Singapore

A taxonomy is a hierarchical graph containing knowledge to provide valuable insights for various web applications. However, the manual construction of taxonomies requires significant human effort. As web content continues to expand at an unprecedented pace, existing taxonomies risk becoming outdated, struggling to incorporate new and emerging information effectively. As a consequence, there is a growing need for dynamic taxonomy expansion to keep them relevant and up-to-date. Existing taxonomy expansion methods often rely on classical word embeddings to represent entities. However, these embeddings fall short of capturing hierarchical polysemy, where an entity’s meaning can vary based on its position in the hierarchy and its surrounding context. To address this challenge, we introduce QuanTaxo, an innovative quantum-inspired framework for taxonomy expansion. QuanTaxo encodes entity representations in quantum space, effectively modeling hierarchical polysemy by leveraging the principles of Hilbert space to capture interference effects between entities, yielding richer and more nuanced representations. Comprehensive experiments on five real-world benchmark datasets show that QuanTaxo significantly outperforms classical embedding models, achieving substantial improvements of 12.3% in accuracy, 11.2% in Mean Reciprocal Rank (MRR), and 6.9% in Wu \&amp; Palmer (Wu\&amp;P) metrics across ten classical embedding-based baselines.

AAAI 2026

QuanTaxo: A Quantum Approach to Self-Supervised Taxonomy Expansion

nlp: applications

krr: ontologies

dmkm: linked open data

knowledge graphs & kb completion

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible. The experience for on-site participants would closely approximate a normal pre-COVID *ACL conference, with 5-6 thematically organized parallel sessions and live Q/A and interactive discussion immediately after the talks. Presentations by virtual participants will be equitably interleaved with those of on-site participants, projected on the auditorium screens as if on-site, and also followed immediately by live Q/A and interactive discussion at a time during reasonable waking hours for the virtual presenter. For all participants, on-site and virtual, who are unable to attend a session due to either time-zone issues or because they are participating in another session live, talk recordings and slides will be available online at a minimum after the live presentation (and in many cases before as well), and questions may be submitted in advance on session-specific discussion boards and answered live in session with the usual visual aids if desired.

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EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

A taxonomy is a hierarchical graph containing knowledge to provide valuable insights for various web applications. However, the manual construction of taxonomies requires significant human effort. As web content continues to expand at an unprecedented pace, existing taxonomies risk becoming outdated, struggling to incorporate new and emerging information effectively. As a consequence, there is a growing need for dynamic taxonomy expansion to keep them relevant and up-to-date. Existing taxonomy expansion methods often rely on classical word embeddings to represent entities. However, these embeddings fall short of capturing hierarchical polysemy, where an entity’s meaning can vary based on its position in the hierarchy and its surrounding context. To address this challenge, we introduce QuanTaxo, an innovative quantum-inspired framework for taxonomy expansion. QuanTaxo encodes entity representations in quantum space, effectively modeling hierarchical polysemy by leveraging the principles of Hilbert space to capture interference effects between entities, yielding richer and more nuanced representations. Comprehensive experiments on five real-world benchmark datasets show that QuanTaxo significantly outperforms classical embedding models, achieving substantial improvements of 12.3% in accuracy, 11.2% in Mean Reciprocal Rank (MRR), and 6.9% in Wu \& Palmer (Wu\&P) metrics across ten classical embedding-based baselines.

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.

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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.

We propose a new theoretical foundation for artificial intelligence (AI) and machine learning (ML), building on ideas in pure mathematics relating to categories and functors. 
This paper builds on our AAAI 2025 tutorial Thinking with Functors: Category Theory for A(G)I, which provides background material. 
In addition, our recent papers on intuitionistic j-do calculus in Topos Causal Models} and GAIA: Categorical Foundations of Generative AI, illustrate how to generalize well-known formalisms in AI, 
such as causal inference and deep learning, to a category-theoretic setting.

Rethinking AI: From Functions to Functors

This perspective article discusses an emerging research direction: to what extent can foundation models yield usable structure for modeling the physical world? We offer a Markovian formulation of structured world models and outline the notion of multi-level hybrid world representations that support compositional structure. We then review and suggest possible discovery paradigms, spanning distillation, interaction-driven continual learning, and ensemble learning.

