-TTA consistently enhances performance across settings—for instance, on VitalDB, it improves Recall and F1 scores by +1.33% and +1.13%, respectively, under fine-tuning, and by +7.46% and +5.07% in zero-shot scenarios—demonstrating strong robustness and generalization.
BOFA: Bridge-Layer Orthogonal Low-Rank Fusion for CLIP-Based Class-Incremental Learning
Authors: Lan Li; Tao Hu; Da-Wei Zhou; Jia-Qi Yang; Han-jia Ye; De-Chuan Zhan
Venue: AAAI 2026 (main)

Abstract: Class-Incremental Learning (CIL) aims to continually learn new classes without forgetting previously acquired knowledge. Vision-language models such as CLIP offer strong transferable representations via multi-modal supervision, making them a promising choice for CIL. However, applying CLIP to CIL poses two major challenges: (1) adapting to downstream tasks often requires additional learnable modules, increasing model complexity and susceptibility to forgetting; and (2) while multi-modal representations offer complementary strengths, existing methods have not fully exploited the synergy between visual and textual modalities. To address these issues, we propose BOFA (Bridge-layer Orthogonal Fusion for Adaptation), a novel framework for CIL. BOFA restricts adaptation to CLIP’s existing cross-modal bridge layer, keeping the core learning process parameter-free and avoiding any extra adaptation modules. To prevent forgetting within this layer, it leverages Orthogonal Low-Rank Fusion, a mechanism that constrains parameter updates to a low-rank ``safe subspace" that is mathematically constructed to be approximately orthogonal to the feature subspace of past tasks. This encourages stable knowledge accumulation and mitigates interference between new and previously learned classes. Furthermore, BOFA employs a cross-modal hybrid prototype that fuses stable textual prototypes with dynamic visual counterparts derived from our adapted bridge layer, resulting in a more robust and discriminative classifier. Extensive experiments on standard benchmarks demonstrate that BOFA achieves superior accuracy and efficiency compared to existing methods.
C-GNN-PRUNE: A Unified Graph-Based Framework for Structure-Aware Pruning of Mixture-of-Experts Models
Authors: Lin Li; Yan Wang; Zhuopeng Wang
Venue: AAAI 2026 (main)

Abstract: The Mixture-of-Experts (MoE) architecture has emerged as a promising paradigm for scaling large language models (LLMs) by activating only a sparse subset of experts per input. However, its massive parameter size remains a major obstacle to efficient deployment. Existing pruning methods often ignore two key aspects: the intricate structural dependencies among experts and the heterogeneous importance of different layers. To tackle these issues, we propose C-GNN-PRUNE, a unified and structure-aware compression framework tailored for MoE models. Our method  introduces an EntropyGuided Allocation Module that dynamically assigns pruning budgets by leveraging expert activation entropy, enabling adaptive handling of inter-layer heterogeneity. To preserve structural collaboration patterns, we construct an expert interaction graph that fuses functional similarity and routing behavior, and employ a GNN-Based Embedding Module to learn structure-aware expert representations. These embeddings, along with co-activation patterns, are fed into a Community Detection Module to identify expert clusters for structured pruning. Finally, an Activation-Aware Selection Module retains the most critical experts in each community, balancing sparsity and expressiveness. Experiments on multiple open-source MoE models demonstrate that C-GNN-PRUNE consistently outperforms prior methods under various pruning ratios, achieving better trade-offs between compression and accuracy. This framework provides a modular and effective solution for structure-preserving compression of large-scale MoE models.
Stratified Knowledge-Density Super-Network for Scalable Vision Transformers
Authors: Longhua Li; Lei Qi; Xin Geng
Venue: AAAI 2026 (main)

Abstract: Training and deploying multiple vision transformer (ViT) models for different resource constraints is costly and inefficient.
To address this, we propose transforming a pre-trained ViT into a stratified knowledge-density super-network, where knowledge is hierarchically organized across weights. This enables flexible extraction of sub-networks that retain maximal knowledge for varying model sizes. We introduce Weighted PCA for Attention Contraction (WPAC), which concentrates knowledge into a compact set of critical weights. WPAC applies token-wise weighted principal component analysis to intermediate features and injects the resulting transformation and inverse matrices into adjacent layers, preserving the original network function while enhancing knowledge compactness. To further promote stratified knowledge organization, we propose Progressive Importance-Aware Dropout (PIAD). PIAD progressively evaluates the importance of weight groups, updates an importance-aware dropout list, and trains the super-network under this dropout regime to promote knowledge stratification. Experiments demonstrate that WPAC outperforms existing pruning criteria in knowledge concentration, and the combination with PIAD offers a strong alternative to state-of-the-art model compression and model expansion methods.
Sub-MoE: Efficient Mixture-of-Expert LLMs Compression via Subspace Expert Merging
Authors: Lujun Li; Qiyuan Zhu; Jiacheng Wang; Xiaoyu Qin; Wei Li; Hao Gu; Sirui Han; Yike Guo
Venue: AAAI 2026 (main)

