Singapore

We propose new quantum algorithms for estimating spectral sums of positive semi-definite (PSD) matrices. The spectral sum of an PSD matrix $A$, for a function $f$, is defined as $Tr[f(A)] = \sum_j f(\lambda_j)$, where $\lambda_j$ are the eigenvalues of $A$. 
Typical examples of spectral sums are the von Neumann entropy, the trace of $A^{-1}$, the log-determinant, and the Schatten $p$-norm, where the latter does not require the matrix to be PSD. The current best classical randomized algorithms estimating these quantities have a runtime that is at least linearly in the number of nonzero entries of the matrix and quadratic in the estimation error. Assuming access to a block-encoding of a matrix, our algorithms are sub-linear in the matrix size, and depend at most quadratically on other parameters, like the condition number and the approximation error, and thus can compete with most of the randomized and distributed classical algorithms proposed in the literature, and polynomially improve the runtime of other quantum algorithms proposed for the same problems.
We show how the algorithms and techniques used in this work can be applied to three problems in spectral graph theory: approximating the number of triangles, the effective resistance, and the number of spanning trees in a graph.

AAAI 2026

Quantum Algorithms for Spectral Sums

quantum algorithms

quantum machine learning

quantum computing

We are pleased to announce the Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24), which will be held in Vancouver, British Columbia at the Vancouver Convention Centre – West Building from 20-27 February, 2024.

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-24 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.

We expect for AAAI-24 to be an in-person conference – one author of all accepted papers will be expected to present work in person unless there are exceptional circumstances that prevent this.

In order to access the AAAI-24 event page you need to register [here](https://aaai.org/aaai-conference/registration/)

AAAI 2024

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. 

technical paper

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 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.

Using Artificial Intelligence to improve teaching and learning benefits greater adaptivity and scalability in education. Knowledge Tracing (KT) is recognized for student modeling task due to its superior performance and application potential in education. To this end, we conceptualize and investigate counterfactual explanation as the connection from XAI for KT to education. Counterfactual explanations offer actionable recourse, are inherently causal and local, and easy for educational stakeholders to understand who are often non-experts. We propose ${\tt KTCF}$, a counterfactual explanation generation method for KT that accounts for knowledge concept relationships, and a post-processing scheme that converts a counterfactual explanation into a sequence of educational instructions. We experiment on a large-scale educational dataset and show our ${\tt KTCF}$ method achieves superior and robust performance over existing methods, with improvements ranging from 5.7% to 34% across metrics. Additionally, we provide a qualitative evaluation of our post-processing scheme, demonstrating that the resulting educational instructions help prevent large skill gaps. Our work demonstrates that counterfactuals have the potential to advance the responsible use of AI in education. Existing XAI works for KT may benefit from educationally grounded conceptualization and developing stakeholder-centered methods.

KTCF: Actionable Recourse in Knowledge Tracing via Counterfactual Explanations for Education

The rapid advancement and integration of artificial
intelligence (AI) into our everyday lives, work, and
classrooms have added demands for K-12 education to ensure
that students are given opportunities to obtain AI
competencies essential for responsible participation in the
AI-driven future. AI literacy encompasses technical
knowledge, ethical awareness, and critical evaluation of AI
tools, as well as the ability to collaborate with AI
systems in creative and productive ways. Research
highlights the importance of age-appropriate approaches
that address foundational AI knowledge, data literacy,
ethics, problem-solving, and creativity, ensuring students
can both use, analyze, critically evaluate, and design AI
solutions for real-world challenges. This not only requires
schools to prepare learners with AI competencies, but also
colleges and universities to ensure that we prepare our
preservice teachers to be AI-ready, who can integrate
technological, pedagogical, and content knowledge in
classroom practice. This paper highlights the importance of
purposefully integrating AI competencies into teacher
education programs and offers practical examples of how
such integration can be achieved.

Integrating AI Competencies into Teacher Education Programs

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, 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 \texttt{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.

