GenAI4ELab/papercli-papers huggingface dataset:
Policy Zooming: Adaptive Discretization-based Infinite-Horizon Average-Reward Reinforcement Learning
Authors: Avik Kar; Rahul Singh
Venue: AAAI 2026 (main)

Abstract: We study infinite-horizon average-reward reinforcement learning for continuous space Lipschitz Markov decision processes (MDPs) in which an agent can play policies from a given set Φ. The proposed algorithms efficiently explore the policy space by “zooming” into the “promising regions” of Φ, thereby achieving adaptivity gains in the performance. We upper bound the regret as O ̃(T^(1-d_(eff.)^(-1) ) ), where d_(eff.) = d_z^Φ+2 for our model-free algorithm PZRL-MF and d_(eff.) = 2d_S + d_z^Φ+ 3 for our model-based algorithm PZRL-MB. Here, d_S is the dimension of the state space, and d_z^Φ is the zooming dimension given a set of policies Φ. d_z^Φ is an alternative measure of the complexity of the problem, and it depends on the underlying MDP as well as on Φ. Hence, the proposed algorithms exhibit low regret in case the problem instance is benign and/or the agent competes against a low-complexity Φ (that has a small d_z^Φ). When specialized to the case of finite-dimensional policy space, we obtain that d_(eff.) scales as the dimension of this space under mild technical conditions; and also obtain d_(eff.) = 2, or equivalently O ̃(√T) regret for PZRL-MF, under a curvature condition on the average reward function that is commonly used in the multi-armed bandit (MAB) literature.
Detecting the Future: All-at-Once Event Sequence Forecasting with Horizon Matching
Authors: Ivan Karpukhin; Andrey Savchenko
Venue: AAAI 2026 (main)

Abstract: Long-horizon events forecasting is a crucial task across various domains, including retail, finance, healthcare, and social networks. Traditional models for event sequences often extend to forecasting on horizon using an autoregressive (recursive) multi-step strategy, which has limited effectiveness due to typical convergence to constant or repetitive outputs. To address this limitation, we introduce DEF, a novel approach for simultaneous forecasting of multiple future events on a horizon with high accuracy and diversity. Our method optimally aligns predictions with ground truth events during training by using a novel matching-based loss function. We establish a new state-of-the-art in long-horizon event prediction, achieving up to a 50% relative improvement over existing temporal point processes and event prediction models. Furthermore, we achieve state-of-the-art performance in next-event prediction tasks while demonstrating high computational efficiency during inference.
GLANCE: Global Actions in a Nutshell for Counterfactual Explainability
Authors: Loukas Kavouras; Eleni Psaroudaki; Konstantinos Tsopelas; Dimitrios Rontogiannis; Nikolaos Theologitis; Dimitris Sacharidis; Giorgos Giannopoulos; Dimitrios Tomaras; Kleopatra Markou; Dimitrios Gunopulos; Dimitris Fotakis; Ioannis Emiris
Venue: AAAI 2026 (main)

Abstract: The widespread deployment of machine learning systems in critical real-world decision-making applications has highlighted the urgent need for counterfactual explainability methods that operate effectively. Global counterfactual explanations, expressed as actions to offer recourse, aim to provide succinct explanations and insights applicable to large population subgroups. High effectiveness, measured by the fraction of the population that is provided recourse, ensures that the actions benefit as many individuals as possible. Keeping the cost of actions low ensures the proposed recourse actions remain practical and actionable. Limiting the number of actions that provide global counterfactuals is essential to maximize interpretability. The primary challenge, therefore, is to balance these trade-offs—maximizing effectiveness, minimizing cost, while maintaining a small number of actions. We introduce GLANCE, a versatile and adaptive algorithm that employs a novel agglomerative approach, jointly considering both the feature space and the space of counterfactual actions, thereby accounting for the distribution of points in a way that aligns with the model's structure.  This design enables the careful balancing of the trade-offs among the three key objectives, with the size objective functioning as a tunable parameter to keep the actions few and easy to interpret. Our extensive experimental evaluation demonstrates that  GLANCE consistently shows greater robustness and performance compared to existing methods across various datasets and models.
Step-by-step Layered Design Generation
Authors: Faizan Farooq Khan; Joseph K J; Koustava Goswami; Mohamed Elhoseiny; Balaji Vasan Srinivasan
Venue: AAAI 2026 (main)

