GenAI4ELab/papercli-papers huggingface dataset:
Libra-MIL: Multimodal Prototypes Stereoscopic Infused with Task-specific Language Priors for Few-shot Whole Slide Image Classification
Authors: Zhenfeng Zhuang; Fangyu Zhou; Liansheng Wang
Venue: AAAI 2026 (main)

Abstract: While Large Language Models (LLMs) are emerging as a promising direction in computational pathology, the substantial computational cost of giga-pixel Whole Slide Images (WSIs) necessitates the use of Multi-Instance Learning (MIL) to enable effective modeling. A key challenge is that pathological tasks typically provide only bag-level labels, while instance-level descriptions generated by LLMs often suffer from bias due to a lack of fine-grained medical knowledge. To address this, we propose that constructing task-specific pathological entity prototypes is crucial for learning generalizable features and enhancing model interpretability. Furthermore, existing vision-language MIL methods often employ unidirectional guidance, limiting cross-modal synergy. In this paper, we introduce a novel approach, Multimodal Prototype-based Multi-Instance Learning, that promotes bidirectional interaction through a balanced information compression scheme. Specifically, we leverage a frozen LLM to generate task-specific pathological entity descriptions, which are learned as text prototypes. Concurrently, the vision branch learns instance-level prototypes to mitigate the model's reliance on redundant data. For the fusion stage, we employ the Stereoscopic Optimal Transport (SOT) algorithm, which is based on a similarity metric, thereby facilitating broader semantic alignment in a higher-dimensional space. We conduct few-shot classification and explainability experiments on three distinct cancer datasets, and the results demonstrate the superior generalization capabilities of our proposed method.
Tuning for Two Adversaries: Enhancing the Robustness Against Transfer and Query-Based Attacks Using Hyperparameter Tuning
Authors: Pascal Zimmer; Ghassan Karame
Venue: AAAI 2026 (main)

Abstract: In this paper, we present the first detailed analysis of how training hyperparameters---such as learning rate, weight decay, momentum, and batch size---influence robustness against both transfer-based and query-based attacks. Supported by theory and experiments, our study spans a variety of practical deployment settings, including centralized training, ensemble learning, and distributed training. We uncover a striking dichotomy: for transfer-based attacks, decreasing the learning rate significantly enhances robustness by up to 64%. In contrast, for query-based attacks, increasing the learning rate consistently leads to improved robustness by up to 28% across various settings and data distributions. Leveraging these findings, we explore---for the first time---the training hyperparameter space to jointly enhance robustness against both transfer-based and query-based attacks. Our results reveal that distributed models benefit the most from hyperparameter tuning, achieving a remarkable tradeoff by simultaneously mitigating both attack types more effectively than other training setups.
HouseTune: Two-Stage Floorplan Generation with LLM Assistance
Authors: Ziyang Zong; Guanying Chen; Zhaohuan Zhan; Fengcheng Yu; Guang Tan
Venue: AAAI 2026 (main)

Abstract: This paper proposes a two-stage text-to-floorplan generation framework that combines the reasoning capability of Large Language Models (LLMs) with the generative power of diffusion models. In the first stage, we leverage a Chain-of-Thought (CoT) prompting strategy to guide an LLM in generating an initial layout, Layout-Init, from natural language descriptions, which ensures a user-friendly and intuitive design process. However, Layout-Init may lack precise geometric alignment and fine-grained structural details due to the inherent limitations of LLMs. To address this, in the second stage we propose a Dual-Noise Prior-Preserved Diffusion (DNPP-Diffusion) model to refine Layout-Init into a final floorplan that better adheres to physical constraints and user requirements. By combining LLMs and a dedicated refining model, our approach is able to generate high-quality floorplans without requiring large-scale domain-specific training data. Experimental results demonstrate its advantages in comparison with state of the art methods, and validate its effectiveness in home design applications.
EchoGen: Cycle-Consistent Learning for Unified Layout-Image Generation and Understanding
Authors: Kai Zou; Hongbo Liu; Dian Zheng; Jianxiong Gao; Zhiwei Zhao; Bin Liu
Venue: AAAI 2026 (main)

