Singapore

Current mainstream AI, at least as presented in media and measured by the number of people involved and papers published, is mainly about big data, deep learning, and recently trendy large language models. All these are techniques that are data-driven, model-free, and number-crunching. Their immense success in some areas, such as computer vision and natural language processing, started the next hype in the era of AI, which brings a question whether neural approaches, after being dismissed at the beginning of the AI era, finally conquered the world of AI and proved applicable to every problem. A deeper look at these new techniques shows they have similar issues as the old-fashioned AI techniques in the past: brittleness, making strange mistakes, and being highly dependent on data used for training. Moreover, there are problems with the explainability of results and no guarantees provided, which is a crucial issue in some application areas.

In this paper, we look at core principles of the neural ML techniques, that is, being data-driven rather than knowledge-based and being model-free rather than model-based, and we argue that symbolic knowledge models can still contribute to the design of trustworthy and explainable AI systems. Specifically, we focus on hierarchical reasoning, namely hierarchical planning, which is useful for highly complex problems but is not addressed by current neural models. We propose a research plan consisting of solving specific problems in hierarchical planning as an example of a knowledge-intensive approach to problem-solving. We show close connections between these problems that allow a smooth transition between solving techniques used to solve these problems. We also propose an ultimate goal of this endeavor, that is, autonomous construction of hierarchical planning models, that addresses the crucial problem of knowledge-based approaches -- how to obtain a formal model (extract knowledge from data).

AAAI 2026

On Trustworthy, Explainable, and Verifiable High-Level Autonomy via Hierarchical Planning

plan correction

plan validatio

plan recognition

model correction

hierarchical planning

explanability

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AAMAS 2021

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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.

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.

Unplanned extubation (UE)—the unintended removal of an airway tube—remains a critical patient safety concern in intensive care units (ICUs), often leading to severe complications or death. 
Real-time UE detection has been limited, largely due to the ethical and privacy challenges of obtaining annotated ICU video data. 
We propose AURA (Augmented Unplanned Removal Alert), a vision-based risk detection system developed and validated entirely on a fully synthetic ICU video dataset. 
By leveraging text-to-video diffusion, we generated diverse and clinically realistic scenarios capturing a range of patient behaviors and care contexts. 
The system applies pose estimation to identify two high-risk movement patterns: collision, defined as hand entry into spatial zones near airway tubes, and agitation, quantified by the velocity of tracked anatomical keypoints. 
Expert assessment validated the realism of the synthetic data, while quantitative evaluation demonstrated high collision detection accuracy and moderate agitation recognition performance. 
This work demonstrates a novel pathway for developing privacy-preserving, reproducible patient safety monitoring systems with potential for deployment in intensive care settings.

AURA: Development and Validation of an Augmented Unplanned Removal Alert System Using Synthetic ICU Videos

Vision-Language-Action (VLA) models have advanced in robotic manipulation, yet practical deployment remains hindered by two key limitations: **1) perceptual redundancy**, where irrelevant visual inputs are processed inefficiently, and **2) superficial instruction-vision alignment**, which hampers semantic grounding of actions.
In this paper, we propose **SemanticVLA**, a novel VLA framework that performs Semantic-Aligned Sparsification and Enhancement for Efficient Robotic Manipulation. Specifically:
**1)** To sparsify redundant perception while preserving semantic alignment, **Semantic-guided Dual Visual Pruner (SD-Pruner)** performs: Instruction-driven Pruner (ID-Pruner) extracts global action cues and local semantic anchors in SigLIP; Spatial-aggregation Pruner (SA-Pruner) compacts geometry-rich features into task-adaptive tokens in DINOv2.
**2)** To exploit sparsified features and integrate semantics with spatial geometry, **Semantic-complementary Hierarchical Fuser (SH-Fuser)** fuses dense patches and sparse tokens across SigLIP and DINOv2 for coherent representation.
**3)** To enhance the transformation from perception to action, **Semantic-conditioned Action Coupler (SA-Coupler)** replaces the conventional observation-to-DoF approach, yielding more efficient and interpretable behavior modeling for manipulation tasks.
Extensive experiments on simulation and real-world tasks show that SemanticVLA sets a new SOTA in both performance and efficiency. SemanticVLA surpasses OpenVLA on LIBERO benchmark by **21.1%** in success rate, while reducing training cost and inference latency by **3.0×** and **2.7×**.

