Table of contents
- (논문 요약) AGENTLESS; Demystifying LLM-based Software Engineering Agents
- (논문 요약) AgentCoder; Multiagent-Code Generation with Iterative Testing and Optimisation
- (논문 요약) Buffer of Thoughts; Thought-Augmented Reasoning with Large Language Models
- (논문 요약) Code Generation with AlphaCodium; From Prompt Engineering to Flow Engineering
- (논문 요약) CodeGemma; Open Code Models Based on Gemma
- (논문 요약) CodeRetriever; Large-scale Contrastive Pre-training for Code Search
- (논문 요약) Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRs
- (논문 요약) DeepSeek-Coder-V2; Breaking the Barrier of Closed-Source Models in Code Intelligence
- (논문 요약) DeepSeek-Prover; Advancing Theorem Proving in LLMs through Large-Scale Synthetic Data
- (논문 요약) Efficient Tool Use with Chain-of-Abstraction Reasoning
- (논문 요약) Executable Code Actions Elicit Better LLM Agents
- (논문 요약) Faithful Logical Reasoning via Symbolic Chain-of-Thought
- (논문 요약) Gold-medalist Performance in Solving Olympiad Geometry with AlphaGeometry2
- (논문 요약) Granite Code Models; A Family of Open Foundation Models for Code Intelligence
- (논문 요약) Integrating Code Generation with Execution and Refinement
- (논문 요약) LEAN-GitHub; Compiling GitHub LEAN repositories for a versatile LEAN prover
- (논문 요약) LIMO; Less Is More for Reasoning
- (논문 요약) Large Language Monkeys; Scaling Inference Compute with Repeated Sampling
- (논문 요약) Large Language Monkeys; Scaling Inference Compute with Repeated Sampling
- (논문 요약) Logic-of-Thought; Injecting Logic into Contexts for Full Reasoning in Large Language Models
- (논문 요약) MUTUAL REASONING MAKES SMALLER LLMS STRONGER PROBLEM-SOLVERS
- (논문 요약) Meta Large Language Model Compiler; Foundation Models of Compiler Optimization
- (논문 요약) NATURALREASONING; Reasoning in the Wild with 2.8M Challenging Questions
- (논문 요약) NExT; Teaching Large Language Models to Reason about Code Execution
- (논문 요약) OPENCODER; THE OPEN COOKBOOK FOR TOP-TIER CODE LARGE LANGUAGE MODELS
- (논문 요약) Open-Reasoner-Zero; An Open Source Approach to Scaling Up Reinforcement Learning on the Base Model
- (논문 요약) OpenAI o1 System Card
- (논문 요약) Qwen2.5-Coder Technical Report
- (논문 요약) REACT; SYNERGIZING REASONING AND ACTING IN LANGUAGE MODELS
- (논문 요약) Reverse Thinking Makes LLMs Stronger Reasoners
- (논문 요약) SWE-AGENT; AGENT-COMPUTER INTERFACES ENABLE AUTOMATED SOFTWARE ENGINEERING
- (논문 요약) SWE-BENCH; CAN LANGUAGE MODELS RESOLVE REAL-WORLD GITHUB ISSUES?
- (논문 요약) SWE-Lancer; Can Frontier LLMs Earn $1 Million from Real-World Freelance Software Engineering?
- (논문 요약) Sky-T1; Train your own O1 preview model within $450
- (논문 요약) Solving olympiad geometry without human demonstrations
- (논문 요약) TORA; A TOOL-INTEGRATED REASONING AGENT FOR MATHEMATICAL PROBLEM SOLVING
- (논문 요약) TULU 3; Pushing Frontiers in Open Language Model Post-Training
- (논문 요약) To Code, or Not To Code? Exploring Impact of Code in Pre-training
- (논문 요약) Training Large Language Models to Reason in a Continuous Latent Space
- (논문 요약) Weak-to-Strong Reasoning
- (논문 요약) rStar-Math; Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking
- (블로그 요약) R1-reproduce
- (코드 실행) Alphacodium