Abstract: Mixture of Experts (MoE) LLMs face significant obstacles due to their massive parameter scale, which imposes memory, storage, and deployment challenges. Although recent expert merging methods aim to achieve greater efficiency by consolidating several experts, they are fundamentally hindered by parameter conflicts arising from expert specialization. In this paper, we present Sub-MoE, a novel MoE compression framework via Subspace Expert Merging. Our key insight is to perform joint Singular Value Decomposition (SVD) on concatenated expert weights, reducing conflicting parameters by extracting shared U-matrices while enabling effective merging of the expert-specific V components.  Specifically, Sub-MoE consists of two innovative stages: (1) Adaptive Expert Clustering, which groups functionally coherent experts via K-means clustering based on cosine similarity of expert outputs; and (2) Subspace Expert Merging, which first performs Experts Union Decomposition to derive the shared U-matrix across experts in the same group, then applies frequency-based merging for individual V-matrices, and completes expert reconstruction using the merged V-matrix. In this way, we align and fuse experts in a shared subspace. Additionally, the framework can be extended with intra-expert compression for further inference optimization. Extensive experiments on Mixtral, DeepSeek, and Qwen-1.5/3 MoE LLMs demonstrate that our Sub-MoE significantly outperforms existing expert pruning and merging methods. Notably, our Sub-MoE maintains 96%/86% of original performance with 25%/50% expert reduction on Mixtral-8×7B in zero-shot benchmarks.
From Tokens to Latent States: Leveraging Pre-trained Language Models for Improving Partially Observable Reinforcement Learning
Authors: Meiju Li; Ruixiang Sun; Xin Li; Mingzhong Wang
Venue: AAAI 2026 (main)

Abstract: Partially observable Markov decision processes (POMDPs) present significant challenges for reinforcement learning, as agents must learn optimal policies while maintaining belief states over unobserved environment states based on partial observations. 
We observe a compelling analogy: large language models (LLMs) autoregressively generate token probability distributions based on preceding context, mirroring how belief states are maintained and updated in POMDPs. 
This insight motivates leveraging the rich prior knowledge embedded in pre-trained LLMs for latent states estimation from observation-action histories. 
However, two critical challenges emerge: on the one hand, modality misalignment prevents LLMs from directly encoding visual observations and discrete actions; on the other hand, semantic misalignment exists between observation-action sequences and token sequences. 
To address these challenges, we introduce a novel framework ELSLLM that employs a Johnson-Lindenstrauss projection (JLP) module to transform input dimensions while preserving state similarity with theoretical guarantees, and utilizes modern Hopfield networks (MHN) to store all word embeddings from pre-trained LLMs as a knowledge repository. 
Through retrieval and querying mechanisms, ELSLLM achieves token-level knowledge alignment without requiring fine-tuning of the pre-trained LLMs. 
Extensive experiments on partially observable environments demonstrate that ELSLLM achieves state-of-the-art performance, significantly outperforming baseline methods with and without LSTM memory mechanisms. 
Our work opens new avenues for integrating pre-trained LLMs with reinforcement learning in partially observable settings.
Rethinking Open-world Prompt Tuning: A Systematic Framework for Evaluation and Optimization
Authors: Mengwei Li; Zilei Wang; Yixin Zhang
Venue: AAAI 2026 (main)

Abstract: Prompt Tuning (PT) is a widely used strategy for adapting pre-trained Vision-Language Models (VLMs) to various downstream tasks. Conventional PT methods evaluate performance separately on known (base) and unknown (new) classes. However, in real-world scenarios, models often encounter inputs without prior knowledge of their class domain. This challenge has motivated the development of Open-world Prompt Tuning (OPT), which requires models to first determine whether a sample belongs to base or new classes and then classify it accordingly. In this work, we carefully review existing OPT methods and identify three key limitations: (L1) incomplete evaluation metrics, (L2) time-consuming and memory-intensive OOD detection methods, and (L3) insufficiently comprehensive optimization strategies. To address these issues, we first tackle L1 by proposing two novel metrics to explicitly evaluate adaptability and generalization under the OPT setting, forming a more comprehensive evaluation framework. For L2, we propose a training-free OOD detection method called Entropy-weighted Rank-normalized Fusion (ERF), which first applies rank normalization to both the maximum and the sum of base-class probabilities, followed by an entropy-weighted fusion of the normalized values. For L3, we propose a plug-and-play Gated Dual-Merging (GDM) strategy to strengthen the classifier’s capability. GDM performs selective merging at the weight level based on an adaptive criterion and combines fine-tuned and LLM-boosted logits at the output level. Extensive experiments on three PT baselines across 11 datasets demonstrate the effectiveness of our proposed ERF and GDM.
Difficulty-Aware Learning Curve Extrapolation
Authors: Mengyang Li; Pinlong Zhao
Venue: AAAI 2026 (main)

