Singapore

Custom Diffusion Models (CDMs) offer impressive capabilities for personalization in generative modeling, yet they remain vulnerable to catastrophic forgetting when learning new concepts sequentially. Existing approaches primarily focus on minimizing interference between concepts, often neglecting the potential for positive inter-concept interactions. In this work, we present FLLP (Forget Less by Learning from Parent), a novel framework that introduces a parent-child inter-concept learning mechanism in hyperbolic space to mitigate forgetting. By embedding concept representations within a Lorentzian manifold, naturally suited for modeling tree-like hierarchies, we define parent-child relationships where previously learned concepts act as guidance for adapting to new ones. Our method not only preserves prior knowledge but also supports continual integration of new concepts. We validate FLLP on three public datasets and one synthetic benchmark, showing consistent improvements in both robustness and generalization.

AAAI 2026

Forget Less by Learning from Parents Through Hierarchical Relationships

text-to-image diffusion

hyperbolic geometry

continual learning

We are pleased to announce the Thirty-Ninth AAAI Conference on Artificial Intelligence (AAAI-25), which will be held in Philadelphia, Pennsylvania at the Pennsylvania Convention Center from February 25 to March 4, 2025.

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-25 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.

### [Invited Speakers](https://aaai.org/conference/aaai/aaai-25/aaai-25-invited-speakers/)

Register [here](https://aaai.org/conference/aaai/aaai-25/registration/)

AAAI 2025

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-25 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.



poster

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.

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We recommend reading [**the registration information**](https://aaai.org/conference/aaai/aaai-26/registration/) first.

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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.

Despite significant advancements in point cloud analysis, reducing energy consumption and improving robustness remain understudied, largely due to the inherent limitations of Convolutional Neural Networks (CNNs). To address this, we take the cue from the primary visual cortex and propose a Dendritic-Connected Continuous-Coupled Neural Network (DC-CCNN), a novel Brain-Inspired Neural Network (BINN) architecture tailored for point cloud analysis. By leveraging the unique characteristics of point clouds, our design combines discrete and continuous encoding, replacing traditional Multilayer Perceptrons (MLPs) with more efficient and robust BINNs. Our approach substantially improves the performance of Brain-Inspired Neural Networks on point analysis tasks and maintaining performance comparable to state-of-the-art methods. Furthermore, DC-CCNN exhibits enhanced robustness against various point cloud deformations and corruptions. Our experimental results demonstrate that DC-CCNN achieves competitive performance on benchmark datasets, making it a promising alternative to traditional deep learning methods for point cloud analysis. With its high efficiency and robustness, DC-CCNN has the potential for widespread adoption in 3D computer vision, robotics, and autonomous systems.

Primary Visual Cortex Inspired Point Cloud Analysis Framework

Large Language Models (LLMs) have greatly advanced knowledge graph question answering (KGQA), yet existing systems are typically optimized for returning highly relevant but predictable answers.
A missing yet desired capacity is to exploit LLMs to suggest surprise and novel (``serendipitious'') answers.
In this paper, we formally define the serendipity-aware KGQA task and propose the SerenQA framework to evaluate LLMs' ability to uncover unexpected insights in scientific KGQA tasks. 
SerenQA includes a rigorous serendipity metric based on relevance, novelty, and surprise, along with an expert-annotated benchmark derived from the Clinical Knowledge Graph, focused on drug repurposing. 
Additionally, it features a structured evaluation pipeline encompassing three subtasks: knowledge retrieval, subgraph reasoning, and serendipity exploration.
Our experiments reveal that while state-of-the-art LLMs perform well on retrieval, they still struggle with identifying genuinely surprising and valuable discoveries, underscoring significant room for future improvements.
Our curated resources have been released as supplementary material.

Assessing LLMs for Serendipity Discovery in Knowledge Graphs: A Case for Drug Repurposing

Deploying Large Language Models (LLMs) in specialized domains introduces significant societal and compliance risks, including bias amplification, misinformation propagation, and privacy violations. These risks predominantly emerge from the dynamic interactions between LLMs and humans in specific contexts. Different domains face unique distribution of hazards, and varying interaction modalities introduce distinct levels of exposure and vulnerability. However, current risk assessment frameworks lack a systematic methodology to capture this dynamic interplay. In this work, we introduce the HEV Generative Sandbox, a novel risk evaluation framework that simulates human-LLM behavior to quantify domain-contextual risks across three interdependent dimensions: 1) Hazard (H): Domain-specific threats inherent to a given context; 2) Exposure (E): The extent to which the LLM and its users are subjected to hazardous scenarios; 3) Vulnerability (V): The susceptibility of the system to risk due to human interaction or model weaknesses. Our approach pioneers "domain-rooted scenario generation", wherein we sample contextual distributions from domain-specific corpora and simulate diverse inputs. By unifying dynamic scenario simulation, causal risk decomposition, and closed-loop evaluation, the HEV Generative Sandbox provides a scalable, domain-sensitive methodology for responsible LLM deployment. This work contributes to advancing the safe deployment of LLMs by providing a comprehensive and automated risk evaluation framework. We plan to make our code and simulation-generated data publicly available upon publication.

