Li Yin

GraphEval2000: Benchmarking and Improving Large Language Models on Graph Datasets 📘 What is this paper about? 🤖 Introduces GraphEval2000, a comprehensive dataset for evaluating LLMs on graph-structured data. 📊 Provides 40 graph data structure problems with 2000 test cases for robust assessment. 🧠 Proposes Structured Symbolic Decomposition (SSD) to enhance LLM performance on graph reasoning tasks. 🚀 Why is this a breakthrough? ⏱ Fills the gap in evaluating LLMs' graph reasoning capabilities. 📈 Shows that private LLMs outperform open-source models, but the gap is closing. 🌍 Enhances usability and performance of LLMs in handling complex graph data. 🔬 Key Findings 🔧 LLMs understand directed graphs better than undirected ones. 🧩 SSD improves the performance of GPT-3.5, GPT-4, and GPT-4o on complex graph tasks. 🛠 Evaluation framework provides comprehensive analysis across multiple graph categories. 🔍 Implications for the Future 🌐 Advances in LLMs' capabilities to handle graph-structured data will benefit numerous applications. 🚗 Potential improvements in fields like network analysis, bioinformatics, and social network analysis. 📈 Better understanding and performance on graph data can lead to more robust AI systems. 💡 Takeaways 🎯 GraphEval2000 is a critical tool for benchmarking LLMs on graph datasets. 🔄 SSD method significantly enhances LLM performance in graph reasoning. 🌟 Private LLMs are currently leading, but the performance gap with open-source models is narrowing.

GraphEval2000: Benchmarking and Improving Large Language Models on Graph Datasets 📘 What is this paper about? 🤖 Introduces GraphEval2000, a comprehensive dataset for evaluating LLMs on graph-structured data. 📊 Provides 40 graph data structure problems with 2000 test cases for robust assessment. 🧠 Proposes Structured Symbolic Decomposition (SSD) to enhance LLM performance on graph reasoning tasks. 🚀 Why is this a breakthrough? ⏱ Fills the gap in evaluating LLMs' graph reasoning capabilities. 📈 Shows that private LLMs outperform open-source models, but the gap is closing. 🌍 Enhances usability and performance of LLMs in handling complex graph data. 🔬 Key Findings 🔧 LLMs understand directed graphs better than undirected ones. 🧩 SSD improves the performance of GPT-3.5, GPT-4, and GPT-4o on complex graph tasks. 🛠 Evaluation framework provides comprehensive analysis across multiple graph categories. 🔍 Implications for the Future 🌐 Advances in LLMs' capabilities to handle graph-structured data will benefit numerous applications. 🚗 Potential improvements in fields like network analysis, bioinformatics, and social network analysis. 📈 Better understanding and performance on graph data can lead to more robust AI systems. 💡 Takeaways 🎯 GraphEval2000 is a critical tool for benchmarking LLMs on graph datasets. 🔄 SSD method significantly enhances LLM performance in graph reasoning. 🌟 Private LLMs are currently leading, but the performance gap with open-source models is narrowing.

GraphEval2000: Benchmarking and Improving Large Language Models on Graph Datasets 📘 What is this paper about? 🤖 Introduces GraphEval2000, a comprehensive dataset for evaluating LLMs on graph-structured data. 📊 Provides 40 graph data structure problems with 2000 test cases for robust assessment. 🧠 Proposes Structured Symbolic Decomposition (SSD) to enhance LLM performance on graph reasoning tasks. 🚀 Why is this a breakthrough? ⏱ Fills the gap in evaluating LLMs' graph reasoning capabilities. 📈 Shows that private LLMs outperform open-source models, but the gap is closing. 🌍 Enhances usability and performance of LLMs in handling complex graph data. 🔬 Key Findings 🔧 LLMs understand directed graphs better than undirected ones. 🧩 SSD improves the performance of GPT-3.5, GPT-4, and GPT-4o on complex graph tasks. 🛠 Evaluation framework provides comprehensive analysis across multiple graph categories. 🔍 Implications for the Future 🌐 Advances in LLMs' capabilities to handle graph-structured data will benefit numerous applications. 🚗 Potential improvements in fields like network analysis, bioinformatics, and social network analysis. 📈 Better understanding and performance on graph data can lead to more robust AI systems. 💡 Takeaways 🎯 GraphEval2000 is a critical tool for benchmarking LLMs on graph datasets. 🔄 SSD method significantly enhances LLM performance in graph reasoning. 🌟 Private LLMs are currently leading, but the performance gap with open-source models is narrowing.

