GenAI4ELab/papercli-papers huggingface dataset:
CrossCheck-Bench: Diagnosing Compositional Failures in Multimodal Conflict Resolution
Authors: Baoliang Tian; Yuxuan Si; Jilong Wang; LingYao Li; Zhongyuan Bao; Zineng Zhou; Tao Wang; Sixu Li; Ziyao Xu; Mingze Wang; Zhouzhuo Zhang; Zhihao Wang; Yi Ke Yun; Ke Tian; Ning Yang; Minghui Qiu
Venue: AAAI 2026 (main)

Abstract: Multimodal Large Language Models are primarily trained and evaluated on aligned image-text pairs, which leaves their ability to detect and resolve real-world inconsistencies largely unexplored. In open-domain applications visual and textual cues often conflict, requiring models to perform structured reasoning beyond surface-level alignment.
We introduce CrossCheck-Bench, a diagnostic benchmark for evaluating contradiction detection in multimodal inputs. The benchmark adopts a hierarchical task framework covering three levels of reasoning complexity and defines seven atomic capabilities essential for resolving cross-modal inconsistencies. CrossCheck-Bench includes 15k question-answer pairs sourced from real-world artifacts with synthetically injected contradictions. The dataset is constructed through a multi-stage annotation pipeline involving more than 450 expert hours to ensure semantic validity and calibrated difficulty across perception, integration, and reasoning.
We evaluate 13 state-of-the-art vision-language models and observe a consistent performance drop as tasks shift from perceptual matching to logical contradiction detection. Most models perform well on isolated entity recognition but fail when multiple clues must be synthesized for conflict reasoning. Capability-level analysis further reveals uneven skill acquisition, especially in tasks requiring multi-step inference or rule-based validation.
Additional probing shows that conventional prompting strategies such as Chain-of-Thought and Set-of-Mark yield only marginal gains. By contrast, methods that interleave symbolic reasoning with grounded visual processing achieve more stable improvements. These results highlight a persistent bottleneck in multimodal reasoning and suggest new directions for building models capable of robust cross-modal verification.
DIAA: A Decoding-Efficient Inference Acceleration Approach for On-Device Large Language Models
Authors: Hao Tian; Sheng Lu; Fuwen Tian; Guangming Cui; Zheng Li; Xuyun Zhang; Quan Z. Sheng; Wanchun Dou
Venue: AAAI 2026 (main)

Abstract: Large Language Models (LLMs) have revolutionized intelligent interactions, enabling mobile applications such as personal assistants on edge devices for local execution. Speculative decoding (SD) has emerged as a promising paradigm to accelerate LLM inference without compromising generation quality, employing a draft-then-verify manner. However, due to the constrained computing and memory resources on edge devices, existing SD works heavily rely on an auxiliary draft model that incurs additional memory burden and hinders the adaptability, as well as static token trees that yield suboptimal inference performance. To this end, we propose DIAA, a Decoding-efficient Inference Acceleration Approach for on-device LLMs. DIAA achieves plug-and-play and model-agnostic inference speedup with memory and computation efficiency for edge devices. Specifically, a pair of lightweight look-up tables (LUTs) is constructed by Top-K token sampling to cache historical tokens and probabilities for rapid candidate drafting. DIAA integrates a dynamic token tree with prior LUTs enabling paralleled verification, updated during decoding process, to adapt the online context. A computation overlap is then employed to pipeline the update operations of token tree, LUTs, and KV cache to improve the computational efficiency. Finally, through extensive experiments implemented on edge platform NVIDIA Jetson, DIAA outperforms existing baselines in generation speed and inference wall-clock time, while incurring minimal memory overhead.
Learning Network Dismantling Without Handcrafted Inputs
Authors: Haozhe Tian; Pietro Ferraro; Robert Noel Shorten; Mahdi Jalili; Homayoun Hamedmoghadam
Venue: AAAI 2026 (main)