SemanticVLA: Semantic-Aligned Sparsification and Enhancement for Efficient Robotic Manipulation

Perceptual image compression has recently gained increasing attention, as it aims to reconstruct visually realistic images using generative models. Most existing methods adopt patch-based generative adversarial networks (PatchGAN) for one-step image generation, where adversarial training helps the decoder learn the distribution of natural images. However, this strategy is often coarse-grained, as it focuses mainly on patch-level consistency and overlooks global structural and semantic details. To address this limitation, we propose a simple yet effective Semantic and Spectral Consistency Learning (SSCL) strategy, which complements existing patch-based approaches for more accurate distribution alignment. For semantic consistency, we leverage semantic vision models to extract semantic features. The semantic discriminator, aware of the specific semantics of each image, provides more adaptive and precise feedback. This guides the encoder to retain meaningful information and helps the decoder synthesize detailed textures, without requiring explicit semantic transmission or additional modules. For spectral consistency, we introduce a frequency discriminator that focuses on high-frequency components, helping to reduce artifacts based on spectral priors. Experiments show that SSCL outperforms existing perceptual codecs in terms of visual quality. Compared to MS-ILLM, SSCL achieves 45% to 60% bit-rate savings on CLIC2020 and Kodak datasets, measured by FID and DISTS.

SSCL: Adversarially Guided Image Compression via Semantic and Spectral Consistency Learning

Machine unlearning in learned cardinality estimation (CE) systems presents unique challenges due to the complex distributional dependencies in multi-table relational data. Specifically, data deletion, a core component of machine unlearning, faces three critical challenges in learned CE models: attribute-level sensitivity, inter-table propagation and domain disappearance leading to severe overestimation in multi-way joins. We propose Cardinality Estimation Pruning (CEP), the first unlearning framework specifically designed for multi-table learned CE systems. CEP introduces Distribution Sensitivity Pruning, which constructs semi-join deletion results and computes sensitivity scores to guide parameter pruning, and Domain Pruning, which removes support for value domains entirely eliminated by deletion. We evaluate CEP on state-of-the-art architectures NeuroCard and FACE across IMDb and TPC-H datasets. Results demonstrate CEP consistently achieves the lowest Q-error in multi-table scenarios, particularly under high deletion ratios, often outperforming full retraining. Furthermore, CEP significantly reduces convergence iterations, incurring negligible computational overhead of 0.3%-2.5% of fine-tuning time.

Forgetting by Pruning: Data Deletion in Join Cardinality Estimation

Universal Photometric Stereo is a promising approach for recovering surface normals without strict lighting assumptions. However, it struggles when multi-illumination cues are unreliable, such as under biased lighting or in shadows or self-occluded regions of complex in-the-wild scenes. We propose GeoUniPS, a universal photometric stereo network that integrates synthetic supervision with high-level geometric priors from large-scale 3D reconstruction models pretrained on massive in-the-wild data. Our key insight is that these 3D reconstruction models serve as visual-geometry foundation models, inherently encoding rich geometric knowledge of real scenes. To leverage this, we design a Light-Geometry Dual-Branch Encoder that extracts both multi-illumination cues and geometric priors from the frozen 3D reconstruction model. We also address the limitations of the conventional orthographic projection assumption by introducing the PS-Perp dataset with realistic perspective projection to enable learning of spatially varying view directions. Extensive experiments demonstrate that GeoUniPS delivers state-of-the-arts performance across multiple datasets, both quantitatively and qualitatively, especially in the complex in-the-wild scenes. Code and models will be released upon acceptance.f

Geometry Meets Light: Leveraging Geometric Priors for Universal Photometric Stereo Under Limited Multi-Illumination Cues

Privacy leakage in Multimodal Large Language Models (MLLMs) has long been an intractable problem. Existing studies, though effectively obscure private information in MLLMs, often overlook the evaluation of authenticity and recovery quality of user privacy. To this end, this work uniquely focuses on the critical challenge of how to restore surrogate-driven protected data in diverse MLLM scenarios. We first bridge this research gap by contributing the SPPE (Surrogate Privacy Protected Editable) dataset, which includes a wide range of privacy categories and user instructions to simulate real MLLM applications. This dataset offers protected surrogates alongside their various MLLM-edited versions, thus enabling the direct assessment of privacy recovery quality. By formulating privacy recovery as a guided generation task conditioned on complementary multimodal signals, we further introduce a unified approach that reliably reconstructs private content while preserving the fidelity of MLLM-generated edits. The experiments on both SPPE and InstructPix2Pix further show that our approach generalizes well across diverse visual content and editing tasks, achieving a strong balance between privacy protection and MLLM usability.

When Privacy Meets Recovery: The Overlooked Half of Surrogate-Driven Privacy Preservation for MLLM Editing

Higher-order brain connectivity (HOBC), which captures interactions among three or more brain regions, provides richer organizational information than traditional pairwise functional connectivity (FC). Recent studies have begun to infer latent HOBC from noninvasive imaging data, but they mainly focus on static analyses, limiting their applicability in dynamic prediction tasks. To address this gap, we propose DCHO, a unified approach for modeling and forecasting the temporal evolution of HOBC based on a decomposition–composition framework, which is applicable to both non-predictive tasks (state classification) and predictive tasks (brain dynamics forecasting). DCHO adopts a decomposition–composition strategy that reformulates the prediction task into two manageable subproblems: HOBC inference and latent trajectory prediction. In the inference stage, we propose a dual-view encoder to extract multiscale topological features and a latent combinatorial learner to capture high-level HOBC information. In the forecasting stage, we introduce a latent-space prediction loss to enhance the modeling of temporal trajectories. Extensive experiments on multiple neuroimaging datasets demonstrate that DCHO achieves superior performance in both non-predictive tasks (state classification) and predictive tasks (brain dynamics forecasting), significantly outperforming existing methods.

