✨Paper2Code: Automating Code Generation from Scientific Papers in Machine Learning

📝 Summary:
PaperCoder is a multi-agent LLM framework that automates converting machine learning papers into functional code repositories. It uses planning, analysis, and generation stages with specialized agents. Evaluations show it effectively creates high-quality implementations, outperforming strong base...

🔹 Publication Date: Published on Apr 24

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2504.17192
• PDF: https://arxiv.org/pdf/2504.17192
• Project Page: https://huggingface.co/papers/2504.15080
• Github: https://github.com/going-doer/Paper2Code

✨ Datasets citing this paper:
• https://huggingface.co/datasets/iaminju/paper2code

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#CodeGeneration #MachineLearning #LLM #AI #Automation

✨DRIVE: Data Curation Best Practices for Reinforcement Learning with Verifiable Reward in Competitive Code Generation

📝 Summary:
This study develops a two-stage reinforcement learning method for competitive code generation. It uses tailored data curation and a hard-focus curriculum, achieving state-of-the-art performance on competitive programming benchmarks.

🔹 Publication Date: Published on Nov 9

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.06307
• PDF: https://arxiv.org/pdf/2511.06307

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#ReinforcementLearning #CodeGeneration #DataCuration #MachineLearning #AIResearch

✨UI2Code^N: A Visual Language Model for Test-Time Scalable Interactive UI-to-Code Generation

📝 Summary:
UI2Code^N is a visual language model trained for interactive UI-to-code generation, editing, and polishing. It uses multi-turn feedback to achieve state-of-the-art performance among open-source models, comparable to leading closed-source solutions.

🔹 Publication Date: Published on Nov 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2511.08195
• PDF: https://arxiv.org/pdf/2511.08195
• Project Page: https://zheny2751-dotcom.github.io/ui2code-n.github.io/
• Github: https://zheny2751-dotcom.github.io/ui2code-n.github.io/

🔹 Models citing this paper:
• https://huggingface.co/zai-org/UI2Code_N

✨ Spaces citing this paper:
• https://huggingface.co/spaces/zai-org/UI2Code_N-demo-case

==================================

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#UI2Code #VisualLanguageModels #CodeGeneration #AI #SoftwareEngineering

✨Code2Video: A Code-centric Paradigm for Educational Video Generation

📝 Summary:
Code2Video is a code-centric agent framework generating educational videos via executable Python code. It uses three collaborative agents to improve coherence and interpretability, outperforming direct code generation by 40% and matching human-crafted tutorials.

🔹 Publication Date: Published on Oct 1

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2510.01174
• PDF: https://arxiv.org/pdf/2510.01174
• Project Page: https://showlab.github.io/Code2Video/
• Github: https://github.com/showlab/code2video

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#AI #VideoGeneration #EducationalTech #CodeGeneration #DeepLearning

✨WizardCoder: Empowering Code Large Language Models with Evol-Instruct

📝 Summary:
WizardCoder is a Code LLM fine-tuned using Evol-Instruct for complex instructions. It significantly outperforms open-source and major closed LLMs on code generation benchmarks.

🔹 Publication Date: Published on Jun 14, 2023

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2306.08568
• PDF: https://arxiv.org/pdf/2306.08568
• Github: https://github.com/nlpxucan/WizardLM

🔹 Models citing this paper:
• https://huggingface.co/WizardLMTeam/WizardCoder-Python-34B-V1.0
• https://huggingface.co/WizardLMTeam/WizardCoder-15B-V1.0
• https://huggingface.co/alpindale/WizardLM-2-8x22B

✨ Datasets citing this paper:
• https://huggingface.co/datasets/WizardLMTeam/WizardLM_evol_instruct_V2_196k
• https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1
• https://huggingface.co/datasets/WizardLMTeam/WizardLM_evol_instruct_70k

✨ Spaces citing this paper:
• https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard
• https://huggingface.co/spaces/Intel/low_bit_open_llm_leaderboard
• https://huggingface.co/spaces/FallnAI/Quantize-HF-Models

==================================

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#CodeLLM #LLM #AIE #CodeGeneration #EvolInstruct

✨SWE-Bench++: A Framework for the Scalable Generation of Software Engineering Benchmarks from Open-Source Repositories

📝 Summary:
SWE-Bench++ is an automated framework generating scalable, multilingual, repository-level coding tasks from live GitHub pull requests. It overcomes manual curation limits and static datasets, offering a benchmark to evaluate and improve code generation models across 11 languages.

