AI agents increasingly handle multi-step technical work: generating QUBO formulations, controlling CAD via local LLMs, writing/debugging drivers, and sharing full scientific trajectories.
A multi-agent framework generates QUBO formulations from natural-language descriptions and uses test cases to correct them. On QUBOBench, 100 problems across 12 domains, it reaches 68% accuracy, beating a single-call baseline by 22%. The result shift...
A public dataset records 558 complete AI scientific agent trajectories with thoughts, tool calls, errors, and confidence. Traditional benchmarks look only at final outputs; here comparable frontier models reveal different error profiles. Claude Opus ...
Nvidia has released SoL-Pi, a standalone extension for the Pi framework. It adds four opt-in mechanisms, disabled by default, born from auto-research loops: they reduce repeated model turns, context replay, oversized observations, and unnecessary log...
An experiment with an old MacBook shows an AI agent observing the screen through a webcam and mirror, writing AMD drivers and checking its own progress in real time. The case signals an 'agent-first' approach to driver development and local debugging...
A guide shows how to connect llama.cpp, the MCP protocol and FreeCAD to generate solids with a local model. The workflow uses Qwen3.8-27B quantized Q4_K_M and an mmproj-F16 vision projector: the model can call tools, read screenshots and verify geome...
An alternative to distillation and fine-tuning uses Codex and GPT Astra to transfer operational procedures for 3D modeling to Qwen Next via OpenCode and MCP Blender. The open model executes tasks after being guided, while the frontier model burns Pro...
