Papers Mathematical Problem-Solving
“Mathematical Problem-Solving” 태그가 달린 논문 106편 · 필터 해제
EvoAgentX: An Automated Framework for Evolving Agentic Workflows
Multi-agent systems (MAS) have emerged as a powerful paradigm for orchestrating large language models (LLMs) and specialized tools to collaboratively address complex tasks. However, existing MAS frameworks often require …
Code GenerationMathMathematical Problem-SolvingmbppLocationReasoner: Evaluating LLMs on Real-World Site Selection Reasoning
Recent advances in large language models (LLMs), particularly those enhanced through reinforced post-training, have demonstrated impressive reasoning capabilities, as exemplified by models such as OpenAI o1 and DeepSeek-…
Code GenerationMathematical Problem-SolvingTeleMath: A Benchmark for Large Language Models in Telecom Mathematical Problem Solving
The increasing adoption of artificial intelligence in telecommunications has raised interest in the capability of Large Language Models (LLMs) to address domain-specific, mathematically intensive tasks. Although recent a…
Logical ReasoningMathematical Problem-SolvingMathematical ReasoningSwS: Self-aware Weakness-driven Problem Synthesis in Reinforcement Learning for LLM Reasoning
Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective for training large language models (LLMs) on complex reasoning tasks, such as mathematical problem solving. A prerequisite for the scalability of…
Knowledge DistillationMathMathematical Problem-SolvingSolving Inequality Proofs with Large Language Models
Inequality proving, crucial across diverse scientific and mathematical fields, tests advanced reasoning skills such as discovering tight bounds and strategic theorem application. This makes it a distinct, demanding front…
Mathematical Problem-SolvingRelation PredictionChain-of-Code Collapse: Reasoning Failures in LLMs via Adversarial Prompting in Code Generation
Large Language Models (LLMs) have achieved remarkable success in tasks requiring complex reasoning, such as code generation, mathematical problem solving, and algorithmic synthesis -- especially when aided by reasoning t…
Code GenerationMathematical Problem-SolvingMORSE-500: A Programmatically Controllable Video Benchmark to Stress-Test Multimodal Reasoning
Despite rapid advances in vision-language models (VLMs), current benchmarks for multimodal reasoning fall short in three key dimensions. First, they overwhelmingly rely on static images, failing to capture the temporal c…
Dataset GenerationMathematical Problem-SolvingMultimodal ReasoningPoLAR: Polar-Decomposed Low-Rank Adapter Representation
We show that low-rank adaptation of large-scale models suffers from a low stable rank that is well below the linear algebraic rank of the subspace, degrading fine-tuning performance. To mitigate the underutilization of t…
Mathematical Problem-SolvingRiemannian optimizationEvaluation of LLMs for mathematical problem solving
Large Language Models (LLMs) have shown impressive performance on a range of educational tasks, but are still understudied for their potential to solve mathematical problems. In this study, we compare three prominent LLM…
GSM8KMathematical Problem-SolvingMathematical ReasoningDecomposing Elements of Problem Solving: What "Math" Does RL Teach?
Mathematical reasoning tasks have become prominent benchmarks for assessing the reasoning capabilities of LLMs, especially with reinforcement learning (RL) methods such as GRPO showing significant performance gains. Howe…
MathMathematical Problem-SolvingMathematical ReasoningReinforcement Learning (RL)Surrogate Signals from Format and Length: Reinforcement Learning for Solving Mathematical Problems without Ground Truth Answers
Large Language Models have achieved remarkable success in natural language processing tasks, with Reinforcement Learning playing a key role in adapting them to specific applications. However, obtaining ground truth answe…
Logical ReasoningMathematical Problem-SolvingError Typing for Smarter Rewards: Improving Process Reward Models with Error-Aware Hierarchical Supervision
Large Language Models (LLMs) are prone to hallucination, especially during multi-hop and reasoning-intensive tasks such as mathematical problem solving. While Outcome Reward Models verify only final answers, Process Rewa…
HallucinationMathMathematical Problem-SolvingMathematical Reasoning+1RaDeR: Reasoning-aware Dense Retrieval Models
We propose RaDeR, a set of reasoning-based dense retrieval models trained with data derived from mathematical problem solving using large language models (LLMs). Our method leverages retrieval-augmented reasoning traject…
MathMathematical Problem-SolvingMathematical ReasoningRetrievalSMART: Self-Generating and Self-Validating Multi-Dimensional Assessment for LLMs' Mathematical Problem Solving
Large Language Models have achieved remarkable results on a variety of mathematical benchmarks. However, concerns remain as to whether these successes reflect genuine mathematical reasoning or superficial pattern recogni…
DiagnosticMathematical Problem-SolvingMathematical ReasoningCan reasoning models comprehend mathematical problems in Chinese ancient texts? An empirical study based on data from Suanjing Shishu
This study addresses the challenges in intelligent processing of Chinese ancient mathematical classics by constructing Guji_MATH, a benchmark for evaluating classical texts based on Suanjing Shishu. It systematically ass…
Mathematical Problem-SolvingTowards Spoken Mathematical Reasoning: Benchmarking Speech-based Models over Multi-faceted Math Problems
Recent advances in large language models (LLMs) and multimodal LLMs (MLLMs) have led to strong reasoning ability across a wide range of tasks. However, their ability to perform mathematical reasoning from spoken input re…
BenchmarkingMathMathematical Problem-SolvingMathematical Reasoning+1HARDMath2: A Benchmark for Applied Mathematics Built by Students as Part of a Graduate Class
Large language models (LLMs) have shown remarkable progress in mathematical problem-solving, but evaluation has largely focused on problems that have exact analytical solutions or involve formal proofs, often overlooking…
MathMathematical Problem-SolvingMathematical ReasoningIs PRM Necessary? Problem-Solving RL Implicitly Induces PRM Capability in LLMs
The development of reasoning capabilities represents a critical frontier in large language models (LLMs) research, where reinforcement learning (RL) and process reward models (PRMs) have emerged as predominant methodolog…
Mathematical Problem-SolvingReinforcement Learning (RL)Reasoning with OmniThought: A Large CoT Dataset with Verbosity and Cognitive Difficulty Annotations
The emergence of large reasoning models (LRMs) has transformed Natural Language Processing by excelling in complex tasks such as mathematical problem-solving and code generation. These models leverage chain-of-thought (C…
Code GenerationMathematical Problem-SolvingPT-MoE: An Efficient Finetuning Framework for Integrating Mixture-of-Experts into Prompt Tuning
Parameter-efficient fine-tuning (PEFT) methods have shown promise in adapting large language models, yet existing approaches exhibit counter-intuitive phenomena: integrating router into prompt tuning (PT) increases train…
MathMathematical Problem-SolvingMixture-of-Expertsparameter-efficient fine-tuning+1