🔹 Publication Date: Published on Dec 19

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.17419
• PDF: https://arxiv.org/pdf/2512.17419
• Project Page: https://research.turing.com/swebench
• Github: https://huggingface.co/papers?q=GitHub%20pull%20requests

==================================

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#SoftwareEngineering #CodeGeneration #AIBenchmarking #MachineLearning #OpenSource

✨SecureCode v2.0: A Production-Grade Dataset for Training Security-Aware Code Generation Models

📝 Summary:
SecureCode v2.0 is a production-grade dataset of 1215 security-focused coding examples. It trains AI models to generate secure code by providing real-incident examples with vulnerable and secure implementations, attacks, defense, and operational security context across 11 languages, using a conve...

🔹 Publication Date: Published on Dec 20

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2512.18542
• PDF: https://arxiv.org/pdf/2512.18542
• Project Page: https://perfecxion.ai/
• Github: https://github.com/scthornton/securecode-v2

==================================

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#Cybersecurity #CodeSecurity #AI #CodeGeneration #Dataset

✨Towards Automated Kernel Generation in the Era of LLMs

📝 Summary:
This survey explores how large language models and agent systems are automating kernel generation and optimization, a critical yet non-scalable process for AI systems. It provides a structured overview of existing approaches, datasets, and benchmarks, aiming to unify this fragmented field and out...

🔹 Publication Date: Published on Jan 22

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.15727
• PDF: https://arxiv.org/pdf/2601.15727
• Github: https://github.com/flagos-ai/awesome-LLM-driven-kernel-generation

==================================

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#LLMs #KernelGeneration #AI #Automation #CodeGeneration

✨GoodVibe: Security-by-Vibe for LLM-Based Code Generation

📝 Summary:
GoodVibe secures LLM-generated code by precisely fine-tuning only a small subset of security-relevant neurons. This neuron-level framework greatly enhances code security and preserves utility with significantly fewer parameters and training costs than traditional methods.

🔹 Publication Date: Published on Feb 11

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.10778
• PDF: https://arxiv.org/pdf/2602.10778

==================================

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#LLM #CodeGeneration #Cybersecurity #AIsecurity #MachineLearning

✨Code2Worlds: Empowering Coding LLMs for 4D World Generation

📝 Summary:
Code2Worlds empowers coding LLMs to generate 4D dynamic scenes by formulating it as language-to-simulation code. It uses a dual-stream architecture and physics-aware closed-loop refinement to ensure physical fidelity. The system significantly outperforms baselines, uniquely generating realistic, ...

🔹 Publication Date: Published on Feb 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.11757
• PDF: https://arxiv.org/pdf/2602.11757
• Project Page: https://aigeeksgroup.github.io/Code2Worlds
• Github: https://aigeeksgroup.github.io/Code2Worlds

==================================

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#LLM #CodeGeneration #4DGeneration #AISimulation #Research

✨Nanbeige4.1-3B: A Small General Model that Reasons, Aligns, and Acts

📝 Summary:
Nanbeige4.1-3B is a 3B-parameter model excelling in agentic behavior, code generation, and reasoning. It outperforms larger models through advanced reward modeling and training, demonstrating broad competence for a small language model.

🔹 Publication Date: Published on Feb 13

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.13367
• PDF: https://arxiv.org/pdf/2602.13367
• Project Page: https://huggingface.co/Nanbeige/Nanbeige4.1-3B

🔹 Models citing this paper:
• https://huggingface.co/Nanbeige/Nanbeige4.1-3B

✨ Spaces citing this paper:
• https://huggingface.co/spaces/PioTio/AIMan

==================================

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#LLM #AI #SmallLanguageModels #AgenticAI #CodeGeneration

✨TAROT: Test-driven and Capability-adaptive Curriculum Reinforcement Fine-tuning for Code Generation with Large Language Models

📝 Summary:
TAROT proposes a reinforcement fine-tuning method for code generation that uses a four-tier test suite and capability-adaptive curriculum. This approach tailors curriculum progression based on a models skill, improving functional correctness and robustness.

🔹 Publication Date: Published on Feb 17

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.15449
• PDF: https://arxiv.org/pdf/2602.15449
• Github: https://github.com/deep-diver/TAROT

==================================

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#LLM #CodeGeneration #ReinforcementLearning #AI #MachineLearning

✨CL4SE: A Context Learning Benchmark For Software Engineering Tasks

📝 Summary:
CL4SE presents a benchmark for evaluating context learning in software engineering tasks, defining four SE-specific context types. It demonstrates an average 24.7% performance improvement for LLMs across tasks like code generation and review, establishing a standardized evaluation framework.

