Papers Math
“Math” 태그가 달린 논문 1,596편 · 필터 해제
Guideline Forest: Experience-Induced Multi-Guideline Reasoning with Stepwise Aggregation
Human reasoning is flexible, adaptive, and grounded in prior experience-qualities that large language models (LLMs) still struggle to emulate. Existing methods either explore diverse reasoning paths at inference time or …
GSM8KHumanEvalMathmbppPlay to Generalize: Learning to Reason Through Game Play
Developing generalizable reasoning capabilities in multimodal large language models (MLLMs) remains challenging. Motivated by cognitive science literature suggesting that gameplay promotes transferable cognitive skills, …
Domain GeneralizationMathMultimodal ReasoningReinforcement Learning (RL)WeThink: Toward General-purpose Vision-Language Reasoning via Reinforcement Learning
Building on the success of text-based reasoning models like DeepSeek-R1, extending these capabilities to multimodal reasoning holds great promise. While recent works have attempted to adapt DeepSeek-R1-style reinforcemen…
MathMathematical ReasoningMultimodal ReasoningReinforcement Learning (RL)The Illusion of Thinking: Understanding the Strengths and Limitations of Reasoning Models via the Lens of Problem Complexity
Recent generations of language models have introduced Large Reasoning Models (LRMs) that generate detailed thinking processes before providing answers. While these models demonstrate improved performance on reasoning ben…
MathSpectral Derivatives
One of the happiest accidents in all math is the ease of transforming a function to and taking derivatives in the Fourier frequency domain. But in order to exploit this extraordinary fact without serious artefacting, and…
MathSPARQ: Synthetic Problem Generation for Reasoning via Quality-Diversity Algorithms
Large language model (LLM) driven synthetic data generation has emerged as a powerful method for improving model reasoning capabilities. However, most methods either distill large state-of-the-art models into small stude…
DiversityLarge Language ModelMathSynthetic Data GenerationAgentSwift: Efficient LLM Agent Design via Value-guided Hierarchical Search
Large language model (LLM) agents have demonstrated strong capabilities across diverse domains. However, designing high-performing agentic systems remains challenging. Existing agent search methods suffer from three majo…
Large Language ModelMathPerceptual Decoupling for Scalable Multi-modal Reasoning via Reward-Optimized Captioning
Recent advances in slow-thinking language models (e.g., OpenAI-o1 and DeepSeek-R1) have demonstrated remarkable abilities in complex reasoning tasks by emulating human-like reflective cognition. However, extending such c…
MathVisual GroundingMINT-CoT: Enabling Interleaved Visual Tokens in Mathematical Chain-of-Thought Reasoning
Chain-of-Thought (CoT) has widely enhanced mathematical reasoning in Large Language Models (LLMs), but it still remains challenging for extending it to multimodal domains. Existing works either adopt a similar textual re…
MathMathematical ReasoningVisual ReasoningSimulating LLM-to-LLM Tutoring for Multilingual Math Feedback
Large language models (LLMs) have demonstrated the ability to generate formative feedback and instructional hints in English, making them increasingly relevant for AI-assisted education. However, their ability to provide…
MathAutomatic Robustness Stress Testing of LLMs as Mathematical Problem Solvers
Large language models (LLMs) have achieved distinguished performance on various reasoning-intensive tasks. However, LLMs might still face the challenges of robustness issues and fail unexpectedly in some simple reasoning…
GSM8KMathMMLUMathematical Reasoning for Unmanned Aerial Vehicles: A RAG-Based Approach for Complex Arithmetic Reasoning
Autonomous UAV operation necessitates reliable mathematical reasoning for tasks such as trajectory planning and power management. While traditional flight control relies on hardcoded equations, recent Large Language Mode…
Arithmetic ReasoningMathMathematical ReasoningRAG+3TreeRPO: Tree Relative Policy Optimization
Large Language Models (LLMs) have shown remarkable reasoning capabilities through Reinforcement Learning with Verifiable Rewards (RLVR) methods. However, a key limitation of existing approaches is that rewards defined at…
MathConfidence Is All You Need: Few-Shot RL Fine-Tuning of Language Models
Large language models (LLMs) excel at reasoning, yet post-training remains critical for aligning their behavior with task goals. Existing reinforcement learning (RL) methods often depend on costly human annotations or ex…
AllMathreinforcement-learningReinforcement Learning+1Generating Pedagogically Meaningful Visuals for Math Word Problems: A New Benchmark and Analysis of Text-to-Image Models
Visuals are valuable tools for teaching math word problems (MWPs), helping young learners interpret textual descriptions into mathematical expressions before solving them. However, creating such visuals is labor-intensiv…
MathRectified Sparse Attention
Efficient long-sequence generation is a critical challenge for Large Language Models. While recent sparse decoding methods improve efficiency, they suffer from KV cache misalignment, where approximation errors accumulate…
Language ModelingLanguage ModellingMathOpenThoughts: Data Recipes for Reasoning Models
Reasoning models have made rapid progress on many benchmarks involving math, code, and science. Yet, there are still many open questions about the best training recipes for reasoning since state-of-the-art models often r…
MathGuided Speculative Inference for Efficient Test-Time Alignment of LLMs
We propose Guided Speculative Inference (GSI), a novel algorithm for efficient reward-guided decoding in large language models. GSI combines soft best-of-$n$ test-time scaling with a reward model $r(x,y)$ and speculative…
MathUnleashing the Reasoning Potential of Pre-trained LLMs by Critique Fine-Tuning on One Problem
We have witnessed that strong LLMs like Qwen-Math, MiMo, and Phi-4 possess immense reasoning potential inherited from the pre-training stage. With reinforcement learning (RL), these models can improve dramatically on rea…
GPUMathReinforcement Learning (RL)MASTER: Enhancing Large Language Model via Multi-Agent Simulated Teaching
Instruction fine-tuning is crucial in NLP tasks, enhancing pretrained models' instruction-following capabilities and task-specific performance. However, obtaining high-quality fine-tuning data for large models is challen…
Data AugmentationInstruction FollowingLanguage ModelingLanguage Modelling+2