GraphEval2000: Benchmarking and Improving Large Language Models on Graph Datasets 📘 What is this paper about? 🤖 Introduces GraphEval2000, a comprehensive dataset for evaluating LLMs on graph-structured data. 📊 Provides 40 graph data structure problems with 2000 test cases for robust assessment. 🧠 Proposes Structured Symbolic Decomposition (SSD) to enhance LLM performance on graph reasoning tasks. 🚀 Why is this a breakthrough? ⏱ Fills the gap in evaluating LLMs' graph reasoning capabilities. 📈 Shows that private LLMs outperform open-source models, but the gap is closing. 🌍 Enhances usability and performance of LLMs in handling complex graph data. 🔬 Key Findings 🔧 LLMs understand directed graphs better than undirected ones. 🧩 SSD improves the performance of GPT-3.5, GPT-4, and GPT-4o on complex graph tasks. 🛠 Evaluation framework provides comprehensive analysis across multiple graph categories. 🔍 Implications for the Future 🌐 Advances in LLMs' capabilities to handle graph-structured data will benefit numerous applications. 🚗 Potential improvements in fields like network analysis, bioinformatics, and social network analysis. 📈 Better understanding and performance on graph data can lead to more robust AI systems. 💡 Takeaways 🎯 GraphEval2000 is a critical tool for benchmarking LLMs on graph datasets. 🔄 SSD method significantly enhances LLM performance in graph reasoning. 🌟 Private LLMs are currently leading, but the performance gap with open-source

GraphEval2000: Benchmarking and Improving Large Language Models on Graph Datasets 📘 What is this paper about? 🤖 Introduces GraphEval2000, a comprehensive dataset for evaluating LLMs on graph-structured data. 📊 Provides 40 graph data structure problems with 2000 test cases for robust assessment. 🧠 Proposes Structured Symbolic Decomposition (SSD) to enhance LLM performance on graph reasoning tasks. 🚀 Why is this a breakthrough? ⏱ Fills the gap in evaluating LLMs' graph reasoning capabilities. 📈 Shows that private LLMs outperform open-source models, but the gap is closing. 🌍 Enhances usability and performance of LLMs in handling complex graph data. 🔬 Key Findings 🔧 LLMs understand directed graphs better than undirected ones. 🧩 SSD improves the performance of GPT-3.5, GPT-4, and GPT-4o on complex graph tasks. 🛠 Evaluation framework provides comprehensive analysis across multiple graph categories. 🔍 Implications for the Future 🌐 Advances in LLMs' capabilities to handle graph-structured data will benefit numerous applications. 🚗 Potential improvements in fields like network analysis, bioinformatics, and social network analysis. 📈 Better understanding and performance on graph data can lead to more robust AI systems. 💡 Takeaways 🎯 GraphEval2000 is a critical tool for benchmarking LLMs on graph datasets. 🔄 SSD method significantly enhances LLM performance in graph reasoning. 🌟 Private LLMs are currently leading, but the performance gap with open-source models is narrowing.

GraphEval2000: Benchmarking and Improving Large Language Models on Graph Datasets 📘 What is this paper about? 🤖 Introduces GraphEval2000, a comprehensive dataset for evaluating LLMs on graph-structured data. 📊 Provides 40 graph data structure problems with 2000 test cases for robust assessment. 🧠 Proposes Structured Symbolic Decomposition (SSD) to enhance LLM performance on graph reasoning tasks. 🚀 Why is this a breakthrough? ⏱ Fills the gap in evaluating LLMs' graph reasoning capabilities. 📈 Shows that private LLMs outperform open-source models, but the gap is closing. 🌍 Enhances usability and performance of LLMs in handling complex graph data. 🔬 Key Findings 🔧 LLMs understand directed graphs better than undirected ones. 🧩 SSD improves the performance of GPT-3.5, GPT-4, and GPT-4o on complex graph tasks. 🛠 Evaluation framework provides comprehensive analysis across multiple graph categories. 🔍 Implications for the Future 🌐 Advances in LLMs' capabilities to handle graph-structured data will benefit numerous applications. 🚗 Potential improvements in fields like network analysis, bioinformatics, and social network analysis. 📈 Better understanding and performance on graph data can lead to more robust AI systems. 💡 Takeaways 🎯 GraphEval2000 is a critical tool for benchmarking LLMs on graph datasets. 🔄 SSD method significantly enhances LLM performance in graph reasoning. 🌟 Private LLMs are currently leading, but the performance gap with open-sourc

