Zhucong Li, Powei Chang, Jin Xiao, Zhijian Zhou, Qianyu He, Jiaqing Liang, Fenglei Cao, Xu Yinghui, Yuan Qi
| Challenge: | LLM-based agents are powerful tools for automating complex scientific workflows, especially in chemistry, but their single-task performance is limited by tool constraints. |
| Approach: | They propose a framework that optimizes the collective capabilities of specialized tools by dynamic coordination within individual tasks. |
| Outcome: | The proposed framework outperforms chemistry-specialized models, generalist LLMs, and agent systems with tool orchestration. |
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Botao Yu, Frazier N. Baker, Ziru Chen, Garrett Herb, Boyu Gou, Daniel Adu-Ampratwum, Xia Ning, Huan Sun
| Challenge: | Existing evaluations of large language models (LLMs) with tools are limited and qualitative . existing evaluations have been limited and only focus on 14 tasks focusing on compound synthesis. |
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MT-Mol: Multi Agent System with Tool-based Reasoning for Molecular Optimization (2025.findings-emnlp)
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| Challenge: | Structured reasoning and tool-grounded molecular optimization are still underexplored. |
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ChemActor: Enhancing Automated Extraction of Chemical Synthesis Actions with LLM-Generated Data (2025.acl-long)
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| Challenge: | Existing methods for extracting chemical procedures from literature are insufficient and low-quality due to the inherent ambiguity of chemical language and the high cost of human annotation. |
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Learning to Use Tools via Cooperative and Interactive Agents (2024.findings-emnlp)
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Zhengliang Shi, Shen Gao, Xiuyi Chen, Yue Feng, Lingyong Yan, Haibo Shi, Dawei Yin, Pengjie Ren, Suzan Verberne, Zhaochun Ren
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CAR: Empowering Agents with Dynamic Tool Synthesis and Global Trajectory Rectification (2026.findings-acl)
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| Challenge: | Existing LLM agents are brittle in open-ended environments due to two limitations: 1) a closed action space; 2) myopic error recovery. |
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ToolScope: Enhancing LLM Agent Tool Use through Tool Merging and Context-Aware Filtering (2026.acl-long)
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| Challenge: | Large language model (LLM) agents often face strict input context limits, preventing efficient consideration of large toolsets. |
| Approach: | They propose a tool that allows LLMs to merge tools with auto-correction and toolScopeRetriever to rank and select only the most relevant tools for each query. |
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Evaluating Multi-Hop Reasoning in Large Language Models: A Chemistry-Centric Benchmark (2026.findings-eacl)
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Mohammad Khodadad, Ali Shiraee Kasmaee, Mahdi Astaraki, Nicholas Sherck, Hamidreza Mahyar, Soheila Samiee
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| Challenge: | Existing task embedding methods rely on fine-tuned, task-specific language models, which hinders their adaptability to prompt-guided Large Language Models (LLMs). |
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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. |
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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. |
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