Abstract: Design generation, in its essence, is a step-by-step process where designers progressively refine and enhance their work through careful modifications. Despite this fundamental characteristic, existing approaches mainly treat design synthesis as a single-step generation problem, significantly underestimating the inherent complexity of the creative process. To bridge this gap, we propose a novel problem setting called Step-by-step Layered Design Generation, that tasks a machine learning model to generate a design, adhering to a sequence of instructions from a designer. Leveraging the recent advancements in Multi-modal LLMs, we propose SLEDGE: Step-by-step LayEred Design GEnerator to model each update to a design as an atomic layered change over its previous state, while being grounded on the instruction.To complement our new problem setting, we introduce a new evaluation suite, including a dataset and a benchmark. Our exhaustive experimental analysis and comparison with state-of-the-art approaches adapted to our new setup bring out the efficacy of our approach. We hope our work will attract attention to this pragmatic and under-explored research area.
BD-Net: Has Depth-Wise Convolution Ever Been Applied in Binary Neural Networks?
Authors: DoYoung Kim; Jin-Seop Lee; Noo-ri Kim; SungJoon Lee; Jee-Hyong Lee
Venue: AAAI 2026 (main)

Abstract: Recent advances in model compression have highlighted the potential of low-bit precision techniques, with Binary Neural Networks (BNNs) attracting attention for their extreme efficiency. However, extreme quantization in BNNs limits representational capacity and destabilizes training, posing significant challenges for lightweight architectures with depth-wise convolutions. To address this, we propose a 1.58-bit convolution to enhance expressiveness and a pre-BN residual connection to stabilize optimization by improving the Hessian condition number. These innovations enable, to the best of our knowledge, the first successful binarization of depth-wise convolutions in BNNs. Our method achieves 33M OPs on ImageNet with MobileNet V1, establishing a new state-of-the-art in BNNs by outperforming prior methods with comparable OPs. Moreover, it consistently outperforms existing methods across various datasets, including CIFAR-10, CIFAR-100, STL-10, Tiny ImageNet, and Oxford Flowers 102, with accuracy improvements of up to 9.3 percentage points.
TabFlash: Efficient Table Understanding with Progressive Question Conditioning and Token Focusing
Authors: Jongha Kim; Minseong Bae; Sanghyeok Lee; Jinsung Yoon; Hyunwoo J. Kim
Venue: AAAI 2026 (main)

Abstract: Table images present unique challenges for effective and efficient understanding due to the need for question-specific focus and the presence of redundant background regions.
Existing Multimodal Large Language Model (MLLM) approaches often overlook these characteristics, resulting in uninformative and redundant visual representations.
To address these issues, we aim to generate visual features that are both informative and compact for improved table understanding.
We first propose progressive question conditioning, which injects the question into Vision Transformer layers with gradually increasing frequency, considering each layer’s capacity to handle additional information, to generate question-aware visual features.
To reduce redundancy, we introduce a pruning strategy that discards background tokens, thereby improving efficiency.
To mitigate information loss from pruning, we further propose token focusing, a training strategy that encourages the model to concentrate essential information in the retained tokens.
By combining these approaches, we present TabFlash, an efficient and effective MLLM for table understanding.
TabFlash achieves state-of-the-art performance, outperforming both open-source and proprietary MLLMs, while requiring 27% less FLOPs and 30% less memory usage compared to the second-best MLLM.
A Catalyst Framework for the Quantum Linear System Problem via the Proximal Point Algorithm
Authors: Junhyung Lyle Kim; Nai-Hui Chia; Anastasios Kyrillidis
Venue: AAAI 2026 (main)

