Papers with PMC
VCSearch: Bridging the Gap Between Well-Defined and Ill-Defined Problems in Mathematical Reasoning (2025.emnlp-main)
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| Challenge: | Existing studies have improved the performance of Large language models on well-defined mathematical benchmarks, but they often overlook ill-defined problems. |
| Approach: | They develop a large-scale benchmark that contains over 5,000 ill-defined mathematical problems. |
| Outcome: | The proposed framework improves the accuracy of identifying unsolvable problems by at least 12% across different LLMs, thus achieving stronger robust mathematical reasoning ability. |
Planning with Multi-Constraints via Collaborative Language Agents (2025.coling-main)
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| Challenge: | Recent advances in neural language models have sparked a new surge of intelligent agent research. |
| Approach: | They propose a method for collaborative LLM-based multi-agent systems that simplifies complex task planning with constraints by decomposing it into a hierarchy of subordinate tasks. |
| Outcome: | The proposed method achieves an average success rate of 42.68% on two constraint-intensive benchmarks, TravelPlanner and API-Bank. |