Abstract: In this work, we present EchoGen, a unified framework for layout-to-image generation and image grounding, capable of generating images with both accurate layout and high fidelity to the text description.(e.g., spatial relationship), and grounding the image robustly at the same time. We believe that image grounding possesses strong text and layout understanding abilities, which can compensate for the corresponding limitations in layout-to-image generation. At the same time, images generated from layouts exhibit high diversity in content, thereby enhancing the robustness of image grounding. Jointly training both tasks within a unified model can promote performance improvements for each. However, we identify that this joint training paradigm encounters several optimization challenges and results in restricted performance. To address these issues, we propose progressive training strategies. First, the Parallel Multi-Task Pre-training (PMTP) stage equips the model with basic abilities for both tasks, leveraging shared tokens to accelerate training. Next, the Dual Joint Optimization (DJO) stage exploits task duality to sequentially integrate the two tasks, enabling unified optimization. Finally, the Cycle RL stage eliminates reliance on visual supervision by using consistency constraints as rewards, significantly enhancing the model’s unified capabilities via the GRPO strategy. Extensive experiments demonstrate state-of-the-art results on both layout-to-image generation and image grounding benchmarks, and reveal clear synergistic gains from optimizing the two tasks together.
Appearance Discrepancy-guided Sequence Hybrid Masking for Robust Scene Text Recognition
Authors: Shihao Zou; Wei Wei; Leyang Xu; Kaihe Xu; Wenfeng Xie
Venue: AAAI 2026 (main)

Abstract: Masked Image Modeling (MIM) has been widely recognized as a powerful self-supervised paradigm for learning general-purpose visual representations. However, standard MIM based on random masking tends to underperform in domain-specific tasks like Scene Text Recognition (STR), due to challenges such as information sparsity and appearance discrepancies caused by partial occlusion or distortion. To address this issue, we propose a novel pre-training framework called Appearance Discrepancy-guided Sequence Hybrid Masking (DSHM), specifically designed to learn robust representations for STR. To this end, we introduce an Appearance Discrepancy Metric that quantifies the discrepancy level of each image patch by measuring its deviation from anisotropic local discrepancy and intra-instance global style discrepancy. The resulting discrepancy scores are utilized in two key components: (1) A Sequence Hybrid Masking strategy, which prioritizes masking high-discrepancy patches in coherent block forms, thereby elevating the pretext task from simple pixel-level completion to more complex structural reasoning; (2) Discrepancy-Conditioned Tokens (DC-Tokens), which encode prior knowledge about patch difficulty into the decoder, enabling an adaptive reconstruction process and improving the model robustness under scenarios with partial occlusion or text distortion. We achieve competitive performance on multiple benchmark datasets, including common benchmarks, Union14M benchmarks, and Chinese benchmarks.
Turbo-VAED: Fast and Stable Transfer of Video-VAEs to Mobile Devices
Authors: Ya Zou; Jingfeng Yao; Siyuan Yu; Shuai Zhang; Wenyu Liu; Xinggang Wang
Venue: AAAI 2026 (main)

Abstract: There is a growing demand for deploying large generative AI models on mobile devices. For recent popular video generative models, however, the Variational AutoEncoder (VAE) represents one of the major computational bottlenecks. Both large parameter sizes and mismatched kernels cause out-of-memory errors or extremely slow inference on mobile devices. To address this, we propose a low-cost solution that efficiently transfers widely used video VAEs to mobile devices. (1) We analyze redundancy in existing VAE architectures and get empirical design insights. By integrating 3D depthwise separable convolutions into our model, we significantly reduce the number of parameters. (2) We observe that the upsampling techniques in mainstream video VAEs are poorly suited to mobile hardware and form the main bottleneck. In response, we propose a decoupled 3D pixel shuffle scheme that slashes end-to-end delay. Building upon these, we develop a universal mobile-oriented VAE decoder, Turbo-VAED. (3) We propose an efficient VAE decoder training method. Since only the decoder is used during deployment, we distill it to Turbo-VAED instead of retraining the full VAE, enabling fast mobile adaptation with minimal performance loss. To our knowledge, our method enables real-time 720p video VAE decoding on mobile devices for the first time. This approach is widely applicable to most video VAEs. When integrated into four representative models, with training cost as low as $95, it accelerates original VAEs by up to 84.5× at 720p resolution on GPUs, uses as low as 17.5% of original parameter count, and retains 96.9% of the original reconstruction quality. Compared to mobile-optimized VAEs, Turbo-VAED achieves a 2.9× speedup in FPS and better reconstruction quality on the iPhone 16 Pro.
HATIR: Heat-Aware Diffusion for Turbulent Infrared Video Super-Resolution
Authors: Yang Zou; Xingyue Zhu; Kaiqi Han; Jun Ma; Xingyuan Li; Zhiying Jiang; Jinyuan Liu
Venue: AAAI 2026 (main)