Abstract: Solving systems of linear equations is a fundamental problem, but it can be computationally intensive for classical algorithms in high dimensions. Existing quantum algorithms can achieve exponential speedups for the quantum linear system problem (QLSP) in terms of the problem dimension, but the advantage is bottlenecked by condition number of the coefficient matrix. In this work, we propose a new quantum algorithm for QLSP inspired by the classical proximal point algorithm (PPA). Our proposed method can be viewed as a meta-algorithm that allows inverting a modified matrix via an existing QLSP solver, thereby directly approximating the solution vector instead of approximating the inverse of the coefficient matrix. By carefully choosing the step size eta, the proposed algorithm can effectively precondition the linear system to mitigate the dependence on condition numbers that hindered the applicability of previous approaches. Importantly, this is the first iterative framework for QLSP where a tunable parameter eta and initialization x_0 allows controlling the trade-off between the runtime and approximation error.
Client-level Active Error Correction in Distributed Learning
Authors: Kwang In Kim
Venue: AAAI 2026 (main)

Abstract: Label errors can significantly degrade model performance, making effective mechanisms crucial. Active error correction (AEC) addresses this by prioritizing data points for human re-labeling where corrections are expected to have significant impact. We extend AEC to distributed collaborative learning, where clients hold local data and a central server allocates labeling resources. Existing AEC methods assume centralized access and do not generalize to distributed settings. To overcome this, we use neural network weight gradients from client updates as proxies for local data and apply a Gaussian process in gradient space to strategically select clients for correction. Our method identifies gradient inconsistencies and encourages diversity through a computationally efficient rank-one Cholesky update. Experiments on eight benchmark datasets demonstrate the effectiveness of our approach.
Feature-Centric Unsupervised Node Representation Learning Without Homophily Assumption
Authors: Sunwoo Kim; Soo Yong Lee; Kyungho Kim; Hyunjin Hwang; Jaemin Yoo; Kijung Shin
Venue: AAAI 2026 (main)

Abstract: Unsupervised node representation learning aims to obtain meaningful node embeddings without relying on node labels. To achieve this, graph convolution, which aggregates information from neighboring nodes, is commonly employed to encode node features and graph topology. However, excessive reliance on graph convolution can be suboptimal—especially in non-homophilic graphs—since it may yield unduly similar embeddings for nodes that differ in their features or topological properties. As a result, adjusting the degree of graph convolution usage has been actively explored in supervised learning settings, whereas such approaches remain underexplored in unsupervised scenarios. To tackle this, we propose FUEL, which adaptively learns the adequate degree of graph convolution usage by aiming to enhance intra-class similarity and inter-class separability in the embedding space. Since classes are unknown, FUEL leverages node features to identify node clusters and treats these clusters as proxies for classes. Through extensive experiments using 15 baseline methods and 14 benchmark datasets, we demonstrate the effectiveness of FUEL in downstream tasks, achieving state-of-the-art performance across graphs with diverse levels of homophily.
Physics-Informed Approach for Exploratory Hamilton–Jacobi–Bellman Equations via Policy Iterations
Authors: Yeongjong Kim; Namkyeong Cho; Minseok Kim; Yeoneung Kim
Venue: AAAI 2026 (main)

Abstract: We propose a mesh-free policy iteration framework based on physics-informed neural networks (PINNs) for solving entropy-regularized stochastic control problems. The method iteratively alternates between soft policy evaluation and improvement using automatic differentiation and neural approximation, without relying on spatial discretization. We present a detailed error analysis that decomposes the total approximation error into three sources: iteration error, policy network error, and PDE residual error. The proposed algorithm is validated with a range of challenging control tasks, including high-dimensional linear-quadratic regulation in 5D and 10D, as well as nonlinear systems such as pendulum and cartpole problems. Numerical results confirm the scalability, accuracy, and robustness of our approach across both linear and nonlinear benchmarks.
Robust Detection of Synthetic Tabular Data Under Schema Variability
Authors: G. Charbel N. Kindji; Elisa Fromont; Lina M. Rojas-Barahona; Tanguy Urvoy
Venue: AAAI 2026 (main)

