GenAI4ELab/papercli-papers huggingface dataset:
OPTION: An Online Pricing Strategy for Asynchronous Federated Learning Against Free-Riding Attacks
Authors: Bangqi Pan; Jianfeng Lu; Shuqin Cao; Xiao Zhang; Gang Li; Guanghui Wen
Venue: AAAI 2026 (main)

Abstract: Asynchronous Federated Learning (AFL) is acclaimed for accelerating collaborative training on heterogeneous systems by eliminating the wait for stragglers. While current solutions focus on improving convergence amidst update delays, they neglect how delayed aggregation fosters free-riding attacks, allowing malicious clients to easily extract the global model without contribution. This behavior results in significant fairness issues and performance degradation. To address this challenge, we propose OPTION, the first online pricing strategy tailored to mitigate free-riding in AFL. OPTION establishes an economic model in which access to model updates is purchased using credits earned from verified contributions. Specifically, OPTION values each model update according to its marginal performance gain and training cost, and subsequently necessitates a download fee from each client based on the Hotelling model to prevent zero-cost acquisition. Moreover, OPTION rewards clients for successful updates under non-arbitrage constraints, effectively balancing individual utility and task budget. To maximize the average model performance while satisfying these conditions, OPTION leverages the Lyapunov drift framework and a probabilistic sampling-based algorithm to optimize the pricing parameters. Extensive experimental results on three real-world datasets demonstrate that OPTION effectively mitigates freeriding attacks in AFL, increases the number of valid updates by at least 23.97%, and achieves a model accuracy improvement of at least 3.01% compared to state-of-the-art baselines.
Correcting False Alarms from Unseen: Adapting Graph Anomaly Detectors at Test Time
Authors: Junjun Pan; Yixin Liu; Chuan Zhou; Fei Xiong; Alan Wee-Chung Liew; Shirui Pan
Venue: AAAI 2026 (main)

Abstract: Graph anomaly detection (GAD), which aims to detect outliers in graph-structured data, has received increasing research attention recently. However, existing GAD methods assume identical training and testing distributions, which is rarely valid in practice. In real-world scenarios, unseen but normal samples may emerge during deployment, leading to a normality shift that degrades the performance of GAD models trained on the original data. Through empirical analysis, we reveal that the degradation arises from (1) semantic confusion, where unseen normal samples are misinterpreted as anomalies due to their novel patterns, and (2) aggregation contamination, where the representations of seen normal nodes are distorted by unseen normals through message aggregation. While retraining or fine-tuning GAD models could be a potential solution to the above challenges, the high cost of model retraining and the difficulty of obtaining labeled data often render this approach impractical in real-world applications. To bridge the gap, we proposed a lightweight and plug-and-play Test-time adaptation framework for correcting Unseen Normal pattErns (TUNE) in GAD. To address semantic confusion, a graph aligner is employed to align the shifted data to the original one at the graph attribute level. Moreover, we utilize the minimization of representation-level shift as a supervision signal to train the aligner, which leverages the estimated aggregation contamination as a key indicator of normality shift. Extensive experiments on 10 real-world datasets demonstrate that TUNE significantly enhances the generalizability of pre-trained GAD models to both synthetic and real unseen normal patterns.
Re-architecting Personalized Federated Learning for Demanding Edge Environments
Authors: Quyang Pan; Sheng Sun; Tingting Wi; Zhiyuan Wu; Yuwei Wang; Min Liu; Bo Gao; Jingyuan Wang
Venue: AAAI 2026 (main)

