ERNIE Evolve de Baidu automatiza la optimización de código de IA, superando los benchmarks anteriores y mejorando la eficiencia de inferencia para su próximo modelo ERNIE-5.5-Flash.
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Baidu has just released ERNIE Evolve, an automated research system that improves AI training and inference code. Given a repository, an objective and evaluation criteria, it picks research directions, edits code and runs experiments. Only changes that pass a hidden evaluator and a separate reviewer are kept. Baidu team tested the ERNIE Evolve automated research system on three public benchmarks. It then optimized inference for ERNIE-5.5-Flash, still in development. TL;DR Size: It is an agent system; Baidu has not disclosed which LLMs power its agents. Runs on: Benchmarks used 1 NVIDIA B200 (NanoChat, SOL-ExecBench) and 8×H100 (Speedrun Track 1). Production tests ran on Kunlunxin P800 and GPUs (GPU model not disclosed). Performance: Beat the best public result on 3 of 4 public metrics, and cleared production checks on ERNIE-5.5-Flash. Best: Generation speed on Kunlunxin P800 reached 623.6% of baseline after 4 research rounds. Worst: SOL-ExecBench mean SOL of 0.819, behind the 0.827 leaderboard best. Bottom line (best): Real production inference gains: +33.7% deterministic throughput, +14.9% decode speed. Bottom line (worst): Self-reported, system not released, and public-benchmark margins are small. What is ERNIE Evolve? ERNIE Evolve is an automated research system from Baidu that optimizes code against a fixed, measurable objective. Baidu announced it on X as a practical step toward recursive self-improvement (RSI). In RSI, AI systems improve the software that builds and serves AI. How does ERNIE Evolve work? The system runs 2 nested loops. The outer loop decides what to investigate. The inner loop develops and tests improvements inside each direction. Director: picks the next research direction and a starting code version. It reads an Experience Bank of past results and a Knowledge Base of papers and code. Selector: chooses a candidate from a lane’s tree and names the next hypothesis to test. Executor: implements that plan in an isolated environment
Una ayuda automática para ordenar, basada en fuentes, palabras clave y entidades, en una escala de 1–12. Alta: 9–12; media: 6–8; baja: 1–5. No mide urgencia, verifica noticias ni representa respaldo editorial.
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