PaperCompiler: Faithful Paper-to-Code Generation via Repository-Level Specification Compilation

arXiv:2609.02272 · cs.CL, cs.AI, cs.SE · Submitted 2026-09-02 · Read on arXiv

cs.CL, cs.AI, cs.SE

Submitted: 2026-09-02

Updated: 2026-09-02

Terminology

• Transformer — 40× in this paper · explained in 2 episodes
• Core — 20× in this paper · explained in 2 episodes
• Fidelity — 14× in this paper · explained in 2 episodes
• Grounding — 10× in this paper · explained in 3 episodes
• Ablation — 9× in this paper · explained in 1 episode
• Benchmark — 8× in this paper · explained in 2 episodes
• Alignment — 7× in this paper · explained in 1 episode
• Feed-Forward Network — 7× in this paper
• Consistency — 6× in this paper · explained in 3 episodes
• Hallucination — 2× in this paper · explained in 3 episodes
• Provenance — 2× in this paper · explained in 4 episodes
• Tokenization — 2× in this paper · explained in 2 episodes
• Topological Order — 2× in this paper · explained in 2 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Benchmarking — 1× in this paper · explained in 3 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Generative AI — 1× in this paper · explained in 2 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Mean Squared Error — 1× in this paper
• Multi-Agent Framework — 1× in this paper · explained in 3 episodes
• Reasoning Traces — 1× in this paper · explained in 3 episodes
• Semantic Drift — 1× in this paper · explained in 3 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Program Synthesis with Large Language Models
• CodePlan: Repository-level Coding using LLMs and Planning
• MLE-bench: Evaluating Machine Learning Agents on Machine Learning Engineering
• CrossCodeEval: A Diverse and Multilingual Benchmark for Cross-File Code Completion
• HiRAS: A Hierarchical Multi-Agent Framework for Paper-to-Code Generation and Execution
• MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework
• LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code
• SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
• DeepCode: Open Agentic Coding
• AutoP2C: An LLM-Based Agent Framework for Code Repository Generation from Multimodal Content in Academic Papers
• RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems
• Paper2Code: Automating Code Generation from Scientific Papers in Machine Learning
• PaperBench: Evaluating AI's Ability to Replicate AI Research
• ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code
• OpenHands: An Open Platform for AI Software Developers as Generalist Agents
• RE-Bench: Evaluating frontier AI R&D capabilities of language model agents against human experts
• Agentless: Demystifying LLM-based Software Engineering Agents
• SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering
• RepoCoder: Repository-Level Code Completion Through Iterative Retrieval and Generation
• AutoReproduce: Automatic AI Experiment Reproduction with Paper Lineage

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