Abstract: Federated Edge Learning (FEL) has emerged as a promising approach for enabling edge devices to collaboratively train machine learning models while preserving data privacy. Despite its advantages, practical FEL deployment faces significant challenges related to device constraints and device-server interactions, necessitating heterogeneous, user-adaptive model training with limited and uncertain communication. While knowledge cache-driven federated learning offers a promising FEL solution for demanding edge environments, its logits-based interaction design provides poor richness of exchanged information for on-device model optimization. To tackle this issue, we introduce DistilCacheFL, a novel personalized FEL architecture that enhances the exchange of optimization insights while delivering state-of-the-art performance with efficient communication. DistilCacheFL incorporates the benefits of both dataset distillation and knowledge cache-driven federated learning by storing and organizing distilled data as knowledge in the server-side knowledge cache, allowing devices to periodically download and utilize personalized knowledge for local model optimization. Moreover, a device-centric cache sampling strategy is introduced to tailor transferred knowledge for individual devices within controlled communication bandwidth. Extensive experiments on five datasets covering image recognition, audio understanding, and mobile sensor data mining tasks demonstrate that (1) DistilCacheFL significantly outperforms state-of-the-art methods regardless of model structures, data distributions, and modalities. (2) DistilCacheFL can train splendid personalized on-device models with at least 
28.6 improvement in communication efficiency.
Prototype Entropy Alignment: Reinforcing Structured Uncertainty in LLM Reasoning
Authors: Zhengyuan Pan; Yanhao Chen; Zhongquan Jian; Wanru Zhao; Haonan Ma; Meihong Wang; Qingqiang Wu
Venue: AAAI 2026 (main)

Abstract: Recent research reveals that a minority of high-entropy tokens significantly influence the reasoning quality of large language models (LLMs). Inspired by this, we propose Prototype Entropy Alignment (PEA), a reinforcement learning framework that models effective reasoning not as a single path but as a collection of learnable "entropy signatures." PEA identifies these signatures by clustering expert trajectories' uncertainty patterns into a diverse and continuously updated set of prototypes. The model is then rewarded for aligning its own reasoning process with these evolving targets, creating a self-improvement loop. Instead of replacing traditional outcome-based rewards, PEA provides a complementary, process-oriented signal. Our experiments show that this synergy is crucial: PEA substantially boosts performance on creative and general reasoning tasks and, when combined with outcome rewards, achieves SOTA results on structured tasks such as mathematics. By rewarding alignment with diverse and evolving reasoning structures, PEA offers a robust, verifier-free pathway to enhance reasoning's adaptability.
On the Misalignment Between Data Learnability and Forgettability in Machine Unlearning
Authors: Zijie Pan; Zuobin Ying; Yajie Wang; Wanlei Zhou
Venue: AAAI 2026 (main)

Abstract: We report a structural mismatch between a data point’s {learnability}—how quickly it improves the loss—and its {forgettability}—how much it anchors the final parameters—an aspect ignored by prior machine unlearning frameworks such as SISA, Fisher-Forget, and influence-based fine-tuning.  
To make this gap measurable we introduce Unlearning Gradient Sensitivity (UGS), an influence score computable with a single Hutch++ sketch, and derive the Learnability–Forgettability Divergence (LFD), the Jensen–Shannon distance between the model’s learning and forgetting distributions.  
We prove that UGS dispersion decays exponentially only under explicit regularisation and that LFD converges to zero when its weight grows sub-linearly relative to the UGS term.  
Building on these findings, we introduce Dual-Aware Training (DAT)—a lightweight regularization method that reduces variability in how easily data points can be forgotten and aligns learning and forgetting behaviors during training. On CIFAR-10, MNIST, and IMDB, DAT maintains the original model accuracy while cutting forgettability divergence in half and significantly lowering the cost of certified unlearning, showing that it’s effective to make models forgettable from the start.
Dynamic Sparsity: Challenging Common Sparsity Assumptions for Learning World Models in Robotic Reinforcement Learning Benchmarks
Authors: Muthukumar Pandaram; Jakob Hollenstein; David Drexel; Samuele Tosatto; Antonio Rodriguez-Sanchez; Justus Piater
Venue: AAAI 2026 (main)