Abstract: Learning Curve Extrapolation (LCE) is a critical technique for accelerating automated machine learning by terminating unpromising training runs early. Recent state-of-the-art methods have improved predictive accuracy by incorporating contextual information, such as neural network architecture. However, these approaches, whether context-agnostic or architecture-aware, still operate under the implicit assumption of a uniform task landscape. They overlook a pivotal, complementary factor: the intrinsic difficulty of the learning task itself. This oversight leads to significant performance degradation, especially for tasks whose learning dynamics diverge from the model's priors. In this work, we argue that task difficulty is a crucial yet neglected dimension for robust LCE. We introduce Difficulty-Aware Learning Curve Extrapolation (DA-LCE), which explicitly conditions its predictions on task complexity. Our core contributions are threefold: (1) We propose a transparent, rule-based method to quantify task difficulty from early learning curve dynamics, eliminating the need for external meta-features. (2) We design a novel data generation pipeline using conditional diffusion models to create high-fidelity, difficulty-conditioned synthetic training data. (3) We introduce a Transformer-based predictor that leverages difficulty information to achieve superior accuracy across diverse benchmarks. Extensive experiments demonstrate that our approach significantly outperforms both difficulty-agnostic and architecture-aware baselines, with task difficulty emerging as a powerful conditioning signal whose impact matches or exceeds that of model architecture.
Multi-Granular Graph Learning with Fine-Grained Behavioral Pattern Awareness for Session-Based Recommendation
Authors: Ming Li; Zihao Yan; Yuting Chen; Lixin Cui; Lu Bai; Feilong Cao; Ke Lv; Zhao Li
Venue: AAAI 2026 (main)

Abstract: Session-based recommendation aims to predict users’ next actions by modeling their ongoing interaction sequences, particularly in scenarios where long-term user profiles are unavailable. While existing methods have achieved promising results by leveraging sequential and graph-based structures, they often rely on global aggregation strategies that emphasize dominant user interests while overlooking the transient and fine-grained behavior patterns embedded in sessions. In practice, user intent evolves across sessions and is reflected through diverse behavioral patterns, ranging from immediate preferences to segmented co-occurrence interests and long-range goals. To address these limitations, we propose GraphFine, a novel multi-granular graph learning framework that achieves fine-grained behavioral pattern awareness for session-based recommendation. Our approach models user behavior at different temporal and semantic granularities through a combination of graph and hypergraph neural networks. Specifically, we employ a position-aware graph to capture short-term item transitions, and construct segmented co-occurrence hypergraphs to uncover high-order semantic relations among co-occurred items. To preserve diverse user intents, we further introduce a multi-view intent readout mechanism that extracts and adaptively integrates intent signals from short-term actions, segmented co-occurrence patterns, and entire sessions. Extensive experiments on benchmark datasets demonstrate that GraphFine consistently outperforms existing state-of-the-art methods, confirming its effectiveness in capturing fine-grained and dynamic user preferences for more accurate recommendation.
High-Pass Matters: Theoretical Insights and Sheaflet-Based Design for Hypergraph Neural Networks
Authors: Ming Li; Yujie Fang; Dongrui Shen; Han Feng; Xiaosheng Zhuang; Kelin Xia; Pietro Lio
Venue: AAAI 2026 (main)

Abstract: Hypergraph neural networks (HGNNs) have shown great potential in modeling higher-order relationships among multiple entities. However, most existing HGNNs primarily emphasize low-pass filtering while neglecting the role of high-frequency information. In this work, we present a theoretical investigation into the spectral behavior of HGNNs and prove that combining both low-pass and high-pass components leads to more expressive and effective models. Notably, our analysis highlights that high-pass signals play a crucial role in capturing local discriminative structures within hypergraphs. Guided by these insights, we propose a novel sheaflet-based HNNs that integrates cellular sheaf theory and framelet transforms to preserve higher-order dependencies while enabling multi-scale spectral decomposition. This framework explicitly emphasizes high-pass components, aligning with our theoretical findings. Extensive experiments on benchmark datasets demonstrate the superiority of our approach over existing methods, validating the importance of high-frequency information in hypergraph learning.
HyperNoRA: Hyperedge Prediction via Node-Level Relation-Aware Self-Supervised Hypergraph Learning
Authors: Ming Li; Zhanle Zhu; Xinyi Li; Lu Bai; Lixin Cui; Feilong Cao; Ke Lv
Venue: AAAI 2026 (main)

