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TitleStatusHype
Token-by-Token Regeneration and Domain Biases: A Benchmark of LLMs on Advanced Mathematical Problem-Solving—0
Token-Hungry, Yet Precise: DeepSeek R1 Highlights the Need for Multi-Step Reasoning Over Speed in MATH—0
LaRS: Latent Reasoning Skills for Chain-of-Thought Reasoning—0
Benchmarking and Improving Generator-Validator Consistency of Language Models—0
Layer Swapping for Zero-Shot Cross-Lingual Transfer in Large Language Models—0
Laying the Foundation First? Investigating the Generalization from Atomic Skills to Complex Reasoning Tasks—0
BeamLoRA: Beam-Constraint Low-Rank Adaptation—0
Basic concepts, definitions, and methods in D number theory—0
Lean-ing on Quality: How High-Quality Data Beats Diverse Multilingual Data in AutoFormalization—0
Backward bifurcation and saddle-node bifurcation in virus-immune dynamics—0
Learning Autonomous Code Integration for Math Language Models—0
Learning Beyond Pattern Matching? Assaying Mathematical Understanding in LLMs—0
Learning by Analogy: Enhancing Few-Shot Prompting for Math Word Problem Solving with Computational Graph-Based Retrieval—0
Token-Supervised Value Models for Enhancing Mathematical Reasoning Capabilities of Large Language Models—0
Learning Fine-Grained Expressions to Solve Math Word Problems—0
Learning from Peers in Reasoning Models—0
Activation Functions Considered Harmful: Recovering Neural Network Weights through Controlled Channels—0
What Makes a Good Dataset for Symbol Description Reading?—0
Learning Hierarchical Structures On-The-Fly with a Recurrent-Recursive Model for Sequences—0
151 Estrategias de Trading (151 Trading Strategies)—0
Learning to Love Edge Cases in Formative Math Assessment: Using the AMMORE Dataset and Chain-of-Thought Prompting to Improve Grading Accuracy—0
Learning to Rank Chain-of-Thought: An Energy-Based Approach with Outcome Supervision—0
Learning to Reason Across Parallel Samples for LLM Reasoning—0
Backup Control Barrier Functions: Formulation and Comparative Study—0
WARM: A Weakly (+Semi) Supervised Model for Solving Math word Problems—0
Learning to Solve Domain-Specific Calculation Problems with Knowledge-Intensive Programs Generator—0
Least-to-Most Prompting Enables Complex Reasoning in Large Language Models—0
Les mathématiques de la langue : l'approche formelle de Montague—0
Let's Do a Thought Experiment: Using Counterfactuals to Improve Moral Reasoning—0
Let's Reason Formally: Natural-Formal Hybrid Reasoning Enhances LLM's Math Capability—0
Let's Reinforce Step by Step—0
Let's reward step by step: Step-Level reward model as the Navigators for Reasoning—0
Auto-regressive Text Generation with Pre-Trained Language Models: An Empirical Study on Question-type Short Text Generation—0
Leveraging Affect Transfer Learning for Behavior Prediction in an Intelligent Tutoring System—0
Leveraging LLMs to Assess Tutor Moves in Real-Life Dialogues: A Feasibility Study—0
Leveraging Multimodal Dialog Technology for the Design of Automated and Interactive Student Agents for Teacher Training—0
Leveraging Unstructured Text Data for Federated Instruction Tuning of Large Language Models—0
Towards better Human-Agent Alignment: Assessing Task Utility in LLM-Powered Applications—0
LEWIS (LayEr WIse Sparsity) -- A Training Free Guided Model Merging Approach—0
Limits of an AI program for solving college math problems—0
Automatized Evaluation of Formalization Exercises in Mathematics—0
LinguaLIFT: An Effective Two-stage Instruction Tuning Framework for Low-Resource Language Tasks—0
Automatic tagging of knowledge points for K12 math problems—0
Automatic Robustness Stress Testing of LLMs as Mathematical Problem Solvers—0
LLaMa-SciQ: An Educational Chatbot for Answering Science MCQ—0
Automatic Generation of Question Hints for Mathematics Problems using Large Language Models in Educational Technology—0
Meaning-Typed Programming: Language Abstraction and Runtime for Model-Integrated Applications—0
LLMs as Potential Brainstorming Partners for Math and Science Problems—0
Automatic Generation of High Quality CCGbanks for Parser Domain Adaptation—0
LLMs can Find Mathematical Reasoning Mistakes by Pedagogical Chain-of-Thought—0
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