Abstract: The use of learned dynamics models, also known as world models, can improve the sample efficiency of reinforcement learning. Recent work suggests that the underlying causal graphs of such dynamics models are sparsely connected, with each of the future state variables depending only on a small subset of the current state variables, and that learning may therefore benefit from sparsity priors. Similarly, temporal sparsity, i.e. sparsely and abruptly changing local dynamics, has also been proposed as a useful inductive bias. In this work, we critically examine these assumptions by analyzing ground truth dynamics from a set of robotic reinforcement learning environments in the MuJoCo Playground benchmark suite, aiming to determine whether the proposed notions of state and temporal sparsity actually tend to hold in typical reinforcement learning tasks. We study (i) whether the causal graphs of environment dynamics are sparse, (ii) whether such sparsity is state-dependent, and (iii) whether local system dynamics change sparsely. Our results indicate that global sparsity is rare, but instead the tasks show local, state-dependent sparsity in their dynamics and this sparsity exhibits distinct structures, appearing in temporally localized clusters (e.g., during contact events) and affecting specific subsets of state dimensions. These findings challenge common sparsity prior assumptions in dynamics learning, emphasizing the need for grounded inductive biases that reflect the state-dependent sparsity structure of real-world dynamics.
Forest vs Tree: The (N, K) Trade-off in Reproducible ML Evaluation
Authors: Deepak Pandita; Flip Korn; Chris Welty; Christopher M Homan
Venue: AAAI 2026 (main)

Abstract: Reproducibility is a cornerstone of scientific validation and of the authority it confers on its results. Reproducibility in machine learning evaluations leads to greater trust, confidence, and value. However, the ground truth responses used in machine learning often necessarily come from humans, among whom disagreement is prevalent, and surprisingly little research has studied the impact of effectively ignoring disagreement in these responses, as is typically the case. One reason for the lack of research is that budgets for collecting human-annotated evaluation data are limited, and obtaining more samples from multiple raters for each example greatly increases the per-item annotation costs. We investigate the trade-off between the number of items (N) and the number of responses per item (K) needed for reliable machine learning evaluation. We analyze a diverse collection of categorical datasets for which multiple annotations per item exist, and simulated distributions fit to these datasets, to determine the optimal (N, K) configuration, given a fixed budget (N x K), for collecting evaluation data and reliably comparing the performance of machine learning models. Our findings show, first, that accounting for human disagreement may come with N x K at no more than 1000 (and often much lower) for every dataset tested on at least one metric. Moreover, this minimal N x K almost always occurred for K > 10. Furthermore, the nature of the tradeoff between K and N, or if one even existed, depends on the evaluation metric, with metrics that are more sensitive to the full distribution of responses performing better at higher levels of K. Our methods can be used to help ML practitioners get more effective test data by finding the optimal metrics and number of items and annotations per item to collect to get the most reliability for their budget.
Decoupling Shared and Personalized Knowledge: A Dual-Branch Federated Learning Framework for Multi-Domain with Non-IID Data
Authors: Yiran Pang; Zhen Ni; Xiangnan Zhong
Venue: AAAI 2026 (main)

Abstract: Federated learning (FL) enables collaborative model training
without centralizing data. In multi-domain scenarios with
non-identically and independently distributed (non-IID) data,
prediction performance is often hindered by catastrophic forgetting
of specialized local knowledge and negative transfer
from conflicting client updates. To address these challenges,
we propose a personalized FL framework with dual-branch
(pFedDB) structure and a two-phase training protocol. The
dual-branch architecture separates the model into a shared
branch for cross-client aggregation and a private branch that
remains on each local client. The private branch is never overwritten
by server updates, which prevents the catastrophic
forgetting of domain-specific knowledge. This structure also
significantly reduces communication overhead per round as
only the shared branch is transmitted. To mitigate negative
transfer, our two-phase protocol first establishes a personalized
knowledge anchor by training a single-branch expert
model on each client’s local data. In the second phase, the locally
trained model is cloned to initialize private and shared
branches. Only the shared branch is aggregated in federated
training. This process enables the shared branch to learn a
general representation that complements the established local
expertise. This design consistently improves the performance
of every client over its single-domain baseline, overcoming
the challenge of negative transfer among clients. Experiments
on our new Chest-X-Ray-4 suite and three public benchmarks
show that the proposed pFedDB method obtains 30% saving
in communication overhead per round and competitive or better
accuracy performance than recent FL methods.
MoCHA: Advanced Vision-Language Reasoning with MoE Connector and Hierarchical Group Attention
Authors: Yuqi Pang; Bowen Yang; Yun Cao; Rong Fan; Xiaoyu Li; Chen He
Venue: AAAI 2026 (main)