GraphEval2000: Benchmarking and Improving Large Language Models on Graph Datasets 📘 What is this paper about? 🤖 Introduces GraphEval2000, a comprehensive dataset for evaluating LLMs on graph-structured data. 📊 Provides 40 graph data structure problems with 2000 test cases for robust assessment. 🧠 Proposes Structured Symbolic Decomposition (SSD) to enhance LLM performance on graph reasoning tasks. 🚀 Why is this a breakthrough? ⏱ Fills the gap in evaluating LLMs' graph reasoning capabilities. 📈 Shows that private LLMs outperform open-source models, but the gap is closing. 🌍 Enhances usability and performance of LLMs in handling complex graph data. 🔬 Key Findings 🔧 LLMs understand directed graphs better than undirected ones. 🧩 SSD improves the performance of GPT-3.5, GPT-4, and GPT-4o on complex graph tasks. 🛠 Evaluation framework provides comprehensive analysis across multiple graph categories. 🔍 Implications for the Future 🌐 Advances in LLMs' capabilities to handle graph-structured data will benefit numerous applications. 🚗 Potential improvements in fields like network analysis, bioinformatics, and social network analysis. 📈 Better understanding and performance on graph data can lead to more robust AI systems. 💡 Takeaways 🎯 GraphEval2000 is a critical tool for benchmarking LLMs on graph datasets. 🔄 SSD method significantly enhances LLM performance in graph reasoning. 🌟 Private LLMs are currently leading, but the performance gap with open-source models is narrowing

GraphEval2000: Benchmarking and Improving Large Language Models on Graph Datasets 📘 What is this paper about? 🤖 Introduces GraphEval2000, a comprehensive dataset for evaluating LLMs on graph-structured data. 📊 Provides 40 graph data structure problems with 2000 test cases for robust assessment. 🧠 Proposes Structured Symbolic Decomposition (SSD) to enhance LLM performance on graph reasoning tasks. 🚀 Why is this a breakthrough? ⏱ Fills the gap in evaluating LLMs' graph reasoning capabilities. 📈 Shows that private LLMs outperform open-source models, but the gap is closing. 🌍 Enhances usability and performance of LLMs in handling complex graph data. 🔬 Key Findings 🔧 LLMs understand directed graphs better than undirected ones. 🧩 SSD improves the performance of GPT-3.5, GPT-4, and GPT-4o on complex graph tasks. 🛠 Evaluation framework provides comprehensive analysis across multiple graph categories. 🔍 Implications for the Future 🌐 Advances in LLMs' capabilities to handle graph-structured data will benefit numerous applications. 🚗 Potential improvements in fields like network analysis, bioinformatics, and social network analysis. 📈 Better understanding and performance on graph data can lead to more robust AI systems. 💡 Takeaways 🎯 GraphEval2000 is a critical tool for benchmarking LLMs on graph datasets. 🔄 SSD method significantly enhances LLM performance in graph reasoning. 🌟 Private LLMs are currently leading, but the performance gap with open-source models is narrowing.

𝐌𝐄𝐌𝐄𝐍𝐓𝐎: 𝐑𝐞𝐢𝐧𝐟𝐨𝐫𝐜𝐞𝐦𝐞𝐧𝐭 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐌𝐞𝐦𝐨𝐫𝐲 𝐟𝐨𝐫 𝐂𝐨𝐦𝐛𝐢𝐧𝐚𝐭𝐨𝐫𝐢𝐚𝐥 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 📘 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐭𝐡𝐢𝐬 𝐩𝐚𝐩𝐞𝐫 𝐚𝐛𝐨𝐮𝐭? 🤖 Introduces MEMENTO, a reinforcement learning (RL) approach leveraging memory for better neural solver adaptation. 📊 Focuses on improving adaptation during inference by dynamically updating action distribution based on previous outcomes. 🧠 Validates effectiveness on benchmark problems like Traveling Salesman and Capacitated Vehicle Routing. 🚀 𝐖𝐡𝐲 𝐢𝐬 𝐭𝐡𝐢𝐬 𝐚 𝐛𝐫𝐞𝐚𝐤𝐭𝐡𝐫𝐨𝐮𝐠𝐡? ⏱ Enhances RL-based heuristics for real-world applications by fully utilizing computational budgets. 📈 Outperforms existing methods by dynamically adapting to specific instances. 🌍 Provides a flexible framework that improves both in-distribution and out-of-distribution performance. 🔬 𝐊𝐞𝐲 𝐅𝐢𝐧𝐝𝐢𝐧𝐠𝐬 🔧 MEMENTO successfully integrates with standard methods to boost performance under a given budget. 🧩 Demonstrates significant improvements across 12 evaluated tasks. 🛠 Shows better scalability and adaptability in industrial solvers compared to handcrafted heuristics. 🔍 𝐈𝐦𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 🌐 Potential for widespread adoption in various industries requiring combinatorial optimization. 🚗 Can optimize complex logistics problems like routing and scheduling. 📈 Enhances the efficiency and quality of industrial solvers, leading to better resource utilization.