Discovering Hybrid World Representations with Co-Evolving Foundation Models

Video-to-Music generation seeks to generate musically appropriate background music that enhances audiovisual immersion for videos. However, current approaches suffer from two critical limitations: 1) incomplete representation of video details, leading to weak alignment, and 2) inadequate temporal and rhythmic correspondence, particularly in achieving precise beat synchronization. To address the challenges, we propose Video Echoed in Music (VeM), a latent music diffusion that generates high-quality soundtracks with semantic, temporal, and rhythmic alignment for input videos. To capture video details comprehensively, VeM employs a hierarchical video parsing that acts as a music conductor, orchestrating multi-level information across modalities. Modality-specific encoders, coupled with a storyboard-guided cross-attention mechanism (SG-CAtt), integrate semantic cues while maintaining temporal coherence through position and duration encoding. For rhythmic precision, the frame-level transition-beat aligner and adapter (TB-As) dynamically synchronize visual scene transitions with music beats. We further contribute a novel video-music paired dataset sourced from e-commerce advertisements and video-sharing platforms, which imposes stricter transition-beat synchronization requirements. Meanwhile, we introduce novel metrics tailored to the task. Experimental results demonstrate superiority, particularly in semantic relevance and rhythmic precision.

Video Echoed in Music: Semantic, Temporal, and Rhythmic Alignment for Video-to-Music Generation

In the federated clustering task, structural heterogeneity across clients inevitably impedes effective multi-source information sharing. To solve this issue, Personalized Federated Learning (PFL) has emerged as a potentially effective solution for image and text clustering. Unlike Euclidean data, graph-structured data exhibits diverse and fragile local patterns, which widely exist in real-world scenarios. Multi-graph data analysis in the federated learning setting is challenging and important, yet remains underexplored. This motivates us to propose a novel **PER**sonalized **F**ederated graph-**E**vel **C**lustering **T**work (PERFECT), which generates a specialized aggregation strategy for each client by uploading key model parameters and representative samples without sharing private information. Specifically, on each client, we first reconstruct privacy-preserving representative samples in a min-max optimization manner, and then upload these samples to the server for subsequent personalized parameter aggregation. On the server, we first extract graph-level embeddings from the uploaded data, and then estimate affinities among multiple learned embeddings to formulate a personalized aggregation strategy for each client. Subsequently, to help each local model better identify the cluster boundaries, we utilize clustering-wise gradient to update the key components in the personalized model parameters from the server. Extensive experimental results have demonstrated the effectiveness and superiority of PERFECT over its competitors.

Personalized Federated Graph-Level Clustering Network

Current image fusion methods struggle to adapt to real-world environments encompassing diverse degradations with spatially varying characteristics. To address this challenge, we propose a robust fusion controller (RFC) capable of achieving degradation-aware image fusion through fine-grained language instructions, ensuring its reliable application in adverse environments. Specifically, RFC first parses language instructions to innovatively derive the functional condition and the spatial condition, where the former specifies the degradation type to remove, while the latter defines its spatial coverage. Then, a composite control priori is generated through a multi-condition coupling network, achieving a seamless transition from abstract language instructions to latent control variables. Subsequently, we design a hybrid attention-based fusion network to aggregate multi-modal information, in which the obtained composite control priori is deeply embedded to linearly modulate the intermediate fused features. To ensure the alignment between language instructions and control outcomes, we introduce a novel language-feature alignment loss, which constrains the consistency between feature-level gains and the composite control priori. Extensive experiments on publicly available datasets demonstrate that our RFC is robust against various composite degradations, particularly in highly challenging flare scenarios.

Robust Fusion Controller: Degradation-Aware Image Fusion with Fine-Grained Language Instructions

Time series data plays a pivotal role in a wide variety of fields but faces challenges related to privacy concerns. Recently, synthesizing data via diffusion models is viewed as a promising solution. However, existing methods still struggle to capture long-range temporal dependencies and complex channel interrelations. In this research, we aim to utilize the sequence modeling capability of a State Space Model called Mamba to extend its applicability to time series data generation. We firstly analyze the core limitations in State Space Model, namely the lack of consideration for correlated temporal lag and channel permutation. Building upon the insight, we propose Lag Fusion Mamba and Permutation Scanning Mamba, which enhance the model's ability to discern significant patterns during the denoising process. Theoretical analysis reveals that both variants exhibit a unified matrix multiplication framework with the original Mamba, offering a deeper understanding of our method. Finally, we integrate two variants and introduce Diffusion Mamba for Time Series (DiM-TS), a high-quality time series generation model that better preserves the temporal periodicity and inter-channel correlations. Comprehensive experiments on public datasets demonstrate the superiority of DiM-TS in generating realistic time series while preserving diverse properties of data.