Abstract: The rise of powerful generative models has sparked concerns over data authenticity. While detection methods have been extensively developed for images and text, the case of tabular data, despite its ubiquity, has been largely overlooked. Yet, detecting synthetic tabular data is especially challenging due to its heterogeneous structure and unseen formats at test time. We address the underexplored task of detecting synthetic tabular data "in the wild", i.e. when the detector is deployed on tables with variable and previously unseen schemas. We introduce a novel datum-wise transformer architecture that significantly outperforms the only previously published baseline, improving both AUC and accuracy by 7 points. By incorporating a table-adaptation component, our model gains an additional 7 accuracy points, demonstrating enhanced robustness. This work provides the first strong evidence that detecting synthetic tabular data in real-world conditions is feasible, and demonstrates substantial improvements over previous approaches. The code will be made available in the extended version.
Object-Centric Latent Action Learning
Authors: Albina Klepach; Alexander Nikulin; Ilya Zisman; Denis Tarasov; Alexander Derevyagin; Andrei Polubarov; Nikita Lyubaykin; Igor Kiselev; Vladislav Kurenkov
Venue: AAAI 2026 (main)

Abstract: Leveraging vast amounts of unlabeled internet video data for embodied AI is currently bottlenecked by the lack of action labels and the presence of action-correlated visual distractors. Although recent latent action policy optimization (LAPO) has shown promise in inferring proxy action labels from visual observations, its performance degrades significantly when distractors are present. To address this limitation, we propose a novel object-centric latent action learning framework that centers on objects rather than pixels. We leverage self-supervised object-centric pretraining to disentangle the movement of the agent and distracting background dynamics. This allows LAPO to focus on task-relevant interactions, resulting in more robust proxy-action labels, enabling better imitation learning and efficient adaptation of the agent with just a few action-labeled trajectories. We evaluated our method in eight visually complex tasks across the Distracting Control Suite (DCS) and Distracting MetaWorld (DMW). Our results show that object-centric pretraining mitigates the negative effects of distractors by 50%, as measured by downstream task performance: average return (DCS) and success rate (DMW).
On Logical Extrapolation for Mazes with Recurrent and Implicit Networks
Authors: Brandon Knutson; Amandin Chyba Rabeendran; Michael Ivanitskiy; Jordan Pettyjohn; Cecilia Diniz Behn; Samy Wu Fung; Daniel McKenzie
Venue: AAAI 2026 (main)

Abstract: Recent work suggests that certain neural network architectures — particularly recurrent neural networks (RNNs) and implicit neural networks (INNs) — are capable of logical extrapolation. When trained on easy instances of a task, these networks (henceforth: logical extrapolators) can generalize to more difficult instances. Previous research has hypothesized that logical extrapolators do so by learning a scalable, iterative algorithm for the given task which converges to the solution. We examine this idea more closely in the context of a single task: maze solving. By varying test data along multiple axes — not just maze size — we show that models introduced in prior work fail in a variety of ways, some expected and others less so. It remains uncertain whether any of these models has truly learned an algorithm. However, we provide evidence that a certain RNN has approximately learned a form of `deadend-filling'. We show that training these models on more diverse data addresses some failure modes but, paradoxically, does not improve logical extrapolation. We also analyze convergence behavior, and show that models explicitly trained to converge to a fixed point are likely to do so when extrapolating, while models that are not may exhibit more exotic limiting behavior such as limit cycles, even when they correctly solve the problem. Our results (i) show that logical extrapolation is not immune to the problem of goal misgeneralization, and (ii) suggest that analyzing the dynamics of extrapolation may yield insights into designing better logical extrapolators.
LatentLLM: Activation-Aware Transform to Multi-Head Latent Attention
Authors: Toshiaki Koike-Akino; Xiangyu Chen; Jing Liu; Ye Wang; Pu (Perry) Wang; Matthew Brand
Venue: AAAI 2026 (main)

