GenAI4ELab/papercli-papers huggingface dataset:
Making Sense of LLM Decisions: A Prototype-based Framework for Explainable Classification
Authors: Bowen Wei; Mehrdad Fazli; Ziwei Zhu
Venue: AAAI 2026 (main)

Abstract: Large language models have demonstrated impressive performance on natural language tasks, but their decision-making processes remain opaque. Existing explanation methods either suffer from limited faithfulness to the model's reasoning or produce explanations that are difficult for humans to understand. To address these challenges, we propose ProtoSurE, a novel prototype-based surrogate framework that provides faithful and understandable explanations for LLMs. ProtoSurE trains an interpretable-by-design surrogate model that aligns with the target LLM while utilizing sentence-level prototypes as understandable concepts. Extensive experiments show that ProtoSurE consistently outperforms state-of-the-art explanation methods across diverse LLMs and datasets. Importantly, ProtoSurE demonstrates strong data efficiency, requiring relatively few training examples to achieve good performance, making it practical for real-world applications.
Clustering with Self-Learned Graph Regression
Authors: Lai Wei; Jin Liu
Venue: AAAI 2026 (main)

Abstract: Graph-based clustering algorithms aim to construct an affinity graph that accurately captures the intrinsic structure of a dataset. To achieve this goal, these algorithms often use the k-nearest-neighbor (k-nn) method to build a graph regularizer for the required affinity graph, enabling it to have a grouping effect. However, due to the complex nature of real-world data, the k-nn method often fails to capture the true neighborhood relationships of a dataset, which in turn limits the quality of the learned affinity graph. Motivated by the insight that a learned affinity graph itself can more effectively reflect the underlying data structure, we propose a new graph-based clustering method, termed Self-learned Graph Regression (SGR). Unlike traditional approaches, SGR constructs its graph regularizer directly from the affinity graph being learned, allowing the graph to adaptively capture more accurate structural information. To solve the proposed problem, we develop an optimization algorithm along with an acceleration strategy. We further analyze the convergence and computational complexity of the proposed algorithm. Extensive clustering experiments on various benchmark datasets demonstrate that our method outperforms the state-of-the-art graph-based clustering algorithms.
A General Anchor-Based Framework for Scalable Fair Clustering
Authors: Shengfei Wei; Suyuan Liu; Jun Wang; Ke Liang; Miaomiao Li; Lei Luo
Venue: AAAI 2026 (main)

Abstract: Fair clustering is crucial for mitigating bias in unsupervised learning, yet existing algorithms often suffer from quadratic or super-quadratic computational complexity, rendering them impractical for large-scale datasets. To bridge this gap, we introduce the Anchor-based Fair Clustering Framework (AFCF), a novel, general, and plug-and-play framework that empowers arbitrary fair clustering algorithms with linear-time scalability. Our approach first selects a small but representative set of anchors using a novel fair sampling strategy. Then, any off-the-shelf fair clustering algorithm can be applied to this small anchor set. The core of our framework lies in a novel anchor graph construction module, where we formulate an optimization problem to propagate labels while preserving fairness. This is achieved through a carefully designed group-label joint constraint, which we prove theoretically ensures that the fairness of the final clustering on the entire dataset matches that of the anchor clustering. We solve this optimization efficiently using an ADMM-based algorithm. Extensive experiments on multiple large-scale benchmarks demonstrate that AFCF drastically accelerates state-of-the-art methods, which reduces computational time by orders of magnitude while maintaining strong clustering performance and fairness guarantees.
FedShard: Federated Unlearning with Efficiency Fairness and Performance Fairness
Authors: Siyuan Wen; Meng Zhang; Yang Yang; Ningning Ding
Venue: AAAI 2026 (main)