HEV Generative Sandbox: A Framework for Assessing Domain-Specific Social Risks Through Human-LLM Simulation

In cooperative Multi-Agent Reinforcement Learning (MARL), efficient exploration is crucial for optimizing the performance of joint policy. However, existing methods often update joint policies via independent agent exploration, without coordination among agents, which inherently constrains the expressive capacity and exploration of joint policies. To address this issue, we propose a conductor-based joint policy framework that directly enhances the expressive capacity of joint policies and coordinates exploration. In addition, we develop a Hierarchical Conductor-based Policy Optimization (HCPO) algorithm that instructs policy updates for the conductor and agents in a direction aligned with performance improvement. A rigorous theoretical guarantee further establishes the monotonicity of the joint policy optimization process. By deploying local conductors, HCPO retains centralized training benefits while eliminating inter-agent communication during execution. Finally, we evaluate HCPO on three challenging benchmarks: StarCraftII Multi-agent Challenge, Multi-agent MuJoCo, and Multi-agent Particle Environment. The results indicate that HCPO outperforms competitive MARL baselines regarding cooperative efficiency and stability.

HCPO: Hierarchical Conductor-Based Policy Optimization in Multi-Agent Reinforcement Learning

Test-time compute scaling has emerged as a powerful paradigm for enhancing mathematical reasoning in large language models (LLMs) by allocating additional computational resources during inference. However, current methods employ uniform resource distribution across all reasoning sub-problems, creating fundamental bottlenecks where challenging sub-problems receive insufficient attention while routine operations consume disproportionate resources. This uniform allocation creates performance bottlenecks where additional computational resources yield diminishing returns. Inspired by dual-process theory, we propose **SCALE** (Selective Resource Allocation), a framework that selectively allocates computational resources based on sub-problem difficulty. SCALE operates through four stages: (1) problem decomposition into sequential reasoning sub-problems, (2) difficulty assessment of each sub-problem to distinguish between routine operations and computationally challenging sub-problems, (3) selective processing mode assignment between System 1 for simple sub-problems and System 2 for complex ones, and (4) sequential execution with context propagation. By concentrating resources on challenging sub-problems while processing routine operations efficiently, SCALE achieves substantial performance improvements with superior resource utilization. Extensive experiments demonstrate that SCALE significantly outperforms uniform scaling baselines, achieving accuracy improvements of up to 13.75 percentage points (57.50\% to 71.25\% on AIME25) while reducing computational costs by 33-53\%, representing a major advance in test-time scaling that addresses fundamental limitations of current approaches.

SCALE: Selective Resource Allocation for Overcoming Performance Bottlenecks in Mathematical Test-time Scaling

High-quality training data is critical to the performance of large language models (LLMs). Recent work has explored using LLMs to rate and select data based on a small set of human-designed criteria (rules), but these approaches often rely heavily on heuristics, lack principled metrics for rule evaluation, and generalize poorly to new tasks. We propose a novel rule-based data selection framework that introduces a metric based on the orthogonality of rule score vectors to evaluate and select complementary rules. Our automated pipeline first uses LLMs to generate diverse rules covering multiple aspects of data quality, then rates samples according to these rules and applies the determinantal point process (DPP) to select the most independent rules. These rules are then used to score the full dataset, and high-scoring samples are selected for downstream tasks such as LLM fine-tuning. We evaluate our framework in two experiment setups: (1) alignment with ground-truth ratings and (2) performance of LLMs fine-tuned on the selected data. Experiments across IMDB, Medical, Math, and Code domains demonstrate that our DPP-based rule selection consistently improves both rating accuracy and downstream model performance over strong baselines.

