Rohan Paul

Claude Opus 5 nearly tripled a Qwen model's SWE-bench Verified score while working as an automated AI researcher. @Evolvent_AI 's newly released open-source RSIGym made that measurement possible by letting an AI agent retrain a model and rewrite its harness inside one budgeted environment. shows that frontier AI agents can substantially improve another AI model when training, serving, and testing come as ready-made services. Agents work from CPU-only containers and call remote services for LoRA fine-tuning, model serving, benchmarking and sandboxes, all charged against a per-run budget. In the main test, 6 frontier agents started from Qwen3.5-35B-A3B-Base and a minimal harness, with $500 of services per benchmark. If you're improving an agent, read its failure logs and fix the harness first: heavier training scored lower in 8 of 10 small tests.