Abstract: To protect clients' right to be forgotten in federated learning, federated unlearning aims to remove the data contribution of leaving clients from the global learned model. While current studies mainly focused on enhancing unlearning efficiency and effectiveness, the crucial aspects of efficiency fairness and performance fairness among decentralized clients during unlearning have remained largely unexplored. In this study, we introduce FedShard, the first federated unlearning algorithm designed to concurrently guarantee both efficiency fairness and performance fairness. FedShard adaptively addresses the challenges introduced by dilemmas among convergence, unlearning efficiency, and unlearning fairness. Furthermore, we propose two novel metrics to quantitatively assess the fairness of unlearning algorithms, which we prove to satisfy well-known properties in other existing fairness measurements. Our theoretical analysis and numerical evaluation validate FedShard's fairness in terms of both unlearning performance and efficiency. We demonstrate that FedShard mitigates unfairness risks such as cascaded leaving and poisoning attacks and realizes more balanced unlearning costs among clients. Experimental results indicate that FedShard accelerates the data unlearning process 1.3-6.2 times faster than retraining from scratch and 4.9 times faster than the state-of-the-art exact unlearning methods.
Universal EEG Epilepsy Detection via Evidential Multi-View De-Biasing
Authors: Ziqi Wen; Cai Xu; Wanqing Zhao; Jie Zhao; Wei Zhao
Venue: AAAI 2026 (main)

Abstract: Epilepsy is a widespread neurological disorder characterized by highly patient-specific EEG patterns. Existing EEG-based seizure detection methods either train individualized models for each patient or adapt models pre-trained on known patients to new ones. However, when encountering previously unseen patients, these methods typically require retraining or fine-tuning, which limits their practical utility in clinical settings. This limitation can be linked to biases caused by patient-specific variations, which obscure the underlying pathological patterns of seizures. To address this, we propose an evidential multi-view framework that reinforces the learning of core epileptic features by promoting consistency across multiple views and reducing reliance on high-uncertainty, patient-specific segments. Specifically, we introduce Bias-guided Fisher-Evidential Multi-View Learning (BF-EML) to guide the model toward discovering intrinsic seizure patterns. BF-EML employs a two-stage training architecture: In Stage 1, we use the Fisher Information Matrix to reorder EEG segments by uncertainty and deliberately train a biased feature generator on low-evidence segments. In Stage 2, we design a dual-branch network where the biased and unbiased branches are alternately trained, encouraging the unbiased branch to reduce its reliance on patient-specific biases. Finally, we introduce a shift-calibrated fusion strategy to enhance the consistency of pathogenic feature integration. Extensive experiments on public datasets and a clinical dataset demonstrate that our method achieves superior performance in both single- and multi-patient scenarios. Importantly, it generalizes well to unseen patients without the need for retraining.
Decoupled Spatiotemporal Forecasting from Extreme Sparse Observations via Quantized Latent Space
Authors: Zhongnan Weng; Yue Hong; Hang Yu; Jiayi Que; Juan Liu; Xiangrong Liu
Venue: AAAI 2026 (main)

Abstract: Predicting spatiotemporal fields governed by partial differential equations (PDEs) from sparse sensor data is a critical and long-standing challenge in science and engineering. Recent deep learning approaches, particularly neural operators, have shown considerable promise in solving PDEs. However, their performance degrades significantly in the demanding regime of extreme sparsity, characterized by spatial sensor coverage of less than 1% and limited temporal observations. To overcome this limitation, we propose a novel framework that decouples the task into two stages: spatial reconstruction and temporal extrapolation. In the first stage, rather than reconstructing the high-dimensional physical field directly, our model learns to reconstruct the complete latent features from sparse observations—features that would otherwise be extracted from a dense field. This process is stabilized by a Vector Quantization (VQ) bottleneck, which discretizes the latent space. In the second stage, a decoder-only Transformer performs temporal extrapolation by autoregressively predicting the future sequence of these discrete latent indices. This design inherently allows the model to generalize to new initial conditions and varying forecast horizons, akin to standard autoregressive models. We validate our framework on three challenging benchmarks, achieving state-of-the-art (SOTA) performance under severe sparsity constraints. Furthermore, we introduce a challenging benchmark dataset based on fire dynamics simulations. On this benchmark, our model successfully forecasts the field's evolution 30 frames into the future from a single timeframe with less than 0.1% spatial observations—a result that pushes well beyond the capabilities of existing methods.
HyperSHAP: Shapley Values and Interactions for Explaining Hyperparameter Optimization
Authors: Marcel Wever; Maximilian Muschalik; Fabian Fumagalli; Marius Lindauer
Venue: AAAI 2026 (main)