Selection of LLM Fine-Tuning Data Based on Orthogonal Rules

We introduce FLAG-4D, a novel framework for generating novel views of dynamic scenes by reconstructing how 3D Gaussian primitives evolve through space and time. Existing methods typically rely on a single Multilayer Perceptron(MLP) to model temporal deformations, and they often struggle to capture complex point motions and fine-grained dynamic details consistently over time, especially from sparse input views. Our approach, FLAG-4D overcomes this by employing a dual-deformation network that dynamically warps a canonical set of 3D Gaussians over time into new positions and anisotropic shapes. This dual-deformation network consists of an Instantaneous Deformation Network (IDN) for modeling fine-grained, local deformations, and Global Motion Network (GMN) for capturing long-range dynamics, refined via mutual learning. To ensure these deformations are both accurate and temporally smooth, FLAG-4D incorporates dense motion features from a pretrained optical flow backbone. We fuse these motion cues from adjacent timeframes and use a deformation-guided attention mechanism to align this flow information with the current state of each evolving 3D Gaussian. Extensive experiments demonstrate that FLAG-4D achieves higher-fidelity and more temporally coherent reconstructions with finer detail preservation than state-of-the-art methods.

FLAG-4D: Flow-Guided Local-Global Dual-Deformation Model for 4D Reconstruction

Automated analysis of temporal changes in multimodal retinal images is critical for the prognostic assessment of ophthalmic diseases. 
Unlike traditional single-timepoint diagnosis, tracking longitudinal changes across multiple imaging modalities introduces significant data bias challenges: (1) Imbalanced modality samples compromise the integration of knowledge within minority modalities; (2) Heterogeneous visual patterns across modalities undermine the perception of disease-relevant biomarkers. To tackle these issues, we propose a Modality-Incremental Expert Aggregation Network (MoEA-Net), which unifies the inter-modal integration and intra-modal perception for enhanced retinal prognostic prediction. Specifically, we employ the large language model (LLM) with incremental LoRA layers for specific modalities to effectively integrate knowledge from imbalanced data. Besides, we introduce a Spatiotemporal-aware Expert (SAE) module to better perceive both the anatomical structures and longitudinal changes within modalities. By progressively combining the SAE module with incremental LoRA, MoEA-Net supports continual knowledge accumulation and improves accurate reasoning. Experimental results show that MoEA-Net achieves state-of-the-art performance on \textit{subretinal fluid change} and \textit{visual recovery} classification tasks, validating its effectiveness. Our code will be open-sourced upon acceptance.

MoEA-Net: Modality-Incremental Expert Aggregation Network for Retinal Prognostic Prediction

Large Language Models (LLMs) have achieved remarkable success across reasoning and knowledge-intensive tasks, yet their static pretraining leaves them unable to handle rapidly evolving or domain-specific knowledge. Retrieval-Augmented Generation (RAG) addresses this by grounding LLM outputs in dynamically retrieved evidence, improving factual accuracy and reducing hallucinations. However, standard RAG pipelines struggle with temporally sensitive queries, especially when documents contain fuzzy or indirect time expressions (e.g., “a few years later”). This leads to Temporal Misalignment, where topically relevant but temporally incorrect results are retrieved. To overcome this, we propose DeFuzzRAG, a lightweight framework that enhances temporal robustness in RAG. DeFuzzRAG employs a small local language model to infer concrete time scopes from vague expressions and applies metadata-based filtering to realign retrieval with the query’s temporal intent. Experiments on a benchmark of fuzzified queries demonstrate that DeFuzzRAG substantially improves retrieval accuracy, raising Hit Rate by 15.7\% while maintaining efficiency and model-agnostic integration. Our findings highlight the importance of temporal reasoning in RAG and establish DeFuzzRAG as a practical, plug-and-play solution for deploying temporally robust LLM systems in real-world settings.

DeFuzzRAG: Handling Fuzzy Time Expressions for Temporal Robustness in Retrieval-Augmented Generation

Textured high-fidelity 3D models are crucial for games, AR/VR, and film, but human-aligned evaluation methods still fall behind despite recent advances in 3D reconstruction and generation. Existing metrics, such as Chamfer Distance, often fail to align with how humans evaluate the fidelity of 3D shapes. Recent learning-based metrics attempt to improve this by relying on rendered images and 2D image quality metrics. However, these approaches face limitations due to incomplete structural coverage and sensitivity to viewpoint choices. Moreover, most methods are trained on synthetic distortions, which differ significantly from real-world distortions, resulting in a domain gap. To address these challenges, we propose a new fidelity evaluation method that is based directly on 3D meshes with texture, without relying on rendering. Our method, named Textured Geometry Evaluation (TGE), jointly uses the geometry and color information to calculate the fidelity of the input textured mesh in comparison to a reference colored shape. To train and evaluate our metric, we design a human-annotated dataset with real-world distortions. Experiments show that TGE outperforms rendering-based and geometry-only methods on real-world distortion datasets.

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