Abstract: Vision large language models (VLLMs) are focusing primarily on handling complex and fine-grained visual information by incorporating advanced vision encoders and scaling up visual models. However, these approaches face high training and inference costs, as well as challenges in extracting visual details, effectively bridging across modalities. In this work, we propose a novel visual framework, MoCHA, to address these issues. Our framework integrates four vision backbones (i.e., CLIP, SigLIP, DINOv2 and ConvNeXt) to extract complementary visual features and is equipped with a sparse Mixture of Experts Connectors (MoECs) module to dynamically select experts tailored to different visual dimensions. To mitigate redundant or insufficient use of the visual information encoded by the MoECs module, we further design a Hierarchical Group Attention (HGA) with intra- and inter-group operations and an adaptive gating strategy for encoded visual features. We train MoCHA on two mainstream LLMs (e.g., Phi2-2.7B and Vicuna-7B) and evaluate their performance across various benchmarks. Notably, MoCHA outperforms state-of-the-art open-weight models on various tasks. For example, compared to CuMo (Mistral-7B), our MoCHA (Phi2-2.7B) presents outstanding abilities to mitigate hallucination by showing improvements of 3.25% in POPE and to follow visual instructions by raising 153 points on MME. Finally, ablation studies further confirm the effectiveness and robustness of the proposed MoECs and HGA in improving the overall performance of MoCHA.
Transferable Model-agnostic Vision-Language Model Adaptation for Efficient Weak-to-Strong Generalization
Authors: Jihwan Park; Taehoon Song; Sanghyeok Lee; Miso Choi; Hyunwoo J. Kim
Venue: AAAI 2026 (main)

Abstract: Vision-Language Models (VLMs) have been widely used in various visual recognition tasks due to their remarkable generalization capabilities. 
As these models grow in size and complexity, fine-tuning becomes costly, emphasizing the need to reuse adaptation knowledge from 'weaker' models to efficiently enhance 'stronger' ones.
However, existing adaptation transfer methods exhibit limited transferability across models due to their model-specific design and high computational demands.
To tackle this, we propose Transferable Model-agnostic adapter (TransMiter), a light-weight adapter that improves vision-language models 'without backpropagation'.
TransMiter captures the knowledge gap between pre-trained and fine-tuned VLMs, in an 'unsupervised' manner.
Once trained, this knowledge can be seamlessly transferred across different models without the need for backpropagation.
Moreover, TransMiter consists of only a few layers, inducing a negligible additional inference cost.
Notably, supplementing the process with a few labeled data further yields additional performance gain, often surpassing a fine-tuned stronger model, with a marginal training cost.
Experimental results and analyses demonstrate that TransMiter effectively and efficiently transfers adaptation knowledge while preserving generalization abilities across VLMs of different sizes and architectures in visual recognition tasks.
Fair Model-based Clustering
Authors: Jinwon Park; Kunwoong Kim; Jihu Lee; Yongdai Kim
Venue: AAAI 2026 (main)

Abstract: The goal of fair clustering is to find clusters such that the proportion of sensitive attributes (e.g., gender, race, etc) in each cluster is similar to the proportion of the entire data. Various fair clustering algorithms have been proposed, which modify standard K-means clustering to satisfy a given fairness constraint. A critical limitation of several existing fair clustering algorithms is that the number of parameters to be learned is proportional to the sample size because the cluster assignment of each datum should be optimized simultaneously with the cluster center, and thus scaling up the algorithms is difficult. In this paper, we propose a new fair clustering algorithm based on finite mixture model called Fair Model-based Clustering (FMC). A main advantage of FMC is that the number of learnable parameters is independent to the sample size and thus can be scaled up easily. In particular, a mini-batch learning is possible to obtain clusters that are approximately fair. Moreover, FMC can be applied to non-metric data (e.g., categorical data) as long as the likelihood is well-defined. Theoretical and empirical justifications of the superiority of the proposed algorithm are provided.
ProLoG: Hybrid Prompt and LoRA Based Adaptation of Vision-Language Models for OOD Generalization
Authors: Jungwuk Park; Dong-Jun Han; Jaekyun Moon
Venue: AAAI 2026 (main)