A Catalyst Framework for the Quantum Linear System Problem via the Proximal Point Algorithm

Domain generalization (DG) and domain adaptation (DA) for 3D semantic segmentation enable the model to maintain high performance while avoiding labor-intensive and time-consuming annotation of target-domain data.
However, under adverse weather conditions, the injection of spatial noise will affect the reflectivity of LiDAR point clouds, exacerbate domain distribution discrepancies, and degrade the generalization ability of the model.
Current methods mainly rely on sparse convolution-based architecture. Due to its limited receptive field, the model captures varying local geometric information when dealing with point clouds of different sparsities, thereby limiting its transferability.
To this end, we propose \textbf{BeyondSparse}, a novel cross-domain 3D semantic segmentation method under adverse weather that incorporates a state-space model into a 3D sparse convolution-based architecture, sequentially modeling all features to learn domain-invariant representations.
This method consists of two main components: domain feature decoupling and Mamba-based encoder. The former performs feature disentanglement before sequential modeling, while the latter performs global modeling on voxelized point cloud data. In addition, we introduce a token-style augmentation to capture the intrinsic properties of input data.
Extensive experimental results demonstrate that our method outperforms SOTA competitors in both DG and DA tasks, for instance, achieving +4.6\% and +0.8\% mIoU on SynLiDAR$\rightarrow$SemanticSTF.

BeyondSparse: Facilitating Mamba to Enhance Cross-Domain 3D Semantic Segmentation in Adverse Weather

In this paper, we present an active exploration framework for high-fidelity 3D reconstruction that incrementally builds a multi-level uncertainty space and selects next-best-views through an uncertainty-driven motion planner. 
We introduce a \emph{hybrid implicit–explicit representation} that fuses neural fields with Gaussian primitives to jointly capture global structural priors and locally observed details. 
Based on this hybrid state, we derive a \emph{hierarchical uncertainty volume} that quantifies both implicit global structure quality and explicit local surface confidence. 
To focus optimization on the most informative regions, we propose an \emph{uncertainty-driven keyframe selection} strategy that anchors high-entropy viewpoints as sparse attention nodes, coupled with a \emph{viewpoint-space sliding window} for uncertainty-aware local refinement. 
The planning module formulates next-best-view selection as an \emph{Expected Hybrid Information Gain} problem and incorporates a risk-sensitive path planner to ensure efficient and safe exploration. 
Extensive experiments on challenging benchmarks demonstrate that our approach consistently achieves state-of-the-art accuracy, completeness, and rendering quality, highlighting its effectiveness for real-world active reconstruction and robotic perception tasks.

Active3D: Active High-Fidelity 3D Reconstruction via Multi-Level Uncertainty Quantification

Training physics-informed neural networks (PINNs) can be viewed as a multi-task optimization problem, where data-driven and physics-driven loss functions must be simultaneously minimized, despite the potential competition between them. Manually tuning the weight coefficients for various loss terms in PINNs is often time-consuming and lacks a systematic approach. To address this challenge, this work proposes an adaptive loss balancing framework for PINNs, using multi-objective optimization (MOO) algorithms to dynamically balance competing loss terms during training. Specifically, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is integrated into the PINN training process to explore the Pareto front of the multiple objectives. A novel variance-aware relative improvement (VARI) weighting method is proposed to translate Pareto-optimal information into adaptive loss weights. The proposed MOO-VARI method is validated through several examples, where the results show that the MOO-VARI PINN consistently outperforms standard PINN and other state-of-the-art adaptive weighting strategies in terms of convergence speed, predictive accuracy, and parameter estimation performance.

A Multi-Objective Optimization Framework for Adaptive Weighting in Physics-Informed Machine Learning

Explainable AI (XAI) is crucial for building transparent and trustworthy machine learning systems, especially in high-stakes domains. Concept Bottleneck Models (CBMs) have emerged as a promising ante-hoc approach that provides interpretable, concept-level explanations by explicitly modeling human-understandable concepts. However, existing CBMs often suffer from poor locality faithfulness, failing to spatially align concepts with meaningful image regions, which limits their interpretability and reliability. 
In this work, we propose SL-CBM (CBM with Semantic Locality), a novel extension that enforces locality faithfulness by generating spatially coherent saliency maps at both concept and class levels. SL-CBM integrates a $1 \times 1$ convolutional layer with a cross-attention mechanism to enhance alignment between concepts, image regions, and final predictions. Unlike prior methods, SL-CBM produces faithful saliency maps inherently tied to the model’s internal reasoning, facilitating more effective debugging and intervention. Extensive experiments on image datasets demonstrate that SL-CBM substantially improves locality faithfulness, explanation quality, and intervention efficacy while maintaining competitive classification accuracy. Our ablation studies highlight the importance of contrastive and entropy-based regularization for balancing accuracy, sparsity, and faithfulness. Overall, SL-CBM bridges the gap between concept-based reasoning and spatial explainability, setting a new standard for interpretable and trustworthy concept-based models. Our implementation is open-source and can be found at \url{https://github.com/Uzukidd/sl-cbm}.