𝐌𝐄𝐌𝐄𝐍𝐓𝐎: 𝐑𝐞𝐢𝐧𝐟𝐨𝐫𝐜𝐞𝐦𝐞𝐧𝐭 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐌𝐞𝐦𝐨𝐫𝐲 𝐟𝐨𝐫 𝐂𝐨𝐦𝐛𝐢𝐧𝐚𝐭𝐨𝐫𝐢𝐚𝐥 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 📘 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐭𝐡𝐢𝐬 𝐩𝐚𝐩𝐞𝐫 𝐚𝐛𝐨𝐮𝐭? 🤖 Introduces MEMENTO, a reinforcement learning (RL) approach leveraging memory for better neural solver adaptation. 📊 Focuses on improving adaptation during inference by dynamically updating action distribution based on previous outcomes. 🧠 Validates effectiveness on benchmark problems like Traveling Salesman and Capacitated Vehicle Routing. 🚀 𝐖𝐡𝐲 𝐢𝐬 𝐭𝐡𝐢𝐬 𝐚 𝐛𝐫𝐞𝐚𝐤𝐭𝐡𝐫𝐨𝐮𝐠𝐡? ⏱ Enhances RL-based heuristics for real-world applications by fully utilizing computational budgets. 📈 Outperforms existing methods by dynamically adapting to specific instances. 🌍 Provides a flexible framework that improves both in-distribution and out-of-distribution performance. 🔬 𝐊𝐞𝐲 𝐅𝐢𝐧𝐝𝐢𝐧𝐠𝐬 🔧 MEMENTO successfully integrates with standard methods to boost performance under a given budget. 🧩 Demonstrates significant improvements across 12 evaluated tasks. 🛠 Shows better scalability and adaptability in industrial solvers compared to handcrafted heuristics. 🔍 𝐈𝐦𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 🌐 Potential for widespread adoption in various industries requiring combinatorial optimization. 🚗 Can optimize complex logistics problems like routing and scheduling. 📈 Enhances the efficiency and quality of industrial solvers, leading to better resource utilization.

𝐌𝐄𝐌𝐄𝐍𝐓𝐎: 𝐑𝐞𝐢𝐧𝐟𝐨𝐫𝐜𝐞𝐦𝐞𝐧𝐭 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐌𝐞𝐦𝐨𝐫𝐲 𝐟𝐨𝐫 𝐂𝐨𝐦𝐛𝐢𝐧𝐚𝐭𝐨𝐫𝐢𝐚𝐥 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 📘 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐭𝐡𝐢𝐬 𝐩𝐚𝐩𝐞𝐫 𝐚𝐛𝐨𝐮𝐭? 🤖 Introduces MEMENTO, a reinforcement learning (RL) approach leveraging memory for better neural solver adaptation. 📊 Focuses on improving adaptation during inference by dynamically updating action distribution based on previous outcomes. 🧠 Validates effectiveness on benchmark problems like Traveling Salesman and Capacitated Vehicle Routing. 🚀 𝐖𝐡𝐲 𝐢𝐬 𝐭𝐡𝐢𝐬 𝐚 𝐛𝐫𝐞𝐚𝐤𝐭𝐡𝐫𝐨𝐮𝐠𝐡? ⏱ Enhances RL-based heuristics for real-world applications by fully utilizing computational budgets. 📈 Outperforms existing methods by dynamically adapting to specific instances. 🌍 Provides a flexible framework that improves both in-distribution and out-of-distribution performance. 🔬 𝐊𝐞𝐲 𝐅𝐢𝐧𝐝𝐢𝐧𝐠𝐬 🔧 MEMENTO successfully integrates with standard methods to boost performance under a given budget. 🧩 Demonstrates significant improvements across 12 evaluated tasks. 🛠 Shows better scalability and adaptability in industrial solvers compared to handcrafted heuristics. 🔍 𝐈𝐦𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 🌐 Potential for widespread adoption in various industries requiring combinatorial optimization. 🚗 Can optimize complex logistics problems like routing and scheduling. 📈 Enhances the efficiency and quality of industrial solvers, leading to better resource utilization. 💡 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬 🎯 MEMENTO leverages memory to improve RL adaptation for combinatorial optimization. 🔄 Outperforms existing methods by dynamically updating action distributions during inference. 🌟 Promises significant advancements in industrial applications requiring efficient 

