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README.md

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 # Awesome-Code-LLM
 
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 This is the repo for our [TMLR](https://jmlr.org/tmlr/) survey [Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code](https://arxiv.org/abs/2311.07989) - a comprehensive review of LLM researches for code. Works in each category are ordered chronologically. If you have a basic understanding of machine learning but are new to NLP, we also provide a list of recommended readings in [section 9](#9-recommended-readings). If you refer to this repo, please cite:
 
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 🔥🔥🔥 [2025/10/13] Featured papers:
 
-- 🔥🔥 [LiveOIBench: Can Large Language Models Outperform Human Contestants in Informatics Olympiads?](https://arxiv.org/abs/2510.09595) from University of Michigan.
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-- 🔥🔥 [Scaling Laws for Code: A More Data-Hungry Regime](https://arxiv.org/abs/2510.08702) from Harbin Institute of Technology.
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-- 🔥🔥 [BigCodeArena: Unveiling More Reliable Human Preferences in Code Generation via Execution](https://arxiv.org/abs/2510.08697) from Monash University.
+- 🔥 [LiveOIBench: Can Large Language Models Outperform Human Contestants in Informatics Olympiads?](https://arxiv.org/abs/2510.09595) from University of Michigan.
 
-- 🔥 [CodeFuse-CR-Bench: A Comprehensiveness-aware Benchmark for End-to-End Code Review Evaluation in Python Projects](https://arxiv.org/abs/2509.14856) from Ant Group.
+- 🔥 [Scaling Laws for Code: A More Data-Hungry Regime](https://arxiv.org/abs/2510.08702) from Harbin Institute of Technology.
 
-- 🔥🔥 [EvoEngineer: Mastering Automated CUDA Kernel Code Evolution with Large Language Models](https://arxiv.org/abs/2510.03760) from City University of Hong Kong.
+- 🔥 [BigCodeArena: Unveiling More Reliable Human Preferences in Code Generation via Execution](https://arxiv.org/abs/2510.08697) from Monash University.
 
 🔥🔥&nbsp;&nbsp;&nbsp;&nbsp; [2025/08/24] 29 papers from ICML 2025 have been added. Search for the keyword "ICML 2025"!
 
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 🔥🔥🔥 [2025/09/22] News from Codefuse
 
+- We released [F2LLM](https://arxiv.org/abs/2510.02294), a fully open embedding model striking a strong balance between model size, training data, and embedding performance. [[code](https://github.com/codefuse-ai/CodeFuse-Embeddings)] [[model & data](https://huggingface.co/collections/codefuse-ai/codefuse-embeddings-68d4b32da791bbba993f8d14)]
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+- We released a new benchmark focusing on code review: [CodeFuse-CR-Bench: A Comprehensiveness-aware Benchmark for End-to-End Code Review Evaluation in Python Projects](https://arxiv.org/abs/2509.14856)
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 - [CGM (Code Graph Model)](https://arxiv.org/abs/2505.16901) is accepted to NeurIPS 2025. CGM currently ranks 1st among open-weight models on [SWE-Bench-Lite leaderboard](https://www.swebench.com/). [[repo](https://github.com/codefuse-ai/CodeFuse-CGM)]
 
 - [GALLa: Graph Aligned Large Language Models](https://arxiv.org/abs/2409.04183) is accepted by ACL 2025 main conference. [[repo](https://github.com/codefuse-ai/GALLa)]
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 - "Speed Up Your Code: Progressive Code Acceleration Through Bidirectional Tree Editing" [2025-07] [ACL 2025] [[paper](https://aclanthology.org/2025.acl-long.1387/)]
 
+- "ECO: Enhanced Code Optimization via Performance-Aware Prompting for Code-LLMs" [2025-10] [[paper](https://arxiv.org/abs/2510.10517)]
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 ### Binary Analysis and Decompilation
 
 - "Using recurrent neural networks for decompilation" [2018-03] [SANER 2018] [[paper](https://ieeexplore.ieee.org/document/8330222)]
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 - "Optimizing Token Choice for Code Watermarking: A RL Approach" [2025-08] [[paper](https://arxiv.org/abs/2508.11925)]
 
+- "Large Language Models Are Effective Code Watermarkers" [2025-10] [[paper](https://arxiv.org/abs/2510.11251)]
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 ### Others
 
 - "Code Membership Inference for Detecting Unauthorized Data Use in Code Pre-trained Language Models" [2023-12] [EMNLP 2024 Findings] [[paper](https://arxiv.org/abs/2312.07200)]