Singapore

Optimization is a foundational pillar of artificial intelligence (AI), underpinning core techniques in planning, scheduling, decision-making, and machine learning. Yet despite decades of algorithmic advances, widespread adoption of state-of-the-art optimization solvers remains limited by the substantial expertise required for effective modeling and solving. This expertise barrier means that powerful optimization tools remain largely inaccessible to non-experts, with most users of leading solvers holding advanced degrees.

Recent advances in generative AI, particularly large language models (LLMs), offer a promising new path for democratizing optimization. By automating key steps in the optimization pipeline – from model formulation through solver configuration to model validation – LLMs promise to broaden access to powerful optimization tools. However, these models rarely work out of the box for complex reasoning tasks like optimization. 

This tutorial surveys emerging research at the intersection of LLMs and mathematical optimization, highlighting both practical systems and open research questions.  We will provide a comprehensive overview of how LLMs can support each stage of the optimization pipeline, including model formulation, solver configuration, and validation. The tutorial is designed to be accessible to attendees without prior experience in either field, offering both conceptual frameworks and practical insights for this rapidly evolving area of research.

AAAI 2026

LLMs for Optimization: Modeling, Solving, and Validating with Generative AI

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.



tutorial

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.

BSLM: A Bi-Level Speech-Language Model for the Joint Modeling of Discrete and Continuous Tokens

Granular Control of Nonverbal Expressions for Achieving Natural Emotional Text-to-Speech

AudioBERTScore: Objective Evaluation of Environmental Sound Synthesis Based on Similarity of Audio Embedding Sequences

Can You Hear Naples? Building and Benchmarking a Neapolitan Speech Corpus

AudioRAG: A Challenging Benchmark for Audio Reasoning and Information Retrieval

Generalizable speech deepfake detection via meta-learned LoRA

Toward High-Quality Cross-lingual Text-to-Speech Synthesis In Low-Resource Scenarios

Semi-supervised Acoustic Scene Classification under Spatial-Temporal Variability with a CRNN-based Model

Online Independent Low-Rank Matrix Analysis as a Lightweight and Trainable Model for Real-Time Multichannel Music Source Separation

There is a growing debate about the implications of multiplicity—conflicting behavior among a set of “good”’ models—for algorithmic decision-making. On one hand, there are concerns over unfair treatment due to conflicting predictions and explanations, further exacerbated in the generative AI ecosystem. Yet, on the other hand, multiplicity also offers the potential to find less discriminatory and more interpretable models. In this tutorial, we aim to increase awareness of different perspectives on multiplicity and position them among broader discussions in the community. Specifically, the main goals of our tutorial are:

* Highlighting the phenomenon of multiplicity in machine learning and calling attention to its growing literature.
* Linking multiplicity with uncertainty and churn, presenting a unified view of various measures of instability.
* Discussing the implications of multiplicity for fairness and explainability in algorithmic decision-making.
* Recognizing exacerbated concerns and new forms of multiplicity in the age of generative AI.
* Engaging the community on when and how to address multiplicity in various practical scenarios.
* Identifying open questions and motivating future research directions on multiplicity.

Next from AAAI 2026

BSLM: A Bi-Level Speech-Language Model for the Joint Modeling of Discrete and Continuous Tokens

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Enhancing Decision Making Through the Integration of Large Language Models and Operations Research Optimization

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