Abstract: The application of message-passing Graph Neural Networks has been a breakthrough for important network science problems. However, the competitive performance often relies on using handcrafted structural features as inputs, which increases computational cost and introduces bias into the otherwise purely data-driven network representations. Here, we eliminate the need for handcrafted features by introducing an attention mechanism and utilizing message-iteration profiles, in addition to an effective algorithmic approach to generate a structurally diverse training set of small synthetic networks. Thereby, we build an expressive message-passing framework and use it to efficiently solve the NP-hard problem of Network Dismantling, virtually equivalent to vital node identification, with significant real-world applications. Trained solely on diversified synthetic networks, our proposed model—MIND: Message Iteration Network Dismantler—generalizes to large, unseen real networks with millions of nodes, outperforming state-of-the-art network dismantling methods. Increased efficiency and generalizability of the proposed model can be leveraged beyond dismantling in a range of complex network problems.
Sampling Control for Imbalanced Calibration in Semi-Supervised Learning
Authors: Senmao Tian; Xiang Wei; Shunli Zhang
Venue: AAAI 2026 (main)

Abstract: Class imbalance remains a critical challenge in semi-supervised learning (SSL), especially when distributional mismatches between labeled and unlabeled data lead to biased classification. Although existing methods address this issue by adjusting logits based on the estimated class distribution of unlabeled data, they often handle model imbalance in a coarse-grained manner, conflating data imbalance with bias arising from varying class-specific learning difficulties. To address this issue, we propose a unified framework, SC-SSL, which suppresses model bias through decoupled sampling control. During training, we identify the key variables for sampling control under ideal conditions. By introducing a classifier with explicit expansion capability and adaptively adjusting sampling probabilities across different data distributions, SC-SSL mitigates feature-level imbalance for minority classes. In the inference phase, we further analyze the weight imbalance of the linear classifier and apply post-hoc sampling control with an optimization bias vector to directly calibrate the logits. Extensive experiments across various benchmark datasets and distribution settings validate the consistency and state-of-the-art performance of SC-SSL.
Adaptive and Context-rich Generative Self-supervised Learning on Graphs
Authors: Yijun Tian; Chuxu Zhang; Ziyi Kou; Zheyuan Liu; Xiangliang Zhang; Nitesh V Chawla
Venue: AAAI 2026 (main)

Abstract: Generative self-supervised learning on graphs has emerged as a popular learning paradigm and demonstrated its efficacy in handling non-Euclidean data. However, several remaining issues limit the capability of existing methods: 1) the disregard of uneven node significance in masking, 2) the underutilization of holistic graph information, 3) the ignorance of semantic knowledge in the representation space due to the exclusive use of reconstruction loss in the output space, and 4) the unstable reconstructions caused by the large volume of masked contents. In light of this, we propose ACE-GSL, an adaptive and context-rich graph self-supervised learning framework to address these issues from the perspectives of adaptivity, integrity, complementarity, and consistency. Specifically, we first develop an adaptive feature mask generator to account for the unique significance of nodes and sample informative masks (adaptivity). We then design a ranking-based structure reconstruction objective joint with feature reconstruction to capture holistic graph information and emphasize the topological proximity between neighbors (integrity). After that, we present a bootstrapping-based similarity module to encode the high-level semantic knowledge in the representation space, complementary to the low-level reconstruction in the output space (complementarity). Finally, we build a consistency assurance module to provide reconstruction objectives with extra stabilized consistency targets (consistency). Extensive experiments demonstrate that ACE-GSL achieves state-of-the-art performance over 28 methods on 20 datasets across 3 tasks.
Decomposing Direct and Indirect Biases in Linear Models Under Demographic Parity Constraint
Authors: Bertille Tierny; Arthur Charpentier; Francois Hu
Venue: AAAI 2026 (main)