Abstract: Modern foundation models such as large language models (LLMs) require a massive amount of computational and memory resources. We propose a new framework to convert such LLMs into a reduced-dimension latent structure. Our method extends a local activation-aware tensor decomposition to a global attention-aware joint tensor decomposition. Our framework can significantly improve the model accuracy over the existing model compression methods when reducing the latent dimension to realize computationally/memory-efficient LLMs. We show the benefit on several benchmark including multi-modal reasoning tasks.
Time Series Forecasting via Direct Per-Step Probability Distribution Modeling
Authors: Linghao Kong; Xiaopeng Hong
Venue: AAAI 2026 (main)

Abstract: Deep neural network-based time series prediction models have recently demonstrated superior capabilities in capturing complex temporal dependencies. However, it is challenging for these models to account for uncertainty associated with their predictions, because they directly output scalar values at each time step. To address such a challenge, we propose a novel model named interleaved dual-branch Probability Distribution Network (interPDN), which directly constructs discrete probability distributions per step instead of a scalar. The regression output at each time step is derived by computing the expectation of the predictive distribution on a predefined support set. To mitigate prediction anomalies, a dual-branch architecture is introduced with interleaved support sets, augmented by coarse temporal-scale branches for long-term trend forecasting. Outputs from another branch are treated as auxiliary signals to impose self-supervised consistency constraints on the current branch's prediction. Extensive experiments on multiple real-world datasets demonstrate the superior performance of interPDN.
Principled Analysis of Deep Reinforcement Learning Evaluation and Design Paradigms
Authors: Ezgi Korkmaz
Venue: AAAI 2026 (main)

Abstract: Starting from the utilization of deep neural networks to approximate the state-action value function that led to winning one of the most challenging games, to algorithmic advancements that allowed solving problems without even explicitly stating the rules of the challenge at hand, reinforcement learning research has been the center of remarkable scientific progress for the past decade. In this paper, we focus on the key ingredients of this research progress and we analyze the canonical evaluation and design paradigms in reinforcement learning.We introduce the theoretical foundations of scaling laws in reinforcement learning and show thatthe asymptotic performance of reinforcement learning algorithms does not have a monotone relationship between performance rankings and data-regimes. We conduct large-scale experiments and our results demonstrate that a line of reinforcement learning research under the canonical design and evaluation paradigms resulted in incorrect conclusions. Our analysis and results provide a core analysis on scaling, capacity and complexity of deep reinforcement learning.
Exploiting Missing Data Remediation Strategies Using Adversarial Missingness Attacks
Authors: Deniz Koyuncu; Alex Gittens; Bülent Yener; Moti Yung
Venue: AAAI 2026 (main)

Abstract: Adversarial Missingness (AM) attacks aim to manipulate model fitting by carefully engineering a missing data problem to achieve a specific malicious objective. AM attacks are significantly different from prior data poisoning attacks in that no malicious data inserted and no data is maliciously perturbed. Current AM attacks are feasible only under the assumption that the modeler (victim) uses full-information maximum likelihood methods to handle missingness. This work aims to remedy this limitation of AM attacks; in the approach taken here, the adversary achieves their goal by solving a bi-level optimization problem to engineer the adversarial missingness mechanism, where the lower level problem incorporates a differentiable approximation of the targeted missingness remediation technique. As instantiations of this framework, AM attacks are provided for three popular techniques: (i) complete case analysis, (ii) mean imputation, and (iii) regression-based imputation for general empirical risk minimization (ERM) problems. Experiments on real-world data show that AM attacks are successful with modest levels of missingness (less than 20%). Furthermore, we show on the real-world Twins dataset that AM attacks can manipulate  the estimated average treatment effect (ATE) as an instance of the general ERM problems: the adversary succeeds in not only reversing the sign, but also in substantially inflating the ATE values from a true value of -1.61% to a manipulated one as high as 10%. These experimental results hold when the ATE is calculated using multiple regression-based estimators with different architectures, even when the adversary is restricted to modifying only a subset of the training data. The goals of this work are to: (i) establish the vulnerability to AM attacks of a significantly wider class of missingness remediation strategies than established in prior work, and (ii) brings the AM threat model to the attention of the community, as there are currently no defense strategies for these attacks.