Abstract: Infrared video has been of great interest in visual tasks under challenging environments, but often suffers from severe atmospheric turbulence and compression degradation. Existing video super-resolution (VSR) methods either neglect the inherent modality gap between infrared and visible images or fail to restore turbulence-induced distortions. Directly cascading turbulence mitigation (TM) algorithms with VSR methods leads to error propagation and accumulation due to the decoupled modeling of degradation between turbulence and resolution. We introduce HATIR, a Heat-Aware Diffusion for Turbulent InfraRed Video Super-Resolution, which injects heat-aware deformation priors into the diffusion sampling path to jointly model the inverse process of turbulent degradation and structural detail loss. Specifically, HATIR constructs a Phasor-Guided Flow Estimator, rooted in the physical principle that thermally active regions exhibit consistent phasor responses over time, enabling reliable turbulence-aware flow to guide the reverse diffusion process. To ensure the fidelity of structural recovery under nonuniform distortions, a Turbulence-Aware Decoder is proposed to selectively suppress unstable temporal cues and enhance edge-aware feature aggregation via turbulence gating and structure-aware attention. We built FLIR-IVSR, the first dataset for turbulent infrared VSR, comprising paired LR-HR sequences from a FLIR T1050sc camera (1024 X 768) spanning 640 diverse scenes with varying camera and object motion conditions. This encourages future research in infrared VSR.
Boosting Adversarial Transferability via Ensemble Non-Attention
Authors: Yipeng Zou; Qin Liu; Jie Wu; Yu Peng; Guo Chen; Hui Zhou; Guanghui Ye
Venue: AAAI 2026 (main)

Abstract: Ensemble attacks integrate the outputs of surrogate models with diverse architectures, which can be combined with various gradient-based attacks to improve adversarial transferability. However, previous work shows unsatisfactory attack performance when transferring across heterogeneous model architectures. The main reason is that the gradient update directions of heterogeneous surrogate models differ widely, making it hard to reduce the gradient variance of ensemble models while making the best of individual model. To tackle this challenge, we design a novel ensemble attack, NAMEA, which for the first time integrates the gradients from the non-attention areas of ensemble models into the iterative gradient optimization process. Our design is inspired by the observation that the attention areas of heterogeneous models vary sharply, thus the non-attention areas of ViTs are likely to be the focus of CNNs and vice versa. Therefore, we merge the gradients respectively from the attention and non-attention areas of ensemble models so as to fuse the transfer information of CNNs and ViTs. Specifically, we pioneer a new way of decoupling the gradients of non-attention areas from those of attention areas, while merging gradients by meta-learning. Empirical evaluations on ImageNet dataset indicate that NAMEA outperforms AdaEA and SMER, the state-of-the-art ensemble attacks by an average of 15.0% and 9.6%, respectively. This work is the first attempt to explore the power of ensemble non-attention in boosting cross-architecture transferability, providing new insights into launching ensemble attacks.
Decoupling What to Count and Where to See for Referring Expression Counting
Authors: Yuda Zou; Zijian Zhang; Yongchao Xu
Venue: AAAI 2026 (main)