DCHO: A Decomposition–Composition Framework for Predicting Higher-Order Brain Connectivity to Enhance Diverse Downstream Applications

Large multimodal models (LMMs) have demonstrated remarkable capabilities across a wide range of tasks, however their knowledge and abilities in the cross-view geo-localization and pose estimation domains remain unexplored, despite potential benefits for navigation, autonomous driving, outdoor robotics, etc. To bridge this gap, we introduce GeoX-Bench, a comprehensive Benchmark designed to explore and evaluate the capabilities of LMMs in cross-view Geo-localization and pose estimation. Specifically, GeoX-Bench contains 10,859 panoramic-satellite image pairs spanning 128 cities in 49 countries, along with corresponding 755,976 question-answering (QA) pairs. Among these, 42,900 QA pairs are designated for benchmarking, while the remaining are intended to enhance the capabilities of LMMs. Based on GeoX-Bench, we evaluate the capabilities of 25 state-of-the-art LMMs on cross-view geo-localization and pose estimation tasks, and further explore the empowered capabilities of instruction-tuning. Our benchmark demonstrate that while current LMMs achieve impressive performance in geo-localization tasks, their effectiveness declines significantly on the more complex pose estimation tasks, highlighting a critical area for future improvement, and instruction-tuning LMMs on the training data of GeoX-Bench can significantly improve the cross-view geo-sense abilities. The GeoX-Bench will be released upon the publication.

GeoX-Bench: Benchmarking Cross-View Geo-Localization and Pose Estimation Capabilities of Large Multimodal Models

Improving large language models (LLMs) for electronic health record (EHR) reasoning is essential for enabling accurate and generalizable clinical predictions. While LLMs excel at medical text understanding, they underperform on EHR-based prediction tasks due to challenges in modeling temporally structured, high-dimensional data. Existing approaches often rely on hybrid paradigms, where LLMs serve merely as frozen prior retrievers while downstream deep learning (DL) models handle prediction, failing to improve the LLM’s intrinsic reasoning capacity and inheriting the generalization limitations of DL models. To this end, we propose EAG-RL, a novel two-stage training framework designed to intrinsically enhance LLMs’ EHR reasoning ability through expert attention guidance, where expert EHR models refer to task-specific DL models trained on EHR data. Concretely, EAG-RL first constructs high-quality, stepwise reasoning trajectories using expert-guided Monte Carlo Tree Search to effectively initialize the LLM’s policy. Then, EAG-RL further optimizes the policy via reinforcement learning by aligning the LLM’s attention with clinically salient features identified by expert EHR models. Extensive experiments on two real-world EHR datasets show that EAG-RL improves the intrinsic EHR reasoning ability of LLMs by an average of 14.62%, while also enhancing robustness to feature perturbations and generalization to unseen clinical domains. These results demonstrate the practical potential of EAG-RL for real-world deployment in clinical prediction tasks.

Toward Better EHR Reasoning in LLMs: Reinforcement Learning with Expert Attention Guidance

Speech translation (ST) aims to translate speech from a source language into text in the target language. Naturally, speech signals contain paralinguistic cues beyond linguistic content, which could influence or even alter the interpretation of a lexically identical sentence, thereby yielding distinct translations. However, existing ST models lack direct and sufficient modeling of paralinguistic information, which limits their ability to perceive paralinguistic cues and understand speech comprehensively, leading to degraded translation performance. In response, we propose $\textbf{P}$ara$\textbf{L}$inguistic-$\textbf{a}$ware $\textbf{S}$peech $\textbf{T}$ranslation ($\textbf{PLaST}$), a novel dual-branch framework which directly leverages paralinguistic cues beyond the linguistic content. Specifically, PLaST employs a speech encoder and a style extractor to independently generate linguistic and paralinguistic representations, respectively. To obtain a purified linguistic representation aligned with the text representation, a hierarchical Optimal Transport ($\textbf{OT}$) is applied on the layer-wise outputs from an LLM decoder. Then, the paralinguistic information is retrieved and refined with an Attention-based Retrieval ($\textbf{AR}$) module, with the linguistic representation serving as queries to enable joint guidance for semantic understanding and translation generation. PLaST outperforms the strong baseline with an average of $\textbf{5.0}$ directional and $\textbf{4.5}$ global contrastive likelihood scores on the paralinguistic-sensitive benchmark ContraProST, demonstrating its superior capability in paralinguistic perception. Further experiments on the standard speech translation benchmark CoVoST-2 show that PLaST generalizes well to typical ST scenarios.
Codes and models will be made publicly available after the peer review process.

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