#code
- 15 Jul 2026 · Paper summary
Paper summary: Function-Aware Fill-in-the-Middle as Mid-Training for Coding Agent Foundation Models
The authors note that an agent's act–observe–continue loop has the same shape as a function call site, and use function-level fill-in-the-middle mid-training on Python repos to improve coding agents on SWE-Bench.
- 19 May 2026 · Paper summary
Paper summary: Code as Agent Harness
A survey that treats code as the infrastructure of agents, not just their output: the interface agents act through, the mechanism for planning, memory and verification, and the shared artefact that coordinates multiple agents.
- 2 Dec 2025 · Paper summary
Paper summary: From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence
A large survey and practical guide to code LLMs, from data curation and pre-training through SFT, RL and coding agents, comparing general and code-specialised models and the gap between benchmarks and real development.
- 1 Sept 2025 · Paper summary
Paper summary: A.S.E: A Repository-Level Benchmark for Evaluating Security in AI-Generated Code
A.S.E is a repository-level benchmark for the security of AI-generated code; it finds that current LLMs still struggle with secure coding, and that a bigger reasoning budget doesn't reliably help.
- 8 Nov 2024 · Paper summary
Paper summary: OpenCoder: The Open Cookbook for Top-Tier Code Large Language Models
OpenCoder is a top-tier code LLM released as an "open cookbook": weights and inference code plus reproducible training data, the full data-processing pipeline and training protocols.
- 19 Sept 2024 · Paper summary
Paper summary: Qwen2.5-Coder Technical Report
Qwen2.5-Coder is a family of six code models (0.5B to 32B) continued-pretrained on over 5.5 trillion tokens, reported to set state-of-the-art results on more than 10 code benchmarks for their sizes.
- 30 Oct 2023 · Paper summary
Paper summary: CodeFusion: A Pre-trained Diffusion Model for Code Generation
CodeFusion generates code with a diffusion model that iteratively denoises a whole program conditioned on the natural-language request, instead of writing tokens left to right.