Singapore

TH08: Algorithms and Systems for Efficient Inference in Generative AI
State-of-the-art Generative AI (GenAI) models now span diverse modalities including language, vision, and audio, and are being deployed across an increasingly wide range of applications. However, their growing size and complexity pose significant challenges for efficient inference, particularly in real-time or resource-constrained settings. This tutorial will introduce participants to a range of techniques that enable high-performance, scalable inference without compromising model accuracy. It will cover practical tools, open-source serving frameworks, and deployment considerations across different hardware platforms, and the underlying principles will be broadly applicable to emerging model architectures. These methods will be illustrated through concrete scenarios, such as low-latency serving of large language models (LLMs), on-device deployment of quantized models, and performance tuning for cost-sensitive applications.

The tutorial is organized into three sessions that collectively build a comprehensive understanding of efficient inference for GenAI models. Session 1 provides an overview of the diverse architectural classes of GenAI models, ranging from decoder-only LLMs to multimodal models. This is followed by an introduction to the multi-layered inference stack, spanning high-level ML frameworks such as PyTorch to low-level hardware instructions — an understanding of which is essential for systematically uncovering performance bottlenecks. This will be accompanied with a demonstration of profiling techniques to analyze these bottlenecks. Session 2 dives into algorithmic, modeling, and systems-level optimizations, covering techniques like efficient attention variants, quantization, speculative decoding, and kernel fusion — while examining how each of these techniques alleviates the bottlenecks discussed in Session 1. Session 3 concludes the tutorial by focusing on practical tools and frameworks that implement many of the previously discussed optimization techniques with accompanying demos. It will highlight vLLM for high-throughput LLM serving, and TensorRT for hardware-optimized compilation across modalities. Together, these sessions will equip participants with the technical depth and tooling needed to deploy GenAI models efficiently in real-world systems.

This tutorial is designed for researchers and practitioners in AI who are interested in understanding and improving the efficiency of GenAI models. It will be particularly valuable for those with expertise in a specific layer of the inference stack, such as modeling, quantization, or infrastructure, and are seeking to develop a holistic perspective connecting algorithmic techniques with system-level and hardware-aware optimizations. Attendees are expected to have basic familiarity with deep learning frameworks (e.g., PyTorch). Prior experience with LLM architectures and deployment is beneficial, but not required.

AAAI 2026

Algorithms and Systems for Efficient Inference in Generative AI

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s 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.* 
[Read more](https://drive.google.com/file/d/1GI_hvOpjswAuYdUTromfeDiPpCcqidwg/view?usp=sharing)

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 ACL 2025". Please be sure to check your spam email folder if you do not see an email confirmation right away.

Please log in

To access this event page, you need to log in with the **email address you registered with**. 

ACL 2025

Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

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.

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.

To access this event page, you are required to register.
Please complete your registration to continue.

We recommend reading [**the registration information**](https://aaai.org/conference/aaai/aaai-26/registration/) first.

**Online Registration Form**: https://aaai.getregistered.net/conference-2026 

Registration Required

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.

Automated planning is a long-standing challenge in AI research, which requires domain models that define the state variables and actions in terms of preconditions and effects. However, the handcrafting of such domain models is widely recognized to be a time-consuming and error-prone process. Therefore, theories and algorithms for automatically learning domain models for planning have been developed over the years. The goal of this tutorial is to provide participants with an overview of state-of-the-art approaches for learning planning domain models under different assumptions, and to enable participants to utilize publicly available frameworks and tools for domain model learning.

The tutorial first introduces the basic concepts for planning domain learning, and situates the problem within the broader context of AI research, relating it to other fields such as Reinforcement Learning (RL) and Knowledge Representation (KR). Then, the tutorial provides an overview of state-of-the-art approaches for learning domain models assuming a given symbolic state representation. The focus is on approaches that learn action preconditions and effects from an input set of trajectories generated by executing actions and observing their effects. The discussed approaches are compared w.r.t. their pros and cons, and their theoretical properties. Subsequently, the tutorial discusses domain model learning approaches that do not assume a symbolic state representation. Such approaches additionally learn to encode state subsymbolic observations (e.g., RGB images) into symbolic representations suitable for planning. A hands-on session guides the participants through the usage of open-source frameworks and tools for domain model learning and evaluation. The tools considered are useful and readily accessible resources for practitioners and researchers who aim to apply domain model learning techniques in possibly different research areas. The tutorial ends by describing methods for online domain model learning, and integrations with RL agents and LLM-based agents.

Participants are expected to have only a basic understanding of automated planning and symbolic domain representation, i.e., that a symbolic state is defined by a set of state variables, and a symbolic action is specified by a set of preconditions and effects. Further information about the tutorial is available at https://domain learning.github.io.