Abstract: Referring Expression Counting (REC) extends class-level object counting to the fine-grained subclass-level, aiming to enumerate objects matching a textual expression that specifies both the class and distinguishing attribute. A fundamental challenge, however, has been overlooked: annotation points are typically placed on class-representative locations (e.g., heads), forcing models to focus on class-level features while neglecting attribute information from other visual regions (e.g., legs for ''walking''). To address this, we propose W2-Net, a novel framework that explicitly decouples the problem into ''what to count'' and ''where to see'' via a dual-query mechanism. Specifically, alongside the standard what-to-count (w2c) queries that localize the object, we introduce dedicated where-to-see (w2s) queries. The w2s queries are guided to seek and extract features from attribute-specific visual regions, enabling precise subclass discrimination. Furthermore, we introduce Subclass Separable Matching (SSM), a novel matching strategy that incorporates a repulsive force to enhance inter-subclass separability during label assignment. W2-Net significantly outperforms the state-of-the-art on the REC-8K dataset, reducing counting error by 22.5% (validation) and 18.0% (test), and improving localization F1 by 7% and 8%, respectively.
CoordAR: One-Reference 6D Pose Estimation of Novel Objects via Autoregressive Coordinate Map Generation
Authors: Dexin Zuo; Ang Li; Wei Wang; Wenxian Yu; Danping Zou
Venue: AAAI 2026 (main)

Abstract: Object 6D pose estimation, a crucial task for robotics and augmented reality applications, becomes particularly challenging when dealing with novel objects whose 3D models are not readily available. To reduce dependency on 3D models, recent studies have explored one-reference-based pose estimation, which requires only a single reference view instead of a complete 3D model. However, existing methods that rely on real-valued coordinate regression suffer from limited global consistency due to the local nature of convolutional architectures and face challenges in symmetric or occluded scenarios owing to a lack of uncertainty modeling. We present CoordAR, a novel autoregressive framework for one-reference 6D pose estimation of unseen objects. CoordAR formulates 3D-3D correspondences between the reference and query views as a map of discrete tokens, which is obtained in an autoregressive and probabilistic manner. To enable accurate correspondence regression, CoordAR introduces 1) a novel coordinate map tokenization that enables probabilistic prediction over discretized 3D space; 2) a modality-decoupled encoding strategy that separately encodes RGB appearance and coordinate cues; and 3) an autoregressive transformer decoder conditioned on both position-aligned query features and the partially generated token sequence. With these novel mechanisms, CoordAR significantly outperforms existing methods on multiple benchmarks and demonstrates strong robustness to symmetry, occlusion, and other challenges in real-world tests.
Faster Symmetry Breaking Constraints for Abstract Structures
Authors: Özgür Akgün; Mun See Chang; Ian P. Gent; Christopher Jefferson
Venue: AAAI 2026 (main)

Abstract: In constraint programming and related paradigms, a modeller specifies their problem in a modelling language for a solver to search and return its solution(s). Using high-level modelling languages such as ESSENCE, a modeller may express their problems in terms of abstract structures. These are structures not natively supported by the solvers, and so they have to be transformed into or represented as other structures before solving. For example, nested sets are abstract structures, and they can be represented as matrices in constraint solvers. Many problems contain symmetries and one very common and highly successful technique used in constraint programming is to “break” symmetries, to avoid searching for symmetric solutions. This can speed up the solving process by many orders of magnitude. Most of these symmetry-breaking techniques involve placing some kind of ordering for the variables of the problem, and picking a particular member under the symmetries, usually the smallest. Unfortunately, applying this technique to abstract variables produces a very
large number of complex constraints that perform poorly in practice. In this paper, we demonstrate a new incomplete method of breaking the symmetries of abstract structures by better exploiting their representations. We apply the method in breaking the symmetries arising from indistinguishable objects, a commonly occurring type of symmetry, and show that our method is faster than the previous methods proposed in (Akgün et al. 2025).
Faster Certified Symmetry Breaking Using Orders with Auxiliary Variables
Authors: Markus Anders; Bart Bogaerts; Benjamin Bogø; Arthur Gontier; Wietze Koops; Ciaran McCreesh; Magnus O. Myreen; Jakob Nordström; Andy Oertel; Adrian Rebola-Pardo; Yong Kiam Tan
Venue: AAAI 2026 (main)

