Challenge: Large language models (LLMs) have demonstrated remarkable capabilities to comprehend human intentions, engage in reasoning, and design planning-like behavior.
Approach: They propose a framework that equips large language models with tool-use capabilities . they propose LLaMA and Chat-GLM as controllers, and a model-based agent framework .
Outcome: The proposed framework equips open-source LLMs with tool-use capabilities . it provides a user-friendly system library with a customizable engine design .

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Towards large language model-based personal agents in the enterprise: Current trends and open problems (2023.findings-emnlp)

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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.
AgentTuning: Enabling Generalized Agent Abilities for LLMs (2024.findings-acl)

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Challenge: Open large language models (LLMs) with great performance in various tasks are far inferior to commercial models such as ChatGPT and GPT-4 when acting as agents to tackle complex tasks in the real world.
Approach: They propose a method to enhance the agent capabilities of LLMs while maintaining their general abilities.
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AgentGym: Evaluating and Training Large Language Model-based Agents across Diverse Environments (2025.acl-long)

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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.
Towards Effective and Efficient Multi-Agent Language Model Systems: Foundations, Prospects, and Applications (2026.acl-tutorials)

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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 .
AutoAgent: A Fully-Automated and Zero-Code Framework for LLM Agents (2026.findings-acl)

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Challenge: Large Language Model (LLM) agents have demonstrated remarkable capabilities in task automation and intelligent decision-making.
Approach: They propose a Fully-Automated and highly Self-Developing framework that enables users to create and deploy LLM agents using natural language alone.
Outcome: AutoAgent is a fully-automated and highly self-developing framework that enables users to create and deploy LLM agents using natural language alone.
A Survey on LLM-powered Agents for Recommender Systems (2025.findings-emnlp)

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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 .
Enhancing LLM Capabilities Beyond Scaling Up (2024.emnlp-tutorials)

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Challenge: general-purpose large language models (LLMs) are expanding in scale and access to unpublic training data.
Approach: This tutorial aims to examine the capabilities of general-purpose large language models . authors discuss adaptation of LLMs to address conflicts, defense against attacks .
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ToolCPT: Improving Tool Utilization in LLM Agents via Continuous Pre-training (2026.findings-acl)

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Challenge: Current approaches to enhancing tool use for LLM-based agents focus on post-training fine-tuning or test-time context extension.
Approach: They propose to enhance tool knowledge for LLM-based agents during continuous pre-training . they curate 5.1 million code artifacts from large-scale, high-quality code repositories .
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Enhancing Open-Domain Task-Solving Capability of LLMs via Autonomous Tool Integration from GitHub (2025.acl-long)

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Challenge: Existing approaches lack flexibility to address diverse and ever-evolving user queries in open domains.
Approach: They propose to evaluate LLMs on open-domain knowledge that requires tools to solve diverse and ever-evolving user queries.
Outcome: The proposed system outperforms baselines in the open domain task-solving benchmark.
Middleware for LLMs: Tools Are Instrumental for Language Agents in Complex Environments (2024.emnlp-main)

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Challenge: Large language models (LLMs) are generalist agents capable of operating within complex environments.
Approach: They propose a class of tools that can serve as a middleware layer shielding LLMs from environmental complexity.
Outcome: The proposed tool can shield the LLM from environmental complexity in two representative complex environments.

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