SMART: Evaluating LLMs’ Mathematical Reasoning via a Human Cognitive Process-Inspired Benchmark (2026.acl-long)
Copied to clipboard
| Challenge: | Existing evaluation methods focus on the final answer or on the intermediate reasoning steps, overlooking its inherently multi-stage and multi-dimensional nature. |
| Approach: | They propose a benchmark that decomposes mathematical problem-solving into four cognitive dimensions and introduces dimension-specific tasks to measure their cognitive processes. |
| Outcome: | The proposed model decomposes mathematical problem-solving into four cognitive dimensions and introduces dimension-specific tasks to measure their cognitive processes. |
Similar Papers
LLMs for Mathematical Modeling: Towards Bridging the Gap between Natural and Mathematical Languages (2025.findings-naacl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have demonstrated strong performance across various natural language processing tasks, but their proficiency in mathematical reasoning remains a key challenge. |
| Approach: | They propose a process-oriented framework to evaluate LLMs' ability to construct mathematical models, using solvers to compare outputs with ground truth. |
| Outcome: | The proposed framework evaluates LLMs' ability to construct mathematical models, using solvers to compare outputs with ground truth. |
From Remembering to Metacognition: Do Existing Benchmarks Accurately Evaluate LLMs? (2025.findings-emnlp)
Copied to clipboard
Geng Zhang, Yizhou Ying, Sihang Jiang, Jiaqing Liang, Guanglei Yue, Yifei Fu, Hailin Hu, Yanghua Xiao
| Challenge: | Existing benchmark datasets focus on low-level cognitive tasks while providing limited coverage of higher-level reasoning skills. |
| Approach: | They analyze the cognitive depth of popular LLM benchmarks using Bloom’s Taxonomy to evaluate both the cognitive and knowledge dimensions. |
| Outcome: | The results show that incorporating higher-level cognitive instructions into the current instruction fine-tuning process improves model performance. |
PyraMathBench: Evaluating and Improving Mathematical Capability in Large Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Numerical reasoning is ubiquitous in scientific research and financial analysis, but few benchmarks evaluate them by integrating numerical processing and mathematical reasoning. |
| Approach: | They propose a numerically-integrated hierarchical benchmark with 27,215 questions derived from 7,404 math word problems that spans 4 key cognitive aspects, 14 subcategories, and 2 modalities. |
| Outcome: | The proposed model improves Qwen-2.5 score with SOLVE and IRPO training. |
Exposing the Achilles’ Heel: Evaluating LLMs Ability to Handle Mistakes in Mathematical Reasoning (2025.acl-long)
Copied to clipboard
| Challenge: | Existing evaluations focus on final accuracy, neglecting the critical aspect of reasoning capabilities. |
| Approach: | They propose to evaluate LLMs’ abilities to detect and correct reasoning mistakes by using rule-based methods and smaller language models. |
| Outcome: | The proposed model outperforms existing models such as GPT-4o and GPT4 in both accuracy and accuracy, but lacks data contamination and memorization concerns. |
LLMs Are Not Intelligent Thinkers: Introducing Mathematical Topic Tree Benchmark for Comprehensive Evaluation of LLMs (2025.naacl-long)
Copied to clipboard
| Challenge: | Large language models (LLMs) have impressive capabilities in mathematical reasoning, but their effectiveness is limited to specific mathematical topics. |
| Approach: | They propose to use the MaTT benchmark to assess large language models' accuracy in multiple-choice scenarios. |
| Outcome: | The proposed model achieved 54% accuracy in a multiple-choice scenario, while the Chain-of-Thought prompting did not improve. |
Do LLMs Overthink Basic Math Reasoning? Benchmarking the Accuracy-Efficiency Tradeoff in Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) achieve impressive performance on complex benchmarks yet sometimes fail on basic math reasoning. |
| Approach: | They propose a benchmark to evaluate the efficiency of reasoning in large language models . they formalize the accuracy-verbosity tradeoff and introduce the overthinking score . |
| Outcome: | The proposed model performs well on complex benchmarks but fails on basic math reasoning . the proposed model generates 18 more tokens while achieving lower accuracy . |
Large Language Models for Mathematical Reasoning: Progresses and Challenges (2024.eacl-srw)
Copied to clipboard
| Challenge: | a survey examines the landscape of mathematical problem-solving techniques . large language models have proven to be potent assets in unraveling nuances of mathematical reasoning . |
| Approach: | They examine the evolution of Large Language Models (LLMs) for solving mathematical problems . they examine the spectrum of LLM-oriented techniques proposed for solving math problems - and their challenges . |
| Outcome: | The survey examines the spectrum of proposed LLM-oriented techniques in solving math problems. |
FineReason: Evaluating and Improving LLMs’ Deliberate Reasoning through Reflective Puzzle Solving (2025.acl-long)
Copied to clipboard
Guizhen Chen, Weiwen Xu, Hao Zhang, Hou Pong Chan, Chaoqun Liu, Lidong Bing, Deli Zhao, Anh Tuan Luu, Yu Rong
| Challenge: | Recent advances in large language models (LLMs) highlight an important shift from the “System 1” way of quick reactions to the “system 2” style of reflection-and-correction problem solving. |
| Approach: | They propose a logic-puzzle benchmark for systematic evaluation of large language models' reasoning capabilities that decomposes each puzzle into atomic steps. |
| Outcome: | The proposed model improves on state checking and state transition tasks and demonstrates gains in reasoning by up to 5.1%. |
Can Large Language Models Win the International Mathematical Games? (2025.emnlp-main)
Copied to clipboard
Alessio Cocchieri, Luca Ragazzi, Giuseppe Tagliavini, Lorenzo Tordi, Antonella Carbonaro, Gianluca Moro
| Challenge: | Recent advances in large language models (LLMs) have demonstrated strong mathematical reasoning abilities, even in visual contexts. |
| Approach: | They propose a benchmark of 2,183 high-quality mathematical problems in an open-ended format that enables a structured evaluation of LLMs’ mathematical and logical reasoning abilities. |
| Outcome: | The new benchmark spans seven age groups and a skill-based taxonomy and enables a structured evaluation of LLMs’ mathematical and logical reasoning abilities. |
Are Your LLMs Capable of Stable Reasoning? (2025.findings-acl)
Copied to clipboard
Junnan Liu, Hongwei Liu, Linchen Xiao, Ziyi Wang, Kuikun Liu, Songyang Gao, Wenwei Zhang, Songyang Zhang, Kai Chen
| Challenge: | Existing evaluation protocols and metrics do not capture the full spectrum of LLM capabilities, especially in complex reasoning tasks. |
| Approach: | They propose a new evaluation metric that continuously assesses model performance across multiple sampling attempts, quantifying both the model’s potential capabilities and operational consistency. |
| Outcome: | The proposed evaluation metric measures model performance across multiple sampling attempts and provides comprehensive insights into their potential capabilities and operational consistency. |