Abstract: Linear models are widely used in high-stakes decision-making due to their simplicity and interpretability. Yet when fairness constraints such as demographic parity are introduced, their effects on model coefficients, and thus on how predictive bias is distributed across features, remain opaque. Existing approaches on linear models often rely on strong and unrealistic assumptions, or overlook the explicit role of the sensitive attribute, limiting their practical utility for fairness assessment.
We propose a post-processing framework that can be applied on top of any linear model to decompose the resulting bias into direct (sensitive-attribute) and indirect (correlated-features) components. Our method analytically characterizes how demographic parity reshapes each model coefficient, including those of both sensitive and non-sensitive features. This enables a transparent, feature-level interpretation of fairness interventions and reveals how bias may persist or shift through correlated variables.
Our framework requires no model retraining and provides actionable insights for model auditing and mitigation. Experiments on both synthetic and real-world datasets demonstrate that our method captures fairness dynamics missed by prior work, offering a practical and interpretable tool for responsible deployment of linear models.
Data Heterogeneity and Forgotten Labels in Split Federated Learning
Authors: Joana Tirana; Dimitra Tsigkari; David Solans Noguero; Nicolas Kourtellis
Venue: AAAI 2026 (main)

Abstract: In Split Federated Learning (SFL), the clients collaboratively train a model with the help of a server by splitting the model into two parts. Part-1 is trained locally at each client and aggregated by the aggregator at the end of each round. Part-2 is trained at a server that sequentially processes the intermediate activations received from each client. We study the phenomenon of catastrophic forgetting (CF) in SFL in the presence of data heterogeneity. In detail, due to the nature of SFL, local updates of part-1 may drift away from global optima, while part-2 is sensitive to the processing sequence, similar to forgetting in continual learning (CL). Specifically, we observe that the trained model performs better in classes (labels) seen at the end of the sequence. We investigate this phenomenon with emphasis on key aspects of SFL, such as the processing order at the server and the cut layer. Based on our findings, we propose Hydra, a novel mitigation method inspired by multi-head neural networks and adapted for the SFL setting. Extensive numerical evaluations show that Hydra outperforms baselines and methods from the literature.
Efficient Modality Translation via Arbitrary Conditioning and Wasserstein Regularization
Authors: Tomas Tokar; Scott Sanner
Venue: AAAI 2026 (main)

Abstract: The central challenge in multimodal generative modeling lies in accurately approximating the joint data distribution, even when some modalities are missing. Existing multimodal VAEs solve this by designing increasingly complex encoding architectures, relying on modality-specific encoders, factorized posteriors, and custom inference procedures. This restricts their ability to capture relations among modalities by amortizing the encoding parameters. We challenge this paradigm by introducing a model trained for arbitrary conditioning, i.e., generating any modality given a subset of observed modalities and a logical index indicating which modalities are present or missing. This enables a single unified encoder to handle any subset of modalities while capturing inter-modal relationships via a compact, shared posterior. We find that to work efficiently in the multimodal setup, arbitrary conditioning requires replacing the KL divergence with Wasserstein regularization, which allows more dispersed latent embeddings to support learning over diverse data and modality subsets. This key insight exposes a critical deficiency in existing methods, which rely on KL regularization that tends to concentrate individual embeddings near the standard Gaussian prior, despite coming from very diverse subsets of multimodal inputs. We prove that Wasserstein regularization ensures that the aggregate latent distribution -- spanning all conditioning subsets -- aligns with the prior without requiring mixture models or auxiliary inference tricks. Empirically, the proposed model improves cross-modal generation and yields better reconstructions than state-of-the-art multimodal VAEs.
NADIR: Differential Attention Flow for Non-Autoregressive Transliteration in Indic Languages
Authors: Lakshya Tomar; Vinayak Abrol; Puneet Agarwal
Venue: AAAI 2026 (main)