Abstract: Hyperparameter optimization (HPO) is a crucial step in achieving strong predictive performance.
Yet, the impact of individual hyperparameters on model generalization is highly context-dependent, prohibiting a one-size-fits-all solution and requiring opaque HPO methods to find optimal configurations.
However, the black-box nature of most HPO methods undermines user trust and discourages adoption. 
To address this, we propose a game-theoretic explainability framework for HPO based on Shapley values and interactions.
Our approach provides an additive decomposition of a performance measure across hyperparameters, enabling local and global explanations of hyperparameters' contributions and their interactions. 
The framework, named HyperSHAP, offers insights into ablation studies, the tunability of learning algorithms, and optimizer behavior across different hyperparameter spaces.
We demonstrate HyperSHAP's capabilities on various HPO benchmarks to analyze the interaction structure of the corresponding HPO problems, demonstrating its broad applicability and actionable insights for improving HPO.
RCMoE: A Communication-Efficient Random Compression Framework for Resource-Constrained Mixture-of-Experts Training
Authors: Donglei Wu; Xiao Cai; Jinglei Tan; Jinda Jia; Guangming Tan; Dingwen Tao; Wen Xia; Zhihong Tian
Venue: AAAI 2026 (main)

Abstract: Mixture-of-Experts (MoE) architecture with experts parallelism scales LLMs efficiently by activating only a subset of experts per input, avoiding proportional training costs. However, the intensive and heterogeneous communication substantially hinders the efficiency and scalability of MoE training in the resource-constrained scenario.  
Existing communication compression techniques fall short in MoE training due to: (i) Intensive training amplifies compression overhead, compromising training efficiency; (ii) Accumulated compression errors propagate through the network, degrading training quality. 
In this paper, we propose RCMoE, a communication-efficient Random Compression framework for MoE training with two core modules:
(1) Local-Stochastic Quantization compresses the all-to-all communication by stochastically quantizing each row of the expert's intermediate computing results in parallel, effectively improving the compression efficiency and reducing compression error; 
(2) Probabilistic Thresholding Sparsification compresses the all-reduce communication by probabilistically sampling large gradients at high probability, thereby reducing the computational complexity and maintaining the convergence efficiency.  
Experiments on four typical MoE training tasks prove that
RCMoE achieves higher 5.9x-8.1x total communication compression ratios and 1.3x-10.1x training speedup compared with the state-of-the-art compression techniques while maintaining the
MoE training accuracy.
A Multi-Objective Optimization Framework for Adaptive Weighting in Physics-Informed Machine Learning
Authors: Guoquan Wu; Zhe Wu
Venue: AAAI 2026 (main)

Abstract: Training physics-informed neural networks (PINNs) can be viewed as a multi-task optimization problem, where data-driven and physics-driven loss functions must be simultaneously minimized, despite the potential competition between them. Manually tuning the weight coefficients for various loss terms in PINNs is often time-consuming and lacks a systematic approach. To address this challenge, this work proposes an adaptive loss balancing framework for PINNs, using multi-objective optimization (MOO) algorithms to dynamically balance competing loss terms during training. Specifically, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is integrated into the PINN training process to explore the Pareto front of the multiple objectives. A novel variance-aware relative improvement (VARI) weighting method is proposed to translate Pareto-optimal information into adaptive loss weights. The proposed MOO-VARI method is validated through several examples, where the results show that the MOO-VARI PINN consistently outperforms standard PINN and other state-of-the-art adaptive weighting strategies in terms of convergence speed, predictive accuracy, and parameter estimation performance.
TawPipe: Topology-Aware Weight Pipeline Parallelism for Accelerating Long-Context Large Models Training
Authors: Houming Wu; Ling Chen
Venue: AAAI 2026 (main)

