E2E coding agents: 27% pass, cheaper scaling, and safer adoption
A new end-to-end benchmark, [ProjDevBench](https://arxiv.org/html/2602.01655v1)[^1] with [code](https://github.com/zsworld6/projdevbench)[^2], reports only 27.38% acceptance for agent-built repos, highlighting gaps in system design, complexity, and resource management. Efficiency is improving: [SWE-Replay](https://quantumzeitgeist.com/17-4-percent-performance-swe-replay-achieves-gain-efficient/)[^3] recycles prior agent trajectories to cut test-time compute by up to 17.4% while maintaining or slightly improving fix rates. For evaluation and safety, Together AI shows open LLM judges can beat GPT‑5.2 on preference alignment ([post](https://www.together.ai/blog/fine-tuning-open-llm-judges-to-outperform-gpt-5-2at/))[^5], Java teams get a pragmatic path via [ASTRA‑LangChain4j](https://quantumzeitgeist.com/ai-astra-langchain4j-achieves-llm-integration/)[^6], and an open‑weight coding LM targets agentic/local dev ([Qwen3‑Coder‑Next](https://www.youtube.com/watch?v=UwVi2iu-xyA&pp=ygURU1dFLWJlbmNoIHJlc3VsdHM%3D))[^7]. [^1]: Adds: defines an E2E agent benchmark with architecture, correctness, and refinement criteria plus pass-rate findings. [^2]: Adds: benchmark repository for tasks, harnesses, and evaluation assets. [^3]: Adds: test-time scaling via trajectory replay with up to 17.4% cost reduction and small performance gains on SWE-Bench variants. [^4]: Adds: DPO-tuned open "LLM-as-judge" models outperform GPT‑5.2 on RewardBench 2 preference alignment, with code/how-to. [^5]: Adds: security analysis of self-propagating adversarial prompts ("prompt worms") and the OpenClaw agent network example. [^6]: Adds: Java integration pattern for agent+LLM via ASTRA modules and LangChain4J, including BeliefRAG and Maven packaging. [^7]: Adds: open-weight coding model positioned for agentic workflows and local development.