🔹 Publication Date: Published on Feb 26

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.23047
• PDF: https://arxiv.org/pdf/2602.23047
• Project Page: https://huggingface.co/papers?q=project-specific%20context
• Github: https://github.com/Tomsawyerhu/CodeCL

✨ Datasets citing this paper:
• https://huggingface.co/datasets/tomhu/codecl

==================================

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#ContextLearning #SoftwareEngineering #LLMs #CodeGeneration #Benchmarks

✨V_1: Unifying Generation and Self-Verification for Parallel Reasoners

📝 Summary:
V1 unifies generation and verification for complex reasoning tasks. It leverages models' superior ability in pairwise self-verification over independent scoring, improving performance and efficiency in code generation and math.

🔹 Publication Date: Published on Mar 4

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.04304
• PDF: https://arxiv.org/pdf/2603.04304
• Project Page: https://harmandotpy.github.io/v1-verification/
• Github: https://github.com/HarmanDotpy/pairwise-self-verification

==================================

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#AI #LLMs #MachineLearning #CodeGeneration #AIReasoning

✨ReflexiCoder: Teaching Large Language Models to Self-Reflect on Generated Code and Self-Correct It via Reinforcement Learning

📝 Summary:
ReflexiCoder uses reinforcement learning to teach large language models autonomous code reflection and self-correction. It internalizes the debugging process into the model, achieving state-of-the-art performance on coding benchmarks, rivaling proprietary models, and reducing inference compute by...

🔹 Publication Date: Published on Mar 6

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.05863
• PDF: https://arxiv.org/pdf/2603.05863
• Github: https://github.com/juyongjiang/ReflexiCoder

==================================

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#LLM #ReinforcementLearning #CodeGeneration #AI #DeepLearning

✨CreativeBench: Benchmarking and Enhancing Machine Creativity via Self-Evolving Challenges

📝 Summary:
Researchers introduced CreativeBench, a benchmark for evaluating machine creativity in code generation using a quality-novelty metric. They found scaling improves combinatorial creativity but yields diminishing returns for exploration. They also proposed EvoRePE, an inference-time strategy to enh...

🔹 Publication Date: Published on Mar 12

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.11863
• PDF: https://arxiv.org/pdf/2603.11863
• Project Page: https://zethwang.github.io/creativebench.github.io/
• Github: https://github.com/ZethWang/CreativeBench

==================================

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#MachineCreativity #CodeGeneration #AIBenchmark #GenerativeAI #AIResearch

✨SlopCodeBench: Benchmarking How Coding Agents Degrade Over Long-Horizon Iterative Tasks

📝 Summary:
Software development is iterative, yet agentic coding benchmarks overwhelmingly evaluate single-shot solutions against complete specifications. Code can pass the test suite but become progressively ha...

🔹 Publication Date: Published on Mar 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.24755
• PDF: https://arxiv.org/pdf/2603.24755
• Project Page: https://www.scbench.ai
• Github: https://github.com/SprocketLab/slop-code-bench

==================================

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#AICoding #Benchmarking #LLMAgents #SoftwareEngineering #CodeGeneration

✨Learning to Commit: Generating Organic Pull Requests via Online Repository Memory

📝 Summary:
Learning to Commit improves LLM coding agent organicity using Online Repository Memory. It distills project-specific coding skills from historical commits, guiding agents to generate code that adheres to project conventions and architectural patterns, leading to more acceptable pull requests.

🔹 Publication Date: Published on Mar 27

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.26664
• PDF: https://arxiv.org/pdf/2603.26664

==================================

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#LLMAgents #SoftwareEngineering #CodeGeneration #AIResearch #MachineLearning

✨Composer 2 Technical Report

📝 Summary:
Composer 2 is a specialized coding model trained via phased learning for real-world software engineering tasks. It demonstrates superior performance on new and public benchmarks, showcasing strong long-term planning and coding intelligence.

🔹 Publication Date: Published on Mar 25

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2603.24477
• PDF: https://arxiv.org/pdf/2603.24477

==================================

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#AI #Coding #SoftwareEngineering #MachineLearning #CodeGeneration

✨InCoder-32B-Thinking: Industrial Code World Model for Thinking

📝 Summary:
Industrial software development lacks expert reasoning traces for hardware constraints, so a model was trained on error-driven reasoning chains and domain-specific execution traces to generate high-qu...

🔹 Publication Date: Published on Apr 3

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2604.03144
• PDF: https://arxiv.org/pdf/2604.03144

==================================

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#AI #CodeGeneration #IndustrialAI #WorldModels #SoftwareDevelopment