Challenge: Large language model (LLM) agents have demonstrated strong problem-solving competence across domains like research and coding.
Approach: They propose to use a tool repository to analyze the ability of large language model agents to solve complex problems.
Outcome: The proposed model outperforms open-source and closed-source models in task completion rate and efficiency.

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TPTU-v2: Boosting Task Planning and Tool Usage of Large Language Model-based Agents in Real-world Industry Systems (2024.emnlp-industry)

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Challenge: Large language models have demonstrated proficiency in addressing tasks that necessitate a combination of task planning and the usage of external tools.
Approach: They propose a framework to enhance the task planning and tool usage abilities of LLMs in industrial systems.
Outcome: The proposed framework enhances the task planning and tool usage abilities of LLM-based agents in industrial systems.
TelAgentBench: A Multi-faceted Benchmark for Evaluating LLM-based Agents in Telecommunications (2025.emnlp-industry)

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Challenge: Large Language Models (LLMs) are becoming powerful agentic systems . generic benchmarks fail to assess realistic, non-English performance .
Approach: They propose to evaluate five core agentic capabilities: Reasoning, Planning, Action (tool-use), Retrieval-Augmented Generation, and Instruction Following.
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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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CostBench: Evaluating Multi-Turn Cost-Optimal Planning and Adaptation in Dynamic Environments for LLM Tool-Use Agents (2026.acl-long)

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Challenge: Existing evaluations of Large Language Models (LLMs) focus on task completion, but neglect a crucial capability: the ability to devise and adjust cost-optimal plans in response to changing environments.
Approach: They propose a scalable, cost-centric benchmark to evaluate agents’ economic reasoning and replanning abilities.
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MMAU: A Holistic Benchmark of Agent Capabilities Across Diverse Domains (2025.findings-naacl)

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Challenge: Existing benchmarks focus on specific application scenarios, emphasizing task completion but failing to dissect the underlying skills that drive these outcomes.
Approach: They propose a Massive Multitask Agent Understanding benchmark that evaluates LLMs across five domains and offline tasks.
Outcome: The Massive Multitask Agent Understanding (MMAU) benchmark evaluates models across five domains including Tool-use, Directed Acyclic Graph (DAG) QA, Data Science and Machine Learning coding, Contest-level programming and Mathematics.
NL ⇒ Schedule: Evaluate Multitask Scheduling Capability of Large Language Models (2026.acl-long)

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Challenge: Existing methods for scheduling from natural language descriptions rely on experts with limited scheduling skills and domain knowledge.
Approach: They propose a model to generate a feasible schedule from natural language descriptions.
Outcome: The proposed framework achieves more robust performance than six state-of-the-art LLM+solver methods.
RLAE: Reinforcement Learning-Assisted Ensemble for LLMs (2025.emnlp-main)

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Challenge: Existing ensemble methods for ensembling large language models rely on fixed weighting strategies that fail to adapt to dynamic, context-dependent characteristics of LLMs.
Approach: They propose a framework that reformulates LLM ensemble through a Markov Decision Process.
Outcome: The proposed framework outperforms existing methods by 3.3% on a diverse set of tasks while achieving lower time latency.
MultiAgentBench : Evaluating the Collaboration and Competition of LLM agents (2025.acl-long)

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Challenge: Large Language Models (LLMs) have shown remarkable capabilities as autonomous agents, yet existing benchmarks focus on single-agent tasks or are confined to narrow domains, failing to capture the dynamics of multi-agent coordination and competition.
Approach: They propose a benchmark to evaluate LLM-based multi-agent systems across diverse, interactive scenarios.
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AgentPro: Enhancing LLM Agents with Automated Process Supervision (2025.emnlp-main)

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Challenge: Existing frameworks lack explicit supervision during the reasoning process, which may lead to error propagation across reasoning chains.
Approach: They propose a framework which automates process supervision for large language model agents by automatically generating step-level annotations and developing a process reward model based on these annotations.
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MPO: Boosting LLM Agents with Meta Plan Optimization (2025.findings-emnlp)

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Challenge: Existing methods for interactive planning tasks suffer from planning hallucinations and require retraining for each new agent.
Approach: They propose a framework that leverages explicit guidance through meta plans to assist agent planning and enables continuous optimization based on feedback from the agent’s task execution.
Outcome: The proposed framework outperforms existing baselines on two representative tasks and significantly improves task completion efficiency and generalization capabilities.

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