Abstract: In this work, we argue that not all sequence-to-sequence tasks require the strong inductive biases of autoregressive (AR) models. Tasks like multilingual transliteration, code refactoring, grammatical correction or text normalization often rely on local dependencies where the full modeling capacity of AR models can be overkill, creating a trade-off between their high accuracy and high inference latency. While non-autoregressive (NAR) models offer speed, they typically suffer from hallucinations and poor length control. To explore this trade-off, we focus on the multilingual transliteration task in Indic languages and introduce NADIR, a novel NAR architecture designed to strike a balance between speed and accuracy. NADIR integrates a Differential Transformer and a Mixture-of-Experts mechanism, enabling it to robustly model complex character mappings without sequential dependencies. NADIR achieves over a 13× speed-up compared to the state-of-the-art AR baseline. It maintains a competitive mean Character Error Rate of 15.78%, compared to 14.44% for the AR model and 21.88% for a standard NAR equivalent. Importantly, NADIR reduces Repetition errors by 49.53%, Substitution errors by 24.45%, Omission errors by 32.92%, and Insertion errors by 16.87%. This work provides a practical blueprint for building fast and reliable NAR systems, effectively bridging the gap between AR accuracy and the demands of real-time, large-scale deployment.
Efficiently Enhancing Long-term Series Forecasting via Adaptive Lookback with Wavelets
Authors: Suxin Tong; Jingling Yuan
Venue: AAAI 2026 (main)

Abstract: Long-term series forecasting leverages historical observations to predict extended future sequences and plays a crucial role across various domains. However, conventional models relying on fixed-length lookback windows struggle with the inherent dynamic dependencies and multi-scale characteristics of time series data. These fixed windows either introduce noise through excessive length or omit critical patterns when too short, while optimal window sizes vary significantly with tasks and external conditions. To address this, we propose the Adaptive Lookback Window (ALW) framework, a wavelet transform-driven approach for multi-scale adaptive lookback window selection. ALW decomposes time series into distinct frequency components through wavelet transforms, quantifies the contribution of each historical time step via scale-specific attention mechanisms, and dynamically determines optimal window lengths through backward information accumulation and soft truncation techniques. Finally, refined input features are generated for downstream prediction models via a weighted reconstruction process. Extensive evaluations across multiple public benchmarks demonstrate that ALW, as an efficient plug-and-play technique, not only reduces the MSE of backbone models by an average of 3.2% but also alleviates hyperparameter tuning requirements and enables input feature dimensionality reduction, which curtails subsequent model computational costs.
Meta-GAIN for Missing Data Imputation
Authors: Tao Tong; Xiaofeng Zhu; Jiangzhang Gan
Venue: AAAI 2026 (main)

Abstract: Although previous deep imputation methods (eg., Generative Adversarial  Network (GAN) based methods) have been widely designed to impute missing data, they still suffer from the issues, ie., lack of the imputation diversity and  the generalization ability. In this paper, we propose a new GAN-based imputation method, namely Meta-based Generative Adversarial Imputation Network (Meta-GAIN), to investigate a  new generator for achieving diverse imputation and generalization ability. Specifically, we employ the Kullback-Leibler (KL) divergence  to achieve the imputation diversity by  generating a continuous embedding space of the original data. We also design a task regularizer  to suppress redundant features and capture a more authentic distribution, thus enhancing the generalization ability of the imputation model. Moreover, we theoretically prove that our proposed regularizer achieves the generalization ability. In addition, we design a new meta network to efficient optimize our objective function as well as to improve imputation diversity. Experimental results  on real datasets show that our  method outperforms all comparison methods under different missing mechanisms in terms of imputation  and classification performance.
Video Echoed in Music: Semantic, Temporal, and Rhythmic Alignment for Video-to-Music Generation
Authors: Xinyi Tong; Yiran Zhu; Jishang Chen; Chunru Zhan; Tianle Wang; Sirui Zhang; Nian Liu; Tiezheng Ge; Duo XU; Xin Jin; Feng Yu; Song-Chun Zhu
Venue: AAAI 2026 (main)