Abstract: 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, limiting efficiency in long-context training. Recent weight-passing approaches (e.g., WeiPipe) mitigate this by transmitting model weights instead of activations, but suffer from redundant peer-to-peer (P2P) transfers and underutilized intra-node bandwidth. We propose TawPipe—topology-aware weight pipeline parallelism, which exploits hierarchical bandwidth in distributed clusters for improved communication efficiency. TawPipe: (i) groups devices based on topology to optimize intra-node collective and inter-node P2P communication; (ii) assigns each device a fixed shard of model weights and gradients, avoiding redundant transfers; and (iii) overlaps communication with computation to hide latency. Unlike global collective operations used in fully sharded data parallelism (FSDP), TawPipe confines most communication within node boundaries, significantly reducing cross-node traffic. Extensive experiments on up to 24 GPUs with LLaMA‑style models show that TawPipe achieves superior throughput and scalability compared to state-of-the-art baselines.
From Parameter to Representation: A Closed-Form Approach for Controllable Model Merging
Authors: Jialin Wu; Jian Yang; Handing Wang; Jiajun Wen; Zhiyong Yu
Venue: AAAI 2026 (main)

Abstract: Model merging combines expert models for multitask performance but faces challenges from parameter interference. This has sparked recent interest in controllable model merging, giving users the ability to explicitly balance performance trade-offs. Existing approaches employ a compile-then-query paradigm, performing a costly offline multi-objective optimization to enable fast, preference-aware model generation. This offline stage typically involves iterative search or dedicated training, with complexity that grows exponentially with the number of tasks. To overcome these limitations, we shift the perspective from parameter-space optimization to a direct correction of the model's final representation. Our approach models this correction as an optimal linear transformation, yielding a closed-form solution that replaces the entire offline optimization process with a single-step, architecture-agnostic computation. This solution directly incorporates user preferences, allowing a Pareto-optimal model to be generated on-the-fly with complexity that scales linearly with the number of tasks. Experimental results show our method generates a superior Pareto front with more precise preference alignment and drastically reduced computational cost.
Multimodal Mixture-of-Experts with Retrieval Augmentation for Protein Active Site Identification
Authors: Jiayang Wu; Jiale Zhou; Rubo Wang; Xingyi Zhang; Xun Lin; Tianxu Lv; Leong Hou U; Yefeng Zheng
Venue: AAAI 2026 (main)

Abstract: Accurate identification of protein active sites at the residue level is crucial for understanding protein function and advancing drug discovery. However, current methods face two critical challenges: vulnerability in single-instance prediction due to sparse training data, and inadequate modality reliability estimation that leads to performance degradation when unreliable modalities dominate fusion processes. To address these challenges, we introduce Multimodal Mixtureof-Experts with Retrieval Augmentation (MERA), the first
retrieval-augmented framework for protein active site identification. MERA employs hierarchical multi-expert retrieval that dynamically aggregates contextual information from chain, sequence, and active-site perspectives through residuelevel mixture-of-experts gating. To prevent modality degradation, we propose a reliability-aware fusion strategy based on Dempster–Shafer evidence theory that quantifies modality trustworthiness through belief mass functions and learnable discounting coefficients, enabling principled multimodal integration. Extensive experiments on ProTAD-Gen and TS125 datasets demonstrate that MERA achieves state-of-the-art performance, with 90% AUPRC on active site prediction and significant gains on peptide-binding site identification, validating the effectiveness of retrieval-augmented multi-expert modeling and reliability-guided fusion
SELDON: Supernova Explosions Learned by Deep ODE Networks
Authors: Jiezhong Wu; Jack O'Brien; Jennifer Li; M. S. Krafczyk; Ved G. Shah; Amanda R. Wasserman; Daniel W. Apley; Gautham Narayan; Noelle I. Samia
Venue: AAAI 2026 (main)

