Singapore

Recent diffusion-based models have significantly improved inpainting quality.
However, existing methods struggle with multi-task inpainting due to conflicting optimization objectives, and current datasets are typically limited to task-specific scenarios, hindering joint training.
To address these challenges, we propose \textbf{MagicPaint}, a unified diffusion-based inpainting model that supports object addition, removal, and unconditional inpainting across both text and image modalities.
MagicPaint semantically decouples operation types and target content by learnable tokens in \textbf{MMToken Module}, effectively reconciling conflicting optimization objectives and enabling robust multi-task, multi-modal inpainting.
Besides, a novel inpainting paradigm named \textbf{MagicMask}, encodes operating intent directly into the mask and applies a mask loss for spatially precise supervision.
In addition, existing inpainting datasets are insufficient for multi-task and multi-modal scenarios, limiting the capability of inpainting models. Thus, we further introduce
a new dataset comprising 2.1M image tuples. It is dedicatedly designed to support diverse inpainting scenarios and significantly improves upon existing datasets, particularly in object removal. 
Through efforts from both model and data perspectives, \textbf{MagicPaint} enables users to operate anything—add, remove or inpaint content which is specified through either text or image modalities in a seamless and unified manner.
Extensive experiments demonstrate that MagicPaint achieves state-of-the-art performance across three key tasks (i.e., text-guided addition, image-guided addition, and object removal) and produces outputs with superior visual consistency and contextual fidelity compared to existing methods. Our code and data will be publicly released.

AAAI 2026

MagicPaint: Operate Anything for Image Inpainting with Diffusion Model

cv: diffusion models for vision

cv: multi-modal vision

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.<br><br>

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

poster

Temporal reasoning is a fundamental capability for large language models (LLMs) to understand real-world dynamics. Existing research on temporal reasoning has predominantly focused on the Gregorian calendar. However, as many countries and regions concurrently adopt multiple calendar systems, temporal reasoning across calendars becomes crucial for LLMs in global and multicultural contexts. Unfortunately, cross-calendar temporal reasoning remains underexplored, with no dedicated benchmark available to evaluate this capability.
To bridge this gap, we introduce SPAN, a cross-calendar temporal reasoning benchmark, which requires LLMs to perform intra-calendar temporal reasoning and inter-calendar temporal conversion. SPAN features 10 cross-calendar temporal reasoning directions, two reasoning types, and two question formats, involving the Gregorian, Chinese lunar, Shaka, Hebrew, Islamic, and Persian calendars. To enable time-variant and contamination-free evaluation, we propose a template-driven evaluation protocol for dynamic instance generation, which allows assessment on a user-specified Gregorian date. We conduct extensive experiments on both open- and closed-source state-of-the-art (SOTA) LLMs over a range of dates spanning 100 years from 1960 to 2060. Our evaluations show that these LLMs achieve an average accuracy of only 34.5%, with none exceeding 80%, indicating that this task remains challenging. Through in-depth analysis of reasoning types, question formats, and temporal reasoning directions, we identify two key obstacles for LLMs: Future-Date Degradation and Calendar Asymmetry Bias. 
To strengthen LLMs' cross-calendar temporal reasoning capability, we further develop an LLM-powered Time Agent that leverages tool-augmented code generation. Empirical results show that Time Agent achieves an average accuracy of 95.31%, outperforming several competitive baselines, highlighting the potential of tool-augmented code generation to advance cross-calendar temporal reasoning. We hope this work will inspire further efforts toward more temporally and culturally adaptive LLMs.

SPAN: Benchmarking and Improving Cross-Calendar Temporal Reasoning of Large Language Models

Image deraining is crucial for vision applications but is challenged by the complex multi-scale physics of rain and its coupling with scenes. To address this challenge, a novel approach inspired by multi-stage image restoration is proposed, incorporating Point Spread Function (PSF) mechanisms to reveal the image degradation process while combining dynamic physical modeling with sequential feature fusion transfer, named SD-PSFNet. Specifically, SD-PSFNet employs a progressive refinement architecture with three cascaded stages that sequentially restore the rain-free image. The network utilizes specialized blocks with learned point spread functions to dynamically model rain streak optics, enabling effective rain-background separation while progressively enhancing outputs through novel PSF components at each stage. Additionally, SD-PSFNet incorporates adaptive gated fusion for optimal cross-stage feature integration, enabling progressive refinement from coarse rain removal to fine detail restoration. Our model achieves state-of-the-art PSNR/SSIM metrics on Rain100H (33.12dB/0.9371), RealRain-1k-L (42.28dB/0.9872), and RealRain-1k-H (41.08dB/0.9838). SD-PSFNet demonstrates superior robustness in complex scenes and dense rain conditions, all while maintaining a modest parameter count (9.63M) compared to more complex contemporary models.

