Transfer a big model's smarts to a smaller one with no retraining — right at inference time. A fresh take on capability transfer. Title: AI4AI at Test-Time: Strong-to-Weak Capability Transfer via Harnesses URL: ❓ How is this different from distillation? 💡 Distillation updates the target's weights during training. Here the weights are never touched: a strong builder model constructs an inference-time scaffold (harness) that helps a weaker model execute. Capability transfers through the inference environment. ❓ What does the scaffold actually do? 💡 Mainly three things: ・Offload unstable reasoning into deterministic code ・Route to different strategies by question type ・Enforce strict formatting so answers parse reliably ❓ How well does it work? 💡 On four Theory-of-Mind benchmarks, GPT-5.4-mini nearly doubled from 0.49 to 0.91, with all 11 builder configs beating baseline. Weaker targets gain the most, while already-strong targets can even regress. ❓ What decides success? 💡 Not probing more validation data, but the builder's own reasoning quality. A strong builder acts as a "compiler of task competence," encoding structure into procedures in one pass. #AIAgents# #TestTimeScaling#