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TitleStatusHype
Can LLMs Reason Abstractly Over Math Word Problems Without CoT? Disentangling Abstract Formulation From Arithmetic Computation—0
A Graph-Based Synthetic Data Pipeline for Scaling High-Quality Reasoning Instructions—0
HyperCLOVA X Technical Report—0
Evaluating Grounded Reasoning by Code-Assisted Large Language Models for Mathematics—0
Human Learning about AI—0
Evaluating GPT-4 at Grading Handwritten Solutions in Math Exams—0
A Chain-of-Thought Prompting Approach with LLMs for Evaluating Students' Formative Assessment Responses in Science—0
Hydrodynamics of Markets:Hidden Links Between Physics and Finance—0
Hypothesis-Driven Theory-of-Mind Reasoning for Large Language Models—0
Improving Academic Plagiarism Detection for STEM Documents by Analyzing Mathematical Content and Citations—0
Can I understand what I create? Self-Knowledge Evaluation of Large Language Models—0
Can ChatGPT Defend its Belief in Truth? Evaluating LLM Reasoning via Debate—0
A Practice of Post-Training on Llama-3 70B with Optimal Selection of Additional Language Mixture Ratio—0
Error Classification of Large Language Models on Math Word Problems: A Dynamically Adaptive Framework—0
AceReason-Nemotron: Advancing Math and Code Reasoning through Reinforcement Learning—0
How well do Computers Solve Math Word Problems? Large-Scale Dataset Construction and Evaluation—0
EoRA: Training-free Compensation for Compressed LLM with Eigenspace Low-Rank Approximation—0
Approximation properties of Residual Neural Networks for Kolmogorov PDEs—0
Entropy Martingale Optimal Transport and Nonlinear Pricing-Hedging Duality—0
Calculus on MDPs: Potential Shaping as a Gradient—0
Approximating Sparse PCA from Incomplete Data—0
Entropy-Guided Watermarking for LLMs: A Test-Time Framework for Robust and Traceable Text Generation—0
BurTorch: Revisiting Training from First Principles by Coupling Autodiff, Math Optimization, and Systems—0
Entropy Adaptive Decoding: Dynamic Model Switching for Efficient Inference—0
Entropic Distribution Matching in Supervised Fine-tuning of LLMs: Less Overfitting and Better Diversity—0
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