SD-PSFNet: Sequential and Dynamic Point Spread Function Network for Image Deraining

Robust Multimodal Learning (RML) aims to address the issues of unreliable predictions of multimodal models.
Nevertheless, previous RML works often struggle to distinguish between different categories that rely on identical intra-modal cues, making ambiguous predictions.
We defined this degree of ``uncertain'' in extracting discriminative features of a multimodal model as vagueness.
Neglecting such vagueness, as previous RML works commonly do, will undermine the ability to extract unique semantics of each category in multimodal models, further resulting in worse robustness under disturbances that affect semantic representations.
Additionally, this vagueness will lead the parameter updating processes towards unreliable fusion, thus diverting the learning processes of the multimodal model from learning unique features of each category.
Based on the above insight, we propose a novel robust multimodal learning approach, termed Hyper-Opinion Quantifying Vagueness (HOQV).
Specifically, we first introduce hyper-opinion to capture and quantify the vagueness of multimodal learning in discriminating representations of different categories.
Moreover, to mitigate the interference in parameter updating of unreliable representations with high vagueness, we also design the Hyper-Opinion Gradient Modulation to guide the optimization processes. 
We evaluate our HOQV on six datasets with different disturbances, including noise and adversarial attack, and demonstrate that our proposed method achieves state-of-the-art performance consistently.

Hyper-Opinion Vagueness Quantification for Robust Multimodal Learning

The goal of this work is to adapt Segment Anything Models (SAM) into crack segmentation tasks via automatic label generation, thus eliminating manual annotation cost. In this regard, an intuitive approach is to extract edges of crack samples and generate labels via the dilation and erosion processes for fine-tuning SAM. However, this simple solution cannot guarantee the quality of generated labels, as crack regions will be corrupted due to the imperfect edge detection. To this end, this paper proposes CoGenSAM, a novel Codebook-interactive Generative Labeling framework that enables an annotation-free SAM fine-tuning. To achieve this, in the first stage, we pre-train a vector-quantized variational auto-encoder (VQVAE) by reconstructing the synthesized crack-like structures for learning crack-aware priors within the codebook. In the second stage, these priors help another VQVAE serve as the restoration model to restore the randomly corrupted structures into uncorrupted ones. Specifically, we propose the crack-aware contrastive-interaction to maximize the mutual information with the above priors via codebook interaction. Then, high-quality labels can be generated by restoring corrupted labels from edge detection, contributing to an annotation-free SAM fine-tuning. We collect a new dataset, Bridge2025, to address the limited availability of related bridge-oriented benchmarks. Experiments show that our performance is close to fully-supervised methods.

CoGenSAM: Codebook-Interactive Generative Labeling for Adapting SAM to Crack Segmentation

Precise segmentation of organ and tissue lesions is essential for clinical diagnosis and treatment. Despite the progress of deep learning and foundation segmentation models, their domain generalization capability remains limited particularly when dealing with cross-domain scenarios or unseen data, leading to significant performance degradation. Current medical SAM-based generalization methods face two primary challenges: First, existing prompt-tuning strategies inadequately capture key domain-invariant features; Second, the reliance on fully labeled source domain data is unrealistic in clinical practice. To address these challenges, we propose a novel Dual domain-Invariant Prompt Optimization (DIPO) enhanced by energy-guided augmentation and frequency consistency regularization for few-shot medical image segmentation generalization. Our approach introduces a multi-band momentum enhancement strategy to dynamically augment source data by leveraging diverse frequency bands of the Fourier amplitude spectrum. Furthermore, we integrate multiscale geometric representation-based non-subsampled shearlet transform and text prompts to strengthen the extraction of shape- and texture-related domain-invariant features. Finally, we employ frequency consistency regularization to refine model robustness using predictions from unlabeled data. Experimental results in prostate and fundus datasets demonstrate that our method significantly outperforms current state-of-the-art methods. The codes will be publicly available.

Energy-guided Dual Domain-invariant Prompting Framework with Fourier Regularization for Generalized Few-Shot Medical Segmentation

Understanding the neural basis of three-dimensional (3D) perception is a fundamental objective in cognitive neuroscience. Despite advances in decoding 2D visual stimuli from neural data, reconstructing high-fidelity 3D objects with detailed texture and geometry remains largely unexplored. In this work, we introduce **NeuroSculptor3D**, the first single-stage, end-to-end framework for reconstructing textured 3D shapes directly from brain activity. NeuroSculptor3D integrates a viewpoint-aware brain embedding module that captures fine-grained spatial variations across visual perspectives, and a hierarchical guidance mechanism that aligns brain-derived features with perceptual, semantic, and structural priors. Together, these components facilitate the generation of consistent multi-view embeddings, which are then decoded via TRELLIS to produce high-quality textured 3D reconstructions. Experiments on the fMRI-Shape dataset demonstrate that NeuroSculptor3D outperforms existing baselines across multiple settings, achieving significant improvements in both structural accuracy and semantic consistency. Code will be released to facilitate further research.

