# STEMVerse: A Dual-Axis Diagnostic Framework for STEM Reasoning in Large Language Models arXiv: https://arxiv.org/abs/2602.02497 · HTML: https://mlsanity.com/paper/2602.02497/ ## Key facts - Cited by 1 papers in the map (data through 14 Oct 2026). - Placed in Mathematical reasoning, one of 17 directions in LLM reasoning. - Builds on 30 of its 49 references that are in the map. - First posted on arXiv 14 Jan 2026. ## Placed in - LLM reasoning / [Mathematical reasoning](https://mlsanity.com/lr/mathematical-reasoning/) ## Builds on (most cited first, 30 of 30) - [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021) - [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025) - [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025) - [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021) - [Qwen2.5 Technical Report](https://mlsanity.com/paper/2412.15115/) (2024) - [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024) - [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024) - [Gemini: A Family of Highly Capable Multimodal Models](https://mlsanity.com/paper/2312.11805/) (2023) - [From System 1 to System 2: A Survey of Reasoning Large Language Models](https://mlsanity.com/paper/2502.17419/) (2025) - [rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking](https://mlsanity.com/paper/2501.04519/) (2025) - [Large Language Models for Mathematical Reasoning: Progresses and Challenges](https://mlsanity.com/paper/2402.00157/) (2024) - [Logic-RL: Unleashing LLM Reasoning with Rule-Based Reinforcement Learning](https://mlsanity.com/paper/2502.14768/) (2025) - [SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models](https://mlsanity.com/paper/2307.10635/) (2023) - [SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines](https://mlsanity.com/paper/2502.14739/) (2025) - [SciAgent: Tool-augmented Language Models for Scientific Reasoning](https://mlsanity.com/paper/2402.11451/) (2024) - [MegaScience: Pushing the Frontiers of Post-Training Datasets for Science Reasoning](https://mlsanity.com/paper/2507.16812/) (2025) - [Large Language Models for Planning: A Comprehensive and Systematic Survey](https://mlsanity.com/paper/2505.19683/) (2025) - [Aviary: training language agents on challenging scientific tasks](https://mlsanity.com/paper/2412.21154/) (2024) - [Improving Physics Reasoning in Large Language Models Using Mixture of Refinement Agents](https://mlsanity.com/paper/2412.00821/) (2024) - [VerifyBench: A Systematic Benchmark for Evaluating Reasoning Verifiers Across Domains](https://mlsanity.com/paper/2507.09884/) (2025) - [Chemical reasoning in LLMs unlocks strategy-aware synthesis planning and reaction mechanism elucidation](https://mlsanity.com/paper/2503.08537/) (2025) - [Review of Case-Based Reasoning for LLM Agents: Theoretical Foundations, Architectural Components, and Cognitive Integration](https://mlsanity.com/paper/2504.06943/) (2025) - [Do Cognitively Interpretable Reasoning Traces Improve LLM Performance?](https://mlsanity.com/paper/2508.16695/) (2025) - [CausalStep: A Benchmark for Explicit Stepwise Causal Reasoning in Videos](https://mlsanity.com/paper/2507.16878/) (2025) - [11Plus-Bench: Demystifying Multimodal LLM Spatial Reasoning with Cognitive-Inspired Analysis](https://mlsanity.com/paper/2508.20068/) (2025) - [Look Less, Reason More: Rollout-Guided Adaptive Pixel-Space Reasoning](https://mlsanity.com/paper/2510.01681/) (2025) - [SciAgent: A Unified Multi-Agent System for Generalistic Scientific Reasoning](https://mlsanity.com/paper/2511.08151/) (2025) - [ATLAS: A High-Difficulty, Multidisciplinary Benchmark for Frontier Scientific Reasoning](https://mlsanity.com/paper/2511.14366/) (2025) - [Select Less, Reason More: Prioritizing Evidence Purity for Video Reasoning](https://mlsanity.com/paper/2510.15440/) (2025) - [Cognitive Mirrors: Exploring the Diverse Functional Roles of Attention Heads in LLM Reasoning](https://mlsanity.com/paper/2512.10978/) (2025) ## Built on by (most cited first, 1 of 1) - [Large Language Models and their Awareness of Mechanics and Spatial Geometry](https://mlsanity.com/paper/2608.14615/) (2026) ## Abstract (arXiv) As Large Language Models (LLMs) achieve significant breakthroughs in complex reasoning tasks, evaluating their proficiency in science, technology, engineering, and mathematics (STEM) has become a primary method for measuring machine intelligence. However, current evaluation paradigms often treat benchmarks as isolated "silos," offering only monolithic aggregate scores that neglect the intricacies of both academic specialization and cognitive depth. This result-oriented approach fails to distinguish whether model errors stem from insufficient domain knowledge or deficiencies in cognitive capacity, thereby limiting the diagnostic value. To address this, we propose STEMVerse, a diagnostic framework designed to systematically analyze the STEM reasoning capabilities of LLMs. This framework characterizes model performance across academic specialization and cognitive complexity to map the capability required for reasoning. We re-aggregate over 20,000 STEM problems from mainstream benchmarks into a unified "Discipline × Cognition" capability space, assigning dual-axis labels to every instance. Utilizing this unified diagnostic framework, we systematically evaluate representative LLM families across varying parameter scales and training paradigms. Our empirical results reveal structural failure patterns in STEM reasoning. By integrating multi-disciplinary coverage and fine-grained cognitive stratification into a unified framework, STEMVerse provides a clear and actionable perspective for understanding the scientific reasoning characteristics of LLMs.