Abstract: Video-to-Music generation seeks to generate musically appropriate background music that enhances audiovisual immersion for videos. However, current approaches suffer from two critical limitations: 1) incomplete representation of video details, leading to weak alignment, and 2) inadequate temporal and rhythmic correspondence, particularly in achieving precise beat synchronization. To address the challenges, we propose Video Echoed in Music (VeM), a latent music diffusion that generates high-quality soundtracks with semantic, temporal, and rhythmic alignment for input videos. To capture video details comprehensively, VeM employs a hierarchical video parsing that acts as a music conductor, orchestrating multi-level information across modalities. Modality-specific encoders, coupled with a storyboard-guided cross-attention mechanism (SG-CAtt), integrate semantic cues while maintaining temporal coherence through position and duration encoding. For rhythmic precision, the frame-level transition-beat aligner and adapter (TB-As) dynamically synchronize visual scene transitions with music beats. We further contribute a novel video-music paired dataset sourced from e-commerce advertisements and video-sharing platforms, which imposes stricter transition-beat synchronization requirements. Meanwhile, we introduce novel metrics tailored to the task. Experimental results demonstrate superiority, particularly in semantic relevance and rhythmic precision.
Post Training Quantization for Efficient Dataset Condensation
Authors: Linh-Tam Tran; Sung-Ho Bae
Venue: AAAI 2026 (main)

Abstract: Dataset Condensation (DC) distills knowledge from large datasets into smaller ones, accelerating training and reducing storage requirements. However, despite notable progress, prior methods have largely overlooked the potential of quantization for further reducing storage costs. In this paper, we take the first step to explore post-training quantization in dataset condensation, demonstrating its effectiveness in reducing storage size while maintaining representation quality without requiring expensive training cost. However, we find that at extremely low bit-widths (e.g., 2-bit), conventional quantization leads to substantial degradation in representation quality, negatively impacting the networks trained on these data. To address this, we propose a novel patch-based post-training quantization approach that ensures localized quantization with minimal loss of information. To reduce the overhead of quantization parameters, especially for small patch sizes, we employ quantization-aware clustering to identify similar patches and subsequently aggregate them for efficient quantization. Furthermore, we introduce a refinement module to align the distribution between original images and their dequantized counterparts, compensating for quantization errors. Our method is a plug-and-play framework that can be applied to synthetic images generated by various DC methods. Extensive experiments across diverse benchmarks including CIFAR-10/100, Tiny ImageNet, and ImageNet subsets demonstrate that our method consistently outperforms prior works under the same storage constraints. Notably, our method doubles the test accuracy of existing methods at extreme compression regimes (e.g., from 26.0% to 54.1% for DM at IPC=1), while operating directly on 2-bit images without additional distillation.
Robust SDE Parameter Estimation Under Missing Time Information Setting
Authors: Van Long Tran; Truyen Tran; Phuoc Nguyen
Venue: AAAI 2026 (main)

Abstract: Recent advances in stochastic differential equations (SDEs) have enabled robust modeling of real-world dynamical processes across diverse domains, such as finance, health, and systems biology. However, parameter estimation for SDEs typically relies on accurately time-stamped observational data. When temporal ordering information is corrupted, missing, or deliberately hidden (e.g., for privacy), existing estimation methods often fail. In this paper, we investigate the conditions under which temporal order can be recovered and introduce a novel framework that simultaneously reconstructs temporal information and estimates SDE parameters. Our approach exploits asymmetries between forward and backward processes, deriving a score-matching criterion to infer the correct temporal order between pairs of observations. We then recover the total order via a sorting procedure and estimate SDE parameters from the reconstructed sequence using maximum likelihood. Finally, we conduct extensive experiments on synthetic and real-world datasets to demonstrate the effectiveness of our method, extending parameter estimation to settings with missing temporal order and broadening applicability in sensitive domains.
Matrix-Free Two-to-Infinity and One-to-Two Norms Estimation
Authors: Askar Tsyganov; Evgeny Frolov; Sergey Samsonov; Maxim Rakhuba
Venue: AAAI 2026 (main)

Abstract: In this paper, we propose new randomized algorithms for estimating the two-to-infinity and one-to-two norms in a matrix-free setting, using only matrix-vector multiplications. Our methods are based on appropriate modifications of Hutchinson's diagonal estimator and its Hutch++ version. We provide oracle complexity bounds for both modifications. We further illustrate the practical utility of our algorithms for Jacobian-based regularization in deep neural network training on image classification tasks. We also demonstrate that our methodology can be applied to mitigate the effect of adversarial attacks in the domain of recommender systems.