software-engineering

GitHub has published research on validating agentic AI behavior when there's no single "correct" answer. The company proposes dominance analysis as an alternative to brittle scripts or opaque LLM-as-judge approaches for building a trust layer in GitHub Copilot coding agents.

MiniMax has released MiniMax-M2.7, a 229-billion parameter model that uniquely participates in its own evolution during development. The model achieves 66.6% medal rate on MLE Bench Lite and 56.22% on SWE-Pro benchmarks, with native support for multi-agent collaboration and complex tool orchestration.

Zhipu AI has released GLM-5.1, a freely available open-weight model designed for long-running programming tasks that achieves 58.4% on SWE-Bench Pro, edging out GPT-5.4 (57.7%) and Claude Opus 4.6 (57.3%). The model's core capability is iterative strategy refinement—it rethinks its approach across hundreds of iterations and thousands of tool calls, recognizing dead ends and shifting tactics without human intervention. However, GLM-5.1 trails on reasoning and knowledge benchmarks, scoring 31% on Humanity's Last Exam compared to Gemini 3.1 Pro's 45%.

Zhipu AI has released GLM-5.1, a 754-billion parameter model designed for agentic engineering with significantly improved coding capabilities over its predecessor. The model achieves 58.4% on SWE-Bench Pro and demonstrates sustained performance improvement over hundreds of tool calls and iterations, unlike earlier models that plateau quickly.