I implemented meta paper [P]
A developer built the PDR+RTV pipeline using Gemini 3.1 Pro on GitHub.
A developer on Reddit has released the first public implementation of Meta AI's latest research paper, "Scaling Test-Time Compute for Agentic Coding" (arXiv:2604.16529). The paper explores how allocating more compute at inference time can dramatically improve agentic coding performance — i.e., AI agents that autonomously write and debug code. Until now, no open-source replication of the core PDR (Progressive Difficulty Refinement) + RTV (Re-Try Verification) pipeline was available.
The implementation, hosted on GitHub under genji970/Scaling-Test-Time-Compute-for-Agentic-Coding, is a minimal research build that runs the Gemini 3.1 Pro model and evaluates on the SWE benchmark (the original paper also used additional benchmarks and models like Opus). Users need a Gemini API key to run the code. This allows researchers and developers to experiment with Meta's technique without rebuilding from scratch, potentially accelerating progress in AI-powered software engineering tools.
- First public implementation of Meta AI's scaling test-time compute paper for agentic coding.
- Implements the PDR+RTV pipeline using Gemini 3.1 Pro and tests on the SWE benchmark.
- Requires a Gemini API key; code is minimal and intended for research use.
Why It Matters
Bridges the gap between cutting-edge AI research and practical tooling for autonomous coding agents.