Single-Stage fMRI-to-3D Reconstruction via Viewpoint-Aware Embedding and Hierarchical Guidance

Many existing financial math reasoning benchmarks suffer from data contamination and high manual construction costs. To address this, we propose a novel formula-driven approach to dynamically construct math reasoning benchmarks in finance. Our two-stage approach: (1) generates single-formula questions by LLMs using a "Mask-for-Solve" paradigm for ground truth answers, and (2) synthesizes multi-formula questions through hierarchical tree-based DAGs. Our approach ensures novelty (via LLMs' creativity) and controllability of difficulty (via DAG structure). Based on a self-constructed financial formula bank, we utilize the proposed method to build FinMathBench, the first formula-driven and fully LLM-generated benchmark aimed at assessing LLMs' math reasoning abilities in finance, containing 946 questions across 4 complexity levels. Evaluation results on 40 LLMs demonstrate significant accuracy drops in multi-formula questions, e.g., 72.9\% (1-Formula) $\rightarrow$ 14.0\% (4-Formula) for GPT-4o under Chain-of-Thought prompting. Three critical flaws of LLMs are also observed: poor direct calculation performance, bias toward frequently solved variables in formulas, and erroneous "correction" of valid but extreme financial values. These findings highlight gaps in current LLMs' domain-specific reasoning and underscore FinMathBench's value for advancing robust financial LLMs.

FinMathBench: A Formula-Driven Benchmark for Evaluating LLMs’ Math Reasoning Capabilities in Finance

We aim to develop a goal specification method that is semantically clear, spatially sensitive, domain-agnostic, and intuitive for human users to guide agent interactions in 3D environments. Specifically, we propose a novel cross-view goal alignment framework that allows users to specify target objects using segmentation masks from their camera views rather than the agent’s observations. We highlight that behavior cloning alone fails to align the agent’s behavior with human intent when the human and agent camera views differ significantly. To address this, we introduce two auxiliary objectives: cross-view consistency loss and target visibility loss, which explicitly enhance the agent's spatial reasoning ability. According to this, we develop ROCKET-2, a state-of-the-art agent trained in Minecraft, achieving an improvement in the efficiency of inference $3\times$ to $6\times$. We demonstrate that ROCKET-2 can directly interpret goals from human camera views, enabling better human-agent interaction. Remarkably, ROCKET-2 demonstrates zero-shot generalization capabilities: despite being trained exclusively on the Minecraft dataset, it can adapt and generalize to other 3D environments like Doom, DMLab, and Unreal through a simple action space mapping.

Steering Visuomotor Policy in Open Worlds via Cross-View Goal Alignment

Vector quantization has emerged as a powerful tool in large-scale multimodal models, unifying heterogeneous representations through discrete token encoding. However, its effectiveness hinges on robust codebook design. Current prototype-based approaches relying on trainable vectors or clustered centroids fall short in representativeness and interpretability, even as multimodal alignment demonstrates its promise in vision-language models. To address these limitations, we propose a simple multimodal prompting-driven quantization framework for point cloud analysis. Our methodology is built upon two core insights: 1) Text embeddings from pre-trained models inherently encode visual semantics through many-to-one contrastive alignment, naturally serving as robust prototype priors; and 2) Multimodal prompts enable adaptive refinement of these prototypes, effectively mitigating vision-language semantic gaps. The framework introduces a dual-constrained quantization space, enforced by compactness and separation regularization, which seamlessly integrates visual and prototype features, resulting in hybrid representations that jointly encode geometric and semantic information. Furthermore, we employ Gumbel-Softmax relaxation to achieve differentiable discretization while maintaining quantization sparsity. Extensive experiments on the ModelNet40 and ScanObjectNN datasets clearly demonstrate the superior effectiveness of the proposed method.

Point Cloud Quantization Through Multimodal Prompting for 3D Understanding

Sample selection is a straightforward technique to combat noisy labels, aiming to prevent mislabeled samples from degrading the robustness of neural networks. 
However, existing methods mitigate compounding selection bias either by leveraging dual-network disagreement or additional forward propagations, leading to multiplied training overhead.
To address this challenge, we introduce $\textit{Jump-teaching}$, an efficient sample selection framework for debiased model update and simplified selection criterion. Based on a key observation that a neural network exhibits significant disagreement across different training iterations, Jump-teaching proposes a jump-manner model update strategy to enable self-correction of selection bias by harnessing temporal disagreement, eliminating the need for multi-network or multi-round training. 
Furthermore, we employ a sample-wise selection criterion building on the intra variance of a decomposed single loss for a fine-grained selection without relying on batch-wise ranking or dataset-wise modeling. 
Extensive experiments demonstrate that Jump-teaching outperforms state-of-the-art counterparts while achieving a nearly overhead-free selection procedure, which boosts training speed by up to $4.47\times$ and reduces peak memory footprint by $54\\%$.

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