Abstract: While vision-language foundation models (VLMs) achieve remarkable performance when fine-tuned on downstream in-distribution (ID) data, this process compromises their generalization ability on out-of-distribution (OOD) data that deviate from the downstream tasks due to overfitting. To address this, we propose ProLoG, a new adaptation method that  effectively fine-tunes VLMs on downstream tasks while achieving high OOD performance. Specifically, we  design a unique integration of prompt tuning and LoRA, offering a robust hybrid platform to improve performance. During training, we propose an augmentation-based regularization loss that enhances the generalization of our hybrid network by using augmented image features aligned with LLM-generated texts containing key attributes of each class.  By leveraging our hybrid design, we also introduce an adaptive inference strategy that flexibly applies trained prompts and LoRA based on a task similarity score to effectively handle both ID and OOD data. Experimental results demonstrate that our proposed method outperforms existing works on various datasets, confirming its advantages.
How Many Experts Are Enough? Towards Optimal Semantic Specialization for Mixture-of-Experts
Authors: Sumin Park; Noseong Park
Venue: AAAI 2026 (main)

Abstract: Finding the optimal configuration of Sparse Mixture-of-
Experts (SMoE) that maximizes semantic differentiation
among experts is essential for exploiting the full potential
of MoE architectures. However, existing SMoE frameworks
either heavily rely on hyperparameter tuning or overlook the
importance of diversifying semantic roles across experts when
adapting the expert pool size. We propose Mixture-of-Experts
for Adaptive Semantic Specialization (MASS), a semantic-aware MoE framework for adaptive expert expansion and
dynamic routing. MASS introduces two key advancements:
(i) a gradient-based semantic drift detector that prompts targeted expert expansion when the existing expert pool lacks
capacity to capture the full semantic diversity of the data, and
(ii) an integration of adaptive routing strategy that dynamically
adjusts expert usage based on token-level routing confidence
mass. We first demonstrate that MASS reliably converges
to the point of optimal balance between cost-performance
trade-off with notably improved sematic specialization in a
highly controlled synthetic setup. Further empirical results on
real-world datasets across language and vision domains show
that MASS consistently outperforms a range of strong MoE
baselines, demonstrating its domain robustness and enhanced
expert specialization.
What Makes a Good Generated Image? Investigating Human and Multimodal LLM Image Preference Alignment
Authors: Rishab Parthasarathy; Jasmine Collins; Cory Stephenson
Venue: AAAI 2026 (main)

Abstract: Automated evaluation of generative text-to-image models remains a challenging problem. Recent works have proposed using multimodal LLMs to judge the quality of images, but these works offer little insight into how multimodal LLMs make use of concepts relevant to humans, such as image style or composition, to generate their overall assessment. In this work, we study what attributes of an image--specifically aesthetics, lack of artifacts, anatomical accuracy, compositional correctness, object adherence, and style--are important for both LLMs and humans to make judgments on image quality. We first curate a dataset of human preferences using synthetically generated image pairs. We use inter-task correlation between each pair of image quality attributes to understand which attributes are related in making human judgments. Repeating the same analysis with LLMs, we find that the relationships between image quality attributes are much weaker. Finally, we study individual image quality attributes by generating synthetic datasets with a high degree of control for each axis. Humans are able to easily judge the quality of an image with respect to all of the specific image quality attributes (e.g. high vs. low aesthetic image), however we find that some attributes, such as anatomical accuracy, are much more difficult for multimodal LLMs to learn to judge. Taken together, these findings reveal interesting differences between how humans and multimodal LLMs perceive images.