SL-CBM: Enhancing Concept Bottleneck Models with Semantic Locality for Better Interpretability

The housing market is a classic exchange economy model where each agent on the demand side initially owns an indivisible good (a house) and has a personal preference over all goods. The goal is to find a core-stable allocation that exhausts all mutually beneficial exchanges among subgroups of agents. While this model has been extensively studied in economics and computer science due to its broad applications, little attention has been paid to settings where preferences are unknown and must be learned through repeated interactions.
In this paper, we propose a statistical learning model within the multi-player multi-armed bandit framework, where players (agents) learn their preferences over arms (goods) from stochastic rewards. We introduce the notion of \emph{core regret} for each player as the market objective. We study both centralized and decentralized approaches, proving $\mathcal{O}(\log T / \Delta^2)$ upper bounds on regret, where $T$ is the time horizon and $\Delta$ is the minimum preference gap among players. For the decentralized setting, we also establish a matching lower bound, demonstrating that our algorithm is order-optimal.

Bandit Learning in Housing Markets

With the rapid advancement of large language models (LLMs), aligning them with human values for safety and ethics has become a critical challenge.
This problem is especially challenging when multiple, potentially conflicting human values must be considered and balanced.
Although several variants of existing alignment methods (such as Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO)) have been proposed to address multi-value alignment, they suffer from notable limitations: 1) they are often unstable and inefficient in multi-value optimization; and 2) they fail to effectively handle value conflicts. 
As a result, these approaches typically struggle to achieve optimal trade-offs when aligning multiple values.

To address this challenge, we propose a novel framework called Multi-Value Alignment (MVA).
It mitigates alignment degradation caused by parameter interference among diverse human values by minimizing their mutual information.
Furthermore, we propose a value extrapolation strategy to efficiently explore the Pareto frontier, thereby constructing a set of LLMs with diverse value preferences.
Extensive experiments demonstrate that MVA consistently outperforms existing baselines in aligning LLMs with multiple human values.

Multi-Value Alignment for LLMs via Value Decorrelation and Extrapolation

The performance of optimization-based robot motion planning
algorithms is highly dependent on the initial solutions,
commonly obtained by running a sampling-based planner to
obtain a collision-free path. However, these methods can be
slow in high-dimensional and complex scenes and produce
nonsmooth solutions. Given previously solved path-planning
problems, it is highly desirable to learn their
distribution and use it as a prior for new similar
problems. Several works propose utilizing this prior to
bootstrap the motion planning problem, either by sampling
initial solutions from it, or using its distribution in a
maximum-a-posterior formulation for trajectory
optimization. In this work, we introduce motion planning
diffusion (MPD), an algorithm that learns trajectory
distribution priors with diffusion models. These generative
models have shown increasing success in encoding multimodal
data and have desirable properties for gradient-based
motion planning, such as cost guidance. Given a motion
planning problem, we construct a cost function and sample
from the posterior distribution using the learned prior
combined with the cost function gradients during the
denoising process. Instead of learning the prior on all
trajectory waypoints, we propose learning a lower
dimensional representation of a trajectory using linear
motion primitives, particularly B-spline curves. This
parametrization guarantees that the generated trajectory is
smooth, can be interpolated at higher frequencies, and
needs fewer parameters than a dense waypoint
representation. We demonstrate the results of our method
ranging from simple 2-D to more complex tasks using a 7-DOF
robot arm manipulator. In addition to learning from
simulated data, we also use human demonstrations on a
real-world pick-and-place task. The experiment results show
that diffusion models are strong priors for encoding
multimodal trajectory distributions for optimization-based
motion planning.


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