Abstract: Symmetry breaking is a crucial technique in modern combinatorial solving, but it is difficult to be sure it is implemented correctly.
The most successful approach to deal with bugs is to make solvers certifying, so that they output not just a solution,  but also a mathematical proof of correctness in a standard format, which can then be checked by a formally verified checker. This requires justifying symmetry reasoning within the proof, but developing efficient methods for this has remained a long-standing open challenge.
A fully general approach was recently proposed, but it relies on encoding lexicographic orders with big integers, which quickly becomes infeasible for large symmetries. In this work, we develop a method for instead encoding orders with auxiliary variables. We show that this leads to orders-of-magnitude speed-ups in both theory and practice by running experiments on proof logging and checking for SAT symmetry breaking using the state-of-the-art satsuma symmetry breaker and the VeriPB proof checking toolchain.
Learning with Structure: Computing Consistent Subsets on Structurally-Regular Graphs
Authors: Aritra Banik; Mano Prakash Parthasarathi; Venkatesh Raman; Diya Roy; Abhishek Sahu
Venue: AAAI 2026 (main)

Abstract: The Minimum Consistent Subset (MCS) problem arises naturally in the context of supervised clustering and instance selection. In supervised clustering, one aims to infer a meaningful partitioning of data using a small labeled subset. However, the sheer volume of training data in modern applications poses a significant computational challenge. The MCS problem formalizes this goal: given a labeled dataset X in a metric space, the task is to compute a smallest subset S of X such that every point in X shares its label with at least one of its nearest neighbors in S.

 Recently, the MCS problem has been extended to graph metrics, where distances are defined by shortest paths. Prior work has shown that MCS remains NP-hard even on simple graph classes like trees, and presented an fixed-parameter tractable (FPT) algorithm parameterized by the number of colors for MCS on trees. This raises the challenge of identifying graph classes that admit algorithms efficient in both input size (n) and the number of colors (c).

 In this work, we study the Minimum Consistent Subset problem on graphs, focusing on two well-established measures: the vertex cover number (vc) and the neighborhood diversity (nd). Specifically, we design efficient algorithms for graphs exhibiting small vc or small nd, which frequently arise in real-world domains characterized by local sparsity or repetitive structure. These parameters are particularly relevant because they capture structural properties that often correlate with the tractability of otherwise hard problems. Graphs with small vertex cover sizes are "almost independent sets", representing sparse interactions, while graphs with small neighborhood diversity exhibit a high degree of symmetry and regularity. Importantly, small neighborhood diversity can occur even in dense graphs, a property frequently observed in domains such as social networks with modular communities or knowledge graphs with repeated relational patterns. Thus, algorithms designed to work efficiently for graphs with small neighborhood diversity are capable of efficiently solving MCS in complex settings where small vertex covers may not exist.

 We show that MCS is FPT when parameterized by the vertex cover number and by neighborhood diversity. In each case, we present an algorithm whose running time is polynomial in n and c, and the non-polynomial part depends solely on the chosen parameter. Notably, our algorithms remain efficient for arbitrarily many colors, as their complexity is polynomially dependent on the number of colors.
Greedily Maximizing Ex-Ante Fairness
Authors: Ruben Becker; Bojana Kodric; Cosimo Vinci
Venue: AAAI 2026 (main)

Abstract: We study a general framework of optimization with the aim to compute fair solutions in settings with a set of agents whose valuations are combined using an aggregation function. The strength of our framework lies (1) in its generality and (2) in the fact that we leverage the power of ex-ante fairness, a concept that has recently gained much attention in the scope of fair allocation and fairness in AI in general. More precisely, in our setting there are n set functions f₁, …, fₙ (e.g., the valuation functions of n agents) that are combined using an aggregation function g (e.g., the minimum, Nash social welfare, p-norm). The power of ex-ante fairness is obtained by allowing as a feasible solution not simply a finite set S, but instead a distribution Π over feasible sets.
The goal in our setting is then to find a probability distribution p in Π that maximizes the value resulting from aggregating (using g) the n expected values of the functions f₁, …, fₙ obtained when sampling a set S according to the distribution p. We stress that this is different from maximizing the expected value of g (ex-post fairness) and typically allows for much fairer solutions. We give three different greedy algorithms for three different settings of this framework and prove that they achieve constant approximation guarantees under certain realistic assumptions. For some of the settings, we show that these approximation guarantees are tight. Specific scenarios that can be modelled using our framework include fair information diffusion in social networks, fair submodular matching problems, and ex-ante versions of item assignment problems.