Baidu Inc.

The countdown is on! ⏳ Just 3 days until Baidu World 2026. Catch the livestream on X or YouTube ↓

Introducing ERNIE Evolve, our automated research system for improving AI training and inference. ⚙️ Given a clear research goal, it selects research directions, modifies code and runs experiments. Only improvements that pass independent evaluation and review are kept, and each round’s findings guide the next. From public benchmarks to ERNIE 5.5 Flash inference tests, here’s what it achieved: - SOTA on NanoChat AutoResearch and NanoGPT Speedrun - 33.7% higher deterministic GPU inference throughput - 14.9% faster single-request decoding on GPUs This is a practical step toward recursive self-improvement (RSI). Next, we plan to extend this approach to training systems, model capabilities and evaluation.

Baidu World 2026 is almost here! On Nov 12, our CEO Robin Li and other speakers will take the stage to explore how businesses can evolve with AI. They'll connect advances in technology with practical applications, sharing their perspectives on how organizations can adapt, innovate, and pursue new opportunities. Tune in on X or YouTube 🧵

ERNIE 5.5 Flash Base just dropped! Time to open that project folder. 🚀 Our efficient sparse MoE language model draws on data spanning multiple domains and languages, achieving SOTA on multiple benchmarks among similarly sized base models. Its architecture and training system were designed together for efficient training and inference, with advances in attention mechanisms, expert routing, and training objectives: - 280B total parameters; 14B activated per token - Pre-trained on 33.5T tokens - Up to 1M-token context for fine-tuning and inference - FP8 and BF16 weights The weights come straight from pre-training, with no mid- or post-training. Compare methods from the same checkpoint, measure what each adds, and shape the model around your own tasks.

Apollo World, our world model for physical AI, was unveiled yesterday at Baidu AI Day in Shenzhen! It turns complex interactions on the road and rare driving scenarios into repeatable, controllable simulations for training and testing autonomous driving systems. - AI traffic agents that make their own decisions and react as autonomous vehicles slow down, change lanes, or yield - Simulated camera, LiDAR, and other sensor data generated in real time, helping autonomous vehicles sense and respond to a virtual world that changes with their actions - Complete rides between any two locations in a virtual city, from accepting a ride request to dropping off passengers - A technology stack suited to robotics and other physical AI applications Apollo World is already part of Apollo Go's testing and validation. What we