Challenge: Multi-agent systems powered by large language models still face challenges . tutorial focuses on three core components to build effective and efficient systems .
Approach: This tutorial introduces recent advances in building effective and efficient multi-agent LLM systems . it focuses on three core components: model distillation, dynamic routing, memory- and compute efficient serving .
Outcome: This tutorial introduces state-of-the-art techniques for building efficient and efficient multi-agent LLM systems . it covers coordination and communication among agents, crucial for collective performance .

Similar Papers

AgentGym: Evaluating and Training Large Language Model-based Agents across Diverse Environments (2025.acl-long)

Copied to clipboard

Challenge: Large language models (LLMs) are promising foundations to build generally-capable agents . however, the community lacks a unified interactive framework that covers diverse environments for comprehensive evaluation of agents.
Approach: They propose a framework that features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction.
Outcome: The proposed framework features 7 real-world scenarios, 14 environments, and 89 tasks for unified, real-time, and concurrent agent interaction.
Multi-Agent Autonomous Driving Systems with Large Language Models: A Survey of Recent Advances, Resources, and Future Directions (2025.findings-emnlp)

Copied to clipboard

Challenge: Large Language Models (LLMs) are used to assist with driving decisions, but they face limitations in perception and computational demands.
Approach: They propose a survey of LLM-based multi-agent ADSs and their applications . they analyze agent-human interactions in scenarios where LLM agents engage with humans .
Outcome: The proposed approach reduces human intervention and improves safety and efficiency.
Towards large language model-based personal agents in the enterprise: Current trends and open problems (2023.findings-emnlp)

Copied to clipboard

Challenge: Existing large language models (LLMs) are brittle to input changes and can produce inconsistent results for the same inputs.
Approach: They propose to use large language models to reason about complex goals and orchestrate a set of pluggable tools or APIs to accomplish a goal.
Outcome: The proposed use cases have many open problems in an exciting area of NLP research, such as trust and explainability, consistency and reproducibility, and the need for new metrics and benchmarks.
A Survey on LLM-powered Agents for Recommender Systems (2025.findings-emnlp)

Copied to clipboard

Challenge: Large Language Models have demonstrated remarkable capabilities in natural language understanding, reasoning, and generation.
Approach: They present a comprehensive synthesis of large language models and their applications . they dissect a four-module agent architecture and review representative designs .
Outcome: The proposed models address fundamental challenges in traditional recommender systems . they include limited comprehension of complex user intents, insufficient interaction capabilities .
Multimodal Large Language Models for Human-AI Interaction: Foundations, Agents, and Inclusive Applications (2026.eacl-tutorials)

Copied to clipboard

Challenge: This tutorial presents foundations, agentic capabilities, and inclusive applications of multimodal large language models.
Approach: This tutorial presents foundations, agentic capabilities, and inclusive applications of multimodal large language models.
Outcome: This tutorial covers foundations, agentic capabilities, and inclusive applications of multimodal large language models.
How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances (2023.emnlp-main)

Copied to clipboard

Challenge: Large language models (LLMs) are impressive in solving tasks, but they can quickly be outdated after deployment.
Approach: They provide a review of recent advances in aligning deployed large language models with the ever-changing world knowledge.
Outcome: The proposed models can be used to perform various tasks directly through in-context learning or for further fine-tuning for domain-specific uses.
Language Agents: Foundations, Prospects, and Risks (2024.emnlp-tutorials)

Copied to clipboard

Challenge: Language agents are autonomous agents that can follow language instructions to perform diverse tasks in real-world or simulated environments.
Approach: They propose to provide a conceptual framework for language agents and a comprehensive discussion on key topics.
Outcome: The proposed tutorial provides a conceptual framework of language agents and comprehensive discussion on important topic areas.
LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey (2026.findings-acl)

Copied to clipboard

Challenge: Recent advances in large language models (LLMs) have sparked growing interest in building fully autonomous agents.
Approach: They propose to integrate human-provided information, feedback, or control into the agent system to enhance system performance, reliability, and safety.
Outcome: The proposed systems improve system performance, reliability, and safety by integrating human-provided information, feedback, or control into the agent system.
LLM Agents for Education: Advances and Applications (2025.findings-emnlp)

Copied to clipboard

Challenge: Large Language Model (LLM) agents are transforming education by automating complex tasks and enhancing both teaching and learning processes.
Approach: This survey analyzes recent advances in applying Large Language Model agents to educational settings . it highlights ethical issues, hallucination and overreliance, and integration with existing ecosystems .
Outcome: The authors analyze the technologies enabling LLM agents and highlight key challenges in deploying them in educational settings.
Adaptation of Large Language Models (2025.naacl-tutorial)

Copied to clipboard

Challenge: a tutorial on adaptation of large language models addresses the growing demand for models that go beyond static capabilities.
Approach: This tutorial will provide an overview of dynamic, domain-specific, and task-adaptive LLM adaptation techniques.
Outcome: This tutorial will outline dynamic, domain-specific, and task-adaptive LLM adaptation techniques.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations