Singapore

Current text-to-image models face challenges in visual text rendering: text encoders like CLIP and T5 lack glyph-level understanding and often struggle to distinguish between the specific words to be rendered and their intended semantic meaning within prompts. In addition, inconsistencies between the base model and its plugins further compromise the quality of synthesized images. In this paper, we enhance the existing text-to-image method by addressing the following aspects: (1) Text-Glyph Alignmentin a Visual Question Answering (VQA) manner to enable glyph understanding for the text encoder. This involves establishing an explicit alignment between the representations of the glyphs and their detailed attribute descriptions, which boosts the model&#39;s ability to capture fine-grained visual features of the text.
(2) Accurate and harmony visual text rendering: integrating pre-aligned glyph-visual embeddings with semantic text tokens through the Multimodal Diffusion Transformer(MMDiT) synchronously, ensuring coherent feature alignment and enhancing both the robustness and fidelity of visual text rendering. (3) Image Aesthetic Refinement: leveraging a multisource data training strategy that incorporates diverse, high-quality image-text pairs from various domains, exposing the model to extensive linguistic and visual diversity while maintaining superior aesthetic quality throughout training. Our experiments demonstrate that the proposed approach significantly outperforms the existing state-of-the-art method.

AAAI 2026

ViType: High-Fidelity Visual Text Rendering via Glyph-Aware Multimodal Diffusion

multilingual typography generation

multimodal diffusion transformer

representation alignment loss

visual question answering (vqa) for text rendering

glyph-aware multimodal diffusion

text-glyph alignment

It is my great pleasure and honor to welcome you to the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), held in San Diego, California, USA, July 2–7, 2026. ACL 2026 continues our fields tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.

It is exciting to see the many developments in the field reflected at our conference, such as the advancement of large language models (born out of our community) into many application directions. This is not without challenges, partly due to unsolved problems, and partly due to misuse of the technology. All this is reflected well at ACL 2026. The conference also reflects that the field is not only about advancing technology, but also the science of fundamentally understanding language and how it is use by many different speaker communities. 
[Full Message from General Chair Philipp Koehn](https://drive.google.com/file/d/1QiJFOmMjvDgsDo3i1XzlQnImxe3RU_Cy/view?usp=sharing) 
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ACL 2026

The 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026) will take place in San Diego, California, from July 2 - 7, 2026.

Current text-to-image models face challenges in visual text rendering: text encoders like CLIP and T5 lack glyph-level understanding and often struggle to distinguish between the specific words to be rendered and their intended semantic meaning within prompts. In addition, inconsistencies between the base model and its plugins further compromise the quality of synthesized images. In this paper, we enhance the existing text-to-image method by addressing the following aspects: (1) Text-Glyph Alignmentin a Visual Question Answering (VQA) manner to enable glyph understanding for the text encoder. This involves establishing an explicit alignment between the representations of the glyphs and their detailed attribute descriptions, which boosts the model's ability to capture fine-grained visual features of the text.
(2) Accurate and harmony visual text rendering: integrating pre-aligned glyph-visual embeddings with semantic text tokens through the Multimodal Diffusion Transformer(MMDiT) synchronously, ensuring coherent feature alignment and enhancing both the robustness and fidelity of visual text rendering. (3) Image Aesthetic Refinement: leveraging a multisource data training strategy that incorporates diverse, high-quality image-text pairs from various domains, exposing the model to extensive linguistic and visual diversity while maintaining superior aesthetic quality throughout training. Our experiments demonstrate that the proposed approach significantly outperforms the existing state-of-the-art method.

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.

To access this event page, you need to log in with the **email address you registered with**. Access credentials will be sent to your email from Underline - subject line "Welcome to AAAI 2026". Please be sure to check your spam email folder if you do not see an email confirmation right away.

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**Online Registration Form**: https://aaai.getregistered.net/conference-2026 

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

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

Federated clustering addresses the critical challenge of extracting patterns from decentralized, unlabeled data but is hampered by the flaw that these approaches force an unacceptable compromise between performance and privacy: \textit{transmitting rich representations like embeddings risks sensitive data leakage, while sharing only abstract cluster prototypes leads to diminished model accuracy}. To resolve this dilemma, we propose Structural Privacy-Preserving Federated Graph Clustering (SPP-FGC), a novel algorithm that innovatively leverages local structural graphs as the primary medium for privacy-preserving knowledge sharing, thus moving beyond the limitations of conventional techniques. Our framework operates on a clear client-server logic; on the client-side, each participant constructs a private structural graph that captures intrinsic data relationships, which the server then securely aggregates and aligns to form a comprehensive global graph from which a unified clustering structure is derived. The framework offers two distinct modes to suit different needs. SPP-FGC is designed as an efficient one-shot method that completes its task in a single communication round, ideal for rapid analysis. For more complex, unstructured data like images, SPP-FGC+ employs an iterative process where clients and the server collaboratively refine feature representations to achieve superior downstream performance. Extensive experiments demonstrate that our framework achieves state-of-the-art performance, improving clustering accuracy by up to 10\% (NMI) over federated baselines while maintaining provable privacy guarantees. The code will be available at https://anonymous.4open.science/r/SPPFGC-B47EA310/.

Towards Federated Clustering: A Client-wise Private Graph Aggregation Framework

Can we train a 3D molecule generator using data from dense regions to generate samples in sparse regions? This challenge can be framed as an out-of-distribution (OOD) generation problem. While prior research on OOD generation predominantly targets property shifts, structural shifts, such as differences in molecular scaffolds or functional groups, represent an equally critical source of distributional shifts. This work introduces the Geometric OOD Diffusion Model (_GOOD_), a novel diffusion-based framework that enables training on data-abundant molecular distributions while generalizing to data-scarce distributions under distributional structural shifts. Central to our approach is a designated equivariant asymmetric autoencoder to capture distributional structural priors. The asymmetric design allows the model to generalize to unseen structural variations by capturing distributional priors representing distinct distributions. The encoded structural-grained priors guide generation toward sparse regions without requiring explicit training on such data. Evaluated across standard benchmarks encompassing OOD structural shifts (e.g., scaffolds, rings), GOOD achieves an improvement of 12.6% in success rate, defined based on molecular validity, uniqueness, and novelty. Furthermore, the framework demonstrates promising performance and generalization on canonical fragment-based drug design tasks, highlighting its utility in learning-based molecular discovery.

Distributional Priors Guided Diffusion for Generating 3D Molecules in Low Data Regimes

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.

QuanTaxo: A Quantum Approach to Self-Supervised Taxonomy Expansion

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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