Abstract: Hyperedge prediction plays a critical role in high-order relational modeling with hypergraphs, yet most existing methods primarily focus on sampling strategies or local aggregation within candidate hyperedges. These approaches often overlook global structural dependencies that are essential for learning expressive node and hyperedge representations. In this paper, we propose HyperNoRA, a novel self-supervised hypergraph learning framework that integrates global node-level relation awareness with contrastive learning. Specifically, we construct a global node relation graph that captures both direct and indirect structural correlations, which guides a structure-aware aggregator to enhance node representations with informative global context. To prevent over-smoothing and maintain discriminability, a contrastive learning module is introduced to align representations across graph augmentations while separating semantically dissimilar nodes. Extensive experiments on several benchmark datasets demonstrate that HyperNoRA consistently outperforms state-of-the-art baselines, and ablation studies verify the effectiveness of its key components.
Self-Supervised Hypergraph Learning with Substructure Awareness for Hyperedge Prediction
Authors: Ming Li; Huiting Wang; Yuting Chen; Lu Bai; Lixin Cui; Feilong Cao; Ke Lv
Venue: AAAI 2026 (main)

Abstract: Hyperedge prediction plays a central role in hypergraph learning, enabling the inference of high-order relations among multiple entities. However, existing methods often rely on a simplistic flat set assumption, treating candidate hyperedges as unstructured collections of nodes and neglecting their potential internal compositionality. Furthermore, the severe scarcity of observed hyperedges poses a challenge for effective supervision. In this work, we propose S3Hyper, a Substructure-contextualized Self-Supervised framework for Hyperedge prediction, which jointly addresses these two challenges. Specifically, we design a substructure-contextualized hyperedge aggregator that models the internal hierarchy of candidate hyperedges by leveraging sub-hyperedge information. In parallel, we introduce an adaptive tri-directional contrastive learning module that incorporates node-level, hyperedge-level, and cross-level alignment objectives, supported by temperature-adaptive mechanisms. Experimental results on four public datasets demonstrate that S3Hyper consistently outperforms strong baselines, with ablation studies verifying the effectiveness of each component.
HyperAim: Hypergraph Contrastive Learning with Adaptive Multi-frequency Filters
Authors: Ming Li; Ruiting Zhao; Zihao Yan; Lu Bai; Lixin Cui; Feilong Cao
Venue: AAAI 2026 (main)

Abstract: Unsupervised hypergraph representation learning has recently gained traction for its ability to model complex high-order interactions without requiring labeled data. However, existing contrastive learning methods typically overlook the frequency diversity inherent in hypergraph signals. To address this issue, we propose HyperAim, a contrastive learning framework that integrates adaptive multi-frequency filtering through both decoupled and coupled designs. Specifically, HyperAim employs two decoupled channels with polynomial low-pass and high-pass filters to separately capture distinct frequency components, and a third channel based on framelet decomposition that adaptively fuses multi-frequency signals in a coupled manner. A frequency-aware contrastive learning strategy is introduced to align representations across views using a combination of InfoNCE loss and pseudo-label-guided supervision. Extensive experiments across 12 benchmark datasets, covering both homophilic and heterophilic hypergraphs, demonstrate the consistent superiority of HyperAim over 17 baselines. Ablation studies further confirm the benefits of explicitly modeling and aligning frequency-specific representations.
Heterophily-aware Contrastive Learning for Heterophilic Hypergraphs
Authors: Ming Li; Yongqi Li; Yuting Chen; Feilong Cao; Ke Lv
Venue: AAAI 2026 (main)

Abstract: Hypergraph neural networks (HNNs) have emerged as powerful tools for modeling high-order relationships in complex systems. However, most existing HNNs are designed under the assumption of homophily, which does not hold in many real-world scenarios where connected nodes often exhibit diverse semantics, i.e., heterophily. This inconsistency leads to suboptimal aggregation and degraded performance, especially in low-label regimes. While a few recent methods have attempted to enhance heterophilic hypergraph learning, they often rely heavily on label supervision and overlook the potential of self-supervised techniques. In this paper, we propose HeroCL, a heterophily-aware contrastive learning framework that improves hypergraph representation under both structural heterogeneity and label scarcity. Specifically, HeroCL integrates a multi-hop neighbor encoding module to capture informative higher-order context and incorporates two complementary contrastive objectives, label-aware and structure-aware, to guide representation learning from both semantic and relational perspectives. A multi-granularity contrastive strategy is introduced to exploit latent signals across multiple neighborhood levels. Extensive experiments on several benchmark datasets against 11 existing baselines demonstrate that HeroCL achieves consistent and significant performance gains, particularly under strong heterophily and limited supervision, validating its robustness and effectiveness.