𝐌𝐄𝐌𝐄𝐍𝐓𝐎: 𝐑𝐞𝐢𝐧𝐟𝐨𝐫𝐜𝐞𝐦𝐞𝐧𝐭 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐌𝐞𝐦𝐨𝐫𝐲 𝐟𝐨𝐫 𝐂𝐨𝐦𝐛𝐢𝐧𝐚𝐭𝐨𝐫𝐢𝐚𝐥 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 📘 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐭𝐡𝐢𝐬 𝐩𝐚𝐩𝐞𝐫 𝐚𝐛𝐨𝐮𝐭? 🤖 Introduces MEMENTO, a reinforcement learning (RL) approach leveraging memory for better neural solver adaptation. 📊 Focuses on improving adaptation during inference by dynamically updating action distribution based on previous outcomes. 🧠 Validates effectiveness on benchmark problems like Traveling Salesman and Capacitated Vehicle Routing. 🚀 𝐖𝐡𝐲 𝐢𝐬 𝐭𝐡𝐢𝐬 𝐚 𝐛𝐫𝐞𝐚𝐤𝐭𝐡𝐫𝐨𝐮𝐠𝐡? ⏱ Enhances RL-based heuristics for real-world applications by fully utilizing computational budgets. 📈 Outperforms existing methods by dynamically adapting to specific instances. 🌍 Provides a flexible framework that improves both in-distribution and out-of-distribution performance. 🔬 𝐊𝐞𝐲 𝐅𝐢𝐧𝐝𝐢𝐧𝐠𝐬 🔧 MEMENTO successfully integrates with standard methods to boost performance under a given budget. 🧩 Demonstrates significant improvements across 12 evaluated tasks. 🛠 Shows better scalability and adaptability in industrial solvers compared to handcrafted heuristics. 🔍 𝐈𝐦𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 🌐 Potential for widespread adoption in various industries requiring combinatorial optimization. 🚗 Can optimize complex logistics problems like routing and scheduling. 📈 Enhances the efficiency and quality of industrial solvers, leading to better resource utilization. 💡 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬 🎯 MEMENTO leverages memory to improve RL adaptation for combinatorial optimization. 🔄 Outperforms existing methods by dynamically updating action distributions during inference. 🌟 Promises significant advancements in industrial applications requiring efficient 

𝐌𝐄𝐌𝐄𝐍𝐓𝐎: 𝐑𝐞𝐢𝐧𝐟𝐨𝐫𝐜𝐞𝐦𝐞𝐧𝐭 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐰𝐢𝐭𝐡 𝐌𝐞𝐦𝐨𝐫𝐲 𝐟𝐨𝐫 𝐂𝐨𝐦𝐛𝐢𝐧𝐚𝐭𝐨𝐫𝐢𝐚𝐥 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 📘 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐭𝐡𝐢𝐬 𝐩𝐚𝐩𝐞𝐫 𝐚𝐛𝐨𝐮𝐭? 🤖 Introduces MEMENTO, a reinforcement learning (RL) approach leveraging memory for better neural solver adaptation. 📊 Focuses on improving adaptation during inference by dynamically updating action distribution based on previous outcomes. 🧠 Validates effectiveness on benchmark problems like Traveling Salesman and Capacitated Vehicle Routing. 🚀 𝐖𝐡𝐲 𝐢𝐬 𝐭𝐡𝐢𝐬 𝐚 𝐛𝐫𝐞𝐚𝐤𝐭𝐡𝐫𝐨𝐮𝐠𝐡? ⏱ Enhances RL-based heuristics for real-world applications by fully utilizing computational budgets. 📈 Outperforms existing methods by dynamically adapting to specific instances. 🌍 Provides a flexible framework that improves both in-distribution and out-of-distribution performance. 🔬 𝐊𝐞𝐲 𝐅𝐢𝐧𝐝𝐢𝐧𝐠𝐬 🔧 MEMENTO successfully integrates with standard methods to boost performance under a given budget. 🧩 Demonstrates significant improvements across 12 evaluated tasks. 🛠 Shows better scalability and adaptability in industrial solvers compared to handcrafted heuristics. 🔍 𝐈𝐦𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 🌐 Potential for widespread adoption in various industries requiring combinatorial optimization. 🚗 Can optimize complex logistics problems like routing and scheduling. 📈 Enhances the efficiency and quality of industrial solvers, leading to better resource utilization