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Mathematical Problem-Solving

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Showing 51–75 of 106 papers

TitleStatusHype
Is PRM Necessary? Problem-Solving RL Implicitly Induces PRM Capability in LLMs—0
PT-MoE: An Efficient Finetuning Framework for Integrating Mixture-of-Experts into Prompt TuningCode0
Reasoning Models Can Be Effective Without Thinking—0
Holistic Capability Preservation: Towards Compact Yet Comprehensive Reasoning Models—0
LearNAT: Learning NL2SQL with AST-guided Task Decomposition for Large Language Models—0
On Vanishing Variance in Transformer Length Generalization—0
Exploring LLM Reasoning Through Controlled Prompt VariationsCode0
Brains vs. Bytes: Evaluating LLM Proficiency in Olympiad Mathematics—0
MathAgent: Leveraging a Mixture-of-Math-Agent Framework for Real-World Multimodal Mathematical Error Detection—0
A Survey on Mathematical Reasoning and Optimization with Large Language ModelsCode0
Does Chain-of-Thought Reasoning Help Mobile GUI Agent? An Empirical StudyCode0
MathFlow: Enhancing the Perceptual Flow of MLLMs for Visual Mathematical ProblemsCode0
Performance Comparison of Large Language Models on Advanced Calculus Problems—0
Self-Evolved Preference Optimization for Enhancing Mathematical Reasoning in Small Language Models—0
SECURA: Sigmoid-Enhanced CUR Decomposition with Uninterrupted Retention and Low-Rank Adaptation in Large Language Models—0
How Do Large Language Monkeys Get Their Power (Laws)?—0
Navigating Semantic Relations: Challenges for Language Models in Abstract Common-Sense Reasoning—0
MathFimer: Enhancing Mathematical Reasoning by Expanding Reasoning Steps through Fill-in-the-Middle Task—0
Teaching LLMs According to Their Aptitude: Adaptive Reasoning for Mathematical Problem Solving—0
STRIVE: Structured Reasoning for Self-Improvement in Claim Verification—0
Scaling Autonomous Agents via Automatic Reward Modeling And Planning—0
Advancing Reasoning in Large Language Models: Promising Methods and Approaches—0
Automating Mathematical Proof Generation Using Large Language Model Agents and Knowledge Graphs—0
Token-Hungry, Yet Precise: DeepSeek R1 Highlights the Need for Multi-Step Reasoning Over Speed in MATH—0
Token-by-Token Regeneration and Domain Biases: A Benchmark of LLMs on Advanced Mathematical Problem-Solving—0
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