StarCoder: A State-of-the-Art LLM for Code

Creating a Coding Assistant with StarCoder

StarCoder2-Instruct: Fully Transparent and Permissive Self-Alignment for Code Generation

Introducing the LiveCodeBench Leaderboard - Holistic and Contamination-Free Evaluation of Code LLMs

CodeGemma - an official Google release for code LLMs

T-LLM Compiler: Trusted LLM-based Code Optimization and Verification Framework

arXiv:2608. 14953v1 Announce Type: new Abstract: Recent advances in Large Language Models (LLMs) have opened opportunities to apply high-level code transformations to the field of code optimization, and it has since emerged as one of the most fundamental tasks for LLMs to perform; however, at present, LLMs struggle to apply wide-ranging code optimization tasks due to both the complexity of the code and the inability to independently verify the correctness of the transformations.

CodeTD: Topology of Attention Detects Hallucinations in Code LLMs

The paper introduces CodeTD, a novel method that uses topological data analysis of attention maps from code language models to pre‑execution assess code correctness and detect hallucinations. It quantifies prompt‑generation mismatch through topological patterns and is evaluated on multiple benchmarks (HumanEval, MBPP, BigCodeBench, MultiPL‑E) across five programming languages and ten Code LLMs up to 34B parameters. Results show CodeTD outperforms recent baselines and transfers well between coding benchmarks.

📚 3LM: A Benchmark for Arabic LLMs in STEM and Code

Performance, Efficiency and Collapse -- Advantages and Challenges in Offline Post-training of Code LLMs

The paper investigates whether reinforcement‑learning post‑training of code‑generating large language models can be done entirely offline using existing datasets, avoiding costly online code generation and GPU‑CPU communication. Experiments show that a few hours of offline RL can substantially boost zero‑shot code generation performance across models from 0.5 B to 7 B parameters, though the magnitude of improvement differs by model family.

LLM4RTL: Tool-Assisted LLM for RTL Generation

arXiv:2606. 15500v1 Announce Type: cross Abstract: Large language models (LLMs) have facilitated impressive progress in software engineering, code generation, tooling, and systems.

LLM-Based Embeddings for Program Analysis and Optimization

arXiv:2608. 07894v1 Announce Type: new Abstract: Recent advances have highlighted the potential of machine learning, particularly Large Language Models (LLMs), for analyzing and optimizing programs.

Benchmarking LLMs for Verilog Design Flows

arXiv:2607. 22759v1 Announce Type: cross Abstract: Large language models (LLMs) show promise in code generation, but their capabilities to produce correct, synthesizable hardware description language (HDL) code still remain to be properly benchmarked.

SyGra: The One-Stop Framework for Building Data for LLMs and SLMs