Domain Model Learning for Automated Planning: Introduction & Domain Learning Basics 

Domain Model Learning for Automated Planning: Learning State Abstractions

Domain Model Learning for Automated Planning: Offline Learning of Action Models

Domain Model Learning for Automated Planning: Hands-on Session

Domain Model Learning for Automated Planning: Active Learning and Open Challenges 

LH03: The Verification of Neural Networks Competition (VNN-COMP): A Lab for Benchmark Proposers, Verification Tool Participants, and the Broader AI Community

Associative Memories (AMs) like Hopfield Networks are elegant models for describing fully recurrent neural networks that store and retrieve information. Recent theoretical advances have revealed deep connections between AMs and modern AI architectures such as Transformers and diffusion models, opening new possibilities for interpreting and designing neural networks. This tutorial provides an approachable introduction to AMs through three components: theoretical foundations from classical to Dense Associative Memories, hands-on implementations using a universal Lagrangian framework for designing AMs (including coding Transformers as AMs), and practical applications connecting AMs to kernel methods, density estimation, and deep clustering.

We provide a technical, hands-on level tutorial for the general attendee of AAAI i.e., graduate students, industry researchers, faculty, and developers. However, we expect our tutorial to additionally appeal to physicists, neuroscientists, and other researchers interested in brain-inspired models of computation. From a student’s perspective, the ordered priorities of this tutorial are to: (1) build intuition, (2) understand the fundamental tooling needed to work with these systems, and (3) engage with simple proofs that highlight their elegant behavior.

The tutorial is accompanied by a website containing links to all lecture notes, code examples, and presentation slides.

Modern Methods in Associative Memory

In recent years, multi-modal time series analysis has gained increasing attention for integrating insights from diverse data sources, enhancing both predictive performance and explanations across various domains. This growing interest is driven by the rapid production of heterogeneous data in the real world, where temporal signals often come not only in numerical form but are also accompanied by complementary modalities such as text, images, or structured metadata. By effectively leveraging information from different modalities, researchers can discover richer patterns and improve model performance in the application. We plan to present a comprehensive half-day tutorial at AAAI 2026, tailored for researchers and practitioners interested in multi-modal time series analysis. This tutorial provides insights into the theoretical and practical aspects of multi-modal time series, covering data characteristics and cross-modality modeling strategies for various downstream tasks. Attendees will also learn best practices for applying multi-modal time series analysis to real-world domains such as finance, healthcare, and transportation. Through these examples, participants will gain a clearer understanding of how to move from theoretical modeling to impactful deployment. The tutorial offers a comprehensive and in-depth understanding, practical skill development, and networking opportunities, connecting theory with real-world applications.

Multi-modal Time Series Analysis: Methods, Datasets, and Applications

Deterministic search and planning typically operate on graphs where each edge has a given cost, aiming to find a path of minimal cost. Heuristic search has developed tools for solving this path-finding problem, including the A* algorithm. However, the objective of search and planning is often much more complex in real life, for example, because one needs to trade-off between different costs. Multi-objective search and planning operate on graphs where each edge has two or more given costs, aiming to find paths that trade-off between the different costs in the form of a Pareto frontier of paths.

For example, transporting hazardous material requires trading off different costs for each street, such as its length and the number of residents that would be exposed to the hazardous material in case of an accident. Other examples include route planning for vehicles and robots (which involves trading off energy consumption and travel time), planning power transmission lines (which involves trading off power-generation cost and power loss), and scheduling satellites and routing packets in computer networks (which involves trading off profit and fairness).

This tutorial will provide an overview of the multi-objective search problem and summarize recent progress in this fast-moving research area. We will cover theoretical foundations, practical algorithms, and challenges that commonly arise in practice in a way that is accessible to all AI researchers and students. Our target audience is anyone interested in search and planning who wants to learn about this fascinating emerging field. More information on the tutorial and a detailed schedule can be found at https://sites.google.com/usc.edu/aaai24-mos-tutorial/home

Recent Advances in Multi-Objective Search

This tutorial provides a comprehensive and unified overview of the rapidly evolving field of LLM-based Multi-Agent Systems (LaMAS). As LLMs become more capable as autonomous agents, the next frontier is orchestrating their collective intelligence to solve complex problems. This tutorial systematically bridges the foundational principles of multi-agent reinforcement learning (MARL) and game theory with the practical design and engineering of modern LaMAS.

The tutorial begins with the theoretical underpinnings of multi-agent interaction before moving to the core components of a modern LLM agent, including reasoning, planning, and tool use. The central part delves into LaMAS architectures, covering communication protocols, coordination strategies, and collaborative optimization. The tutorial concludes by exploring the frontiers of the field, discussing evaluation methodologies, open-source frameworks, and real-world applications. Attendees will gain a robust theoretical understanding, a map of the current research landscape, and the practical knowledge needed to build and evaluate their own LaMAS.

Next from AAAI 2026

Domain Model Learning for Automated Planning: Introduction & Domain Learning Basics

Similar lecture

Energy Considerations of Large Language Model Inference and Efficiency Optimizations

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES