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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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.
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 .
Outcome: This tutorial aims to examine the evolution of general-purpose large language models (LLMs) the authors argue that the evolution is dependent on the availability of training data and the scale of the models.
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 .
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Fundamental Capabilities of Large Language Models and their Applications in Domain Scenarios: A Survey (2024.acl-long)

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Challenge: Existing benchmark-based evaluations cannot accurately reflect the performance of real-world applications.
Approach: They propose a reliable strategy for domains to choose more robust LLMs for real-world applications.
Outcome: The proposed strategy addresses the challenges faced by domains to choose more robust LLMs for real-world applications.
How Do Large Language Models Capture the Ever-changing World Knowledge? A Review of Recent Advances (2023.emnlp-main)

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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.
Small LLMs Are Weak Tool Learners: A Multi-LLM Agent (2024.emnlp-main)

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Challenge: Large Language Models (LLMs) have revolutionized natural language processing with impressive capabilities, but they lack domain specificity, real-time information and face challenges in solving specialized problems.
Approach: They propose a multi-LLM approach that decomposes the aforementioned capabilities into a planner, caller, and summarizer.
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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.
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AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments (2026.acl-long)

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Challenge: Existing benchmarks evaluate agents in simplified, idealized settings, relying on pre-packaged tool interfaces, overlooking critical steps, and assume inputs are clean and fully specified.
Approach: They propose a framework that evaluates language agents in simplified, idealized settings . they show that even SOTA systems like Gemini and GPT-5 struggle on AgentGym2 .
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ACEBench: A Comprehensive Evaluation of LLM Tool Usage (2025.findings-emnlp)

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Challenge: Existing benchmarks for evaluating LLMs’ tool usage face several limitations: limited evaluation scenarios, lacking assessments in real multi-turn dialogue contexts; narrow evaluation dimensions, with insufficient detailed assessments of how LLM use tools; and reliance on LLM or real API executions for evaluation, which introduces significant overhead.
Approach: ACEBench is a benchmark for evaluating tool usage in Large Language Models . it categorizes data into three primary types based on evaluation methodology: Normal, Special, and Agent.
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LLMArena: Assessing Capabilities of Large Language Models in Dynamic Multi-Agent Environments (2024.acl-long)

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Challenge: Existing benchmarks for evaluating large language models use static datasets, leading to data leakage or overlooking the complexities of multi-agent interactions.
Approach: They propose a framework that evaluates the diverse capabilities of LLM agents in multi-agent dynamic environments.
Outcome: The proposed framework assesses the diverse capabilities of LLM agents in multi-agent dynamic environments.

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