Abstract: The discovery rate of optical transients will explode to 10 million public alerts per night once the Vera C. Rubin Observatory’s Legacy Survey of Space and Time comes online, overwhelming the traditional physics-based inference pipelines. A continuous-time forecasting AI model is of interest because it can deliver millisecond-scale inference for thousands of objects per day, whereas legacy MCMC codes need hours per object. In this paper, we propose SELDON, a new continuous-time variational autoencoder for panels of sparse and irregularly time-sampled (gappy) astrophysical light curves that are nonstationary, heteroscedastic, and inherently dependent. SELDON combines a masked GRU-ODE encoder with a latent neural ODE propagator and an interpretable Gaussian-basis decoder. The encoder learns to summarize panels of imbalanced and correlated data even when only a handful of points are observed. The neural ODE then integrates this hidden state forward in continuous time, extrapolating to future unseen epochs. This extrapolated time series is further encoded by deep sets to a latent distribution that is decoded to a weighted sum of Gaussian basis functions, the parameters of which are physically meaningful. Such parameters (e.g., rise time, decay rate, peak flux) directly drive downstream prioritization of spectroscopic follow-up for astrophysical surveys. Beyond astronomy, the architecture of SELDON offers a generic recipe for interpretable and continuous-time sequence modeling in any time domain where data are multivariate, sparse, heteroscedastic, and irregularly spaced.
SCoUT: A Framework for Structured Stereotype Analysis in Language Models
Authors: Jinxuan Wu; Bin Li; Xiangyang Xue
Venue: AAAI 2026 (main)

Abstract: Existing stereotype auditing methods for Large Language Models (LLMs) typically rely on isolated rating schemes or task-specific probes, lacking theoretical grounding and failing to reveal internal organization beyond surface-level output patterns. In this paper, we introduce SCoUT (Stereotype Content-oriented Utility structure via Thurstonian modeling), a closed-loop framework that structurally models, explicitly probes, and functionally steers stereotype dimensions (warmth and competence) in LLMs. SCoUT first reconstructs a global stereotype utility structure aligned with Stereotype Content Model theory via Thurstonian comparative judgments. Across multiple open-source LLMs, this modeling achieves high pairwise-preference prediction accuracy (≥ 0.90 on larger-scale models) and exhibits strong cross-model consistency. Probing internal attention mechanisms localizes this structure to specific heads (Spearman’s ρ up to 0.83 for warmth and 0.90 for competence) and surfaces a salient asymmetry between warmth and competence. Further, targeted inference-time activation modifications on these dimension-sensitive heads consistently steer model outputs along the intended axes. By bridging behavioral measurement with internal representation and controllable steering, SCoUT offers an end-to-end framework that uncovers and interprets the latent structure of stereotypes, advancing stereotype auditing from surface detection to structural analysis.
Facial-R1: Aligning Reasoning and Recognition for Facial Emotion Analysis
Authors: Jiulong Wu; Yucheng Shen; Lingyong Yan; Haixin Sun; Deguo Xia; Jizhou Huang; Min Cao
Venue: AAAI 2026 (main)

Abstract: Facial Emotion Analysis (FEA) extends traditional facial emotion recognition by incorporating explainable, fine-grained reasoning. 
The task integrates three subtasks—emotion recognition, facial Action Unit (AU) recognition, and AU-based emotion reasoning—to jointly model affective states. 
While recent approaches leverage Vision-Language Models (VLMs) and achieve promising results, they face two critical limitations: 
(1) hallucinated reasoning, where VLMs generate plausible but inaccurate explanations due to insufficient emotion-specific knowledge; 
and (2) misalignment between emotion reasoning and recognition, caused by fragmented connections between observed facial features and final labels.
We propose Facial-R1, a three-stage alignment framework that effectively addresses both challenges with minimal supervision. 
First, we employ instruction fine-tuning to establish basic emotional reasoning capability for reducing hallucinations. 
Second, we introduce reinforcement training guided by emotion and AU labels as reward signals, which explicitly aligns the generated reasoning process with the predicted emotion. 
Third, we design a data synthesis pipeline that iteratively leverages the prior stages to expand the training dataset, enabling scalable self-improvement of the model.
Built upon this framework, we introduce FEA-20K, a benchmark dataset comprising 17,737 training and 1,688 test samples with fine-grained emotion analysis annotations. 
Extensive experiments across eight standard benchmarks demonstrate that Facial-R1 achieves state-of-the-art performance in FEA, with strong generalization and robust interpretability.