DiM-TS: Bridge the Gap Between Selective State Space Models and Time Series for Generative Modeling

Large Language Model (LLM)-based multi-agent systems are increasingly used to simulate human interactions and solve collaborative tasks. A common practice is to assign agents with personas to encourage behavioral diversity. However, this raises a critical yet underexplored question: do personas introduce biases into multi-agent interactions? This paper presents a systematic investigation into persona-induced biases in multi-agent interactions, with a focus on social traits like trustworthiness (how an agent's opinion is received by others) and insistence (how strongly an agent advocates for its opinion). Through a series of controlled experiments in collaborative problem-solving and persuasion tasks, we reveal that (1) LLM-based agents exhibit biases in both trustworthiness and insistence, with personas from historically advantaged groups (e.g., men and White individuals) perceived as less trustworthy and demonstrating less insistence; and (2) agents exhibit significant in-group favoritism, showing a higher tendency to conform to others who share the same persona. These biases persist across various LLMs, group sizes, and numbers of interaction rounds, highlighting an urgent need for awareness and mitigation to ensure the fairness and reliability of multi-agent systems.

From Single to Societal: Analyzing Persona-Induced Bias in Multi-Agent Interactions

We introduce a conceptual framework and provide considerations for the institutional design of AI incident reporting (IR) systems, i.e., processes for collecting information about safety- and rights-related events caused by general-purpose AI. As general-purpose AI systems are increasingly adopted, they are causing more real-world harms and displaying the potential to cause significantly more dangerous incidents—events that did or could have caused harm to individuals, property, or the environment. Through a literature review, we develop a framework for understanding the institutional design of AI incident reporting systems, which includes seven dimensions: policy goal, actors submitting and receiving reports, type of incidents reported, level of risk materialization, enforcement of reporting, anonymity of reporters, and post-reporting actions. We then examine nine case studies of incident reporting in safety-critical industries to extract design considerations for AI incident reporting in the United States. We discuss, among other factors, differences in systems operated by regulatory vs. non-regulatory government agencies, near miss reporting, the roles of mandatory reporting thresholds and voluntary reporting channels, how to enable safety learning after reporting, sharing incident information, and clarifying legal frameworks for reporting. Our aim is to inform researchers and policymakers about when particular design choices might be more or less appropriate for AI incident reporting.

Designing Incident Reporting Systems for Harms from General-Purpose AI

There is increasing interest in applying artificial intelligence (AI) to automate and support complex decision-making tasks. However, it remains unclear how algorithms compare to human judgment in contexts requiring semantic understanding and domain expertise. We examine this in the context of the judge assignment problem --- matching submissions to suitably qualified evaluators --- at a prominent U.S. university startup competition. Awarding over $\textdollar$500,000 annually, this is a real-world setting where high-quality judge assignment is critical. We develop and deploy HLSE (Hybrid Lexical–Semantic Similarity Ensemble), an AI-based approach, at the competition and compare algorithmic against human expert assignments by collecting blinded match quality scores from judges for $309$ judge-venture matches. Using a Mann–Whitney U statistic based test, we found no statistically significant difference in assignment quality between the two approaches ($AUC=0.48, p=0.40$). On average, algorithmic matches are rated $3.90$ and manual matches are rated $3.94$ on a $5$-point scale, where $5$ indicates an excellent match. Furthermore, manual assignments that took a full week in past years can be completed in under ten minutes by the algorithm during deployment. These results demonstrate that HLSE achieves human expert level matching quality while offering greater scalability and efficiency, underscoring the potential of AI-driven solutions to robustly support and enhance human decision-making for judge assignment in high-stakes settings.

Who Is a Better Matchmaker? Human vs. Algorithmic Judge Assignment in a High-Stakes Startup Competition

This paper presents a theoretically grounded optimization
framework for neural network training that integrates an
Exponentially Decaying Learning Rate with Lyapunov-based
stability analysis. We develop a dynamic learning rate
algorithm and prove that it induces connected and stable
descent paths through the loss landscape by maintaining the
connectivity of super-level sets Sλ = {θ ∈ ℝn : ℒ(θ) ≥ λ}.
Under the condition that the Lyapunov function V(θ) = ℒ(θ)
satisfies Δ V(θ) ⋅ Δ ℒ(θ) ≥ 0, we establish that these
super-level sets are not only connected but also
equiconnected across epochs, providing uniform topological
stability. We further derive convergence guarantees using
a second-order Taylor expansion and demonstrate that our
exponentially scheduled learning rate with gradient-based
modulation leads to a monotonic decrease in loss. The
proposed algorithm incorporates this schedule into a
stability-aware update mechanism that adapts step sizes
based on both curvature and energy-level geometry. This
work formalizes the role of topological structure in
convergence dynamics and introduces a provably stable
optimization algorithm for high-dimensional, non-convex
neural networks.

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Rethinking AI: From Functions to Functors

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TEMP: Taxonomy Expansion with Dynamic Margin Loss through Taxonomy-Paths

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Next from AAAI 2026

Rethinking AI: From Functions to Functors

Similar lecture

TEMP: Taxonomy Expansion with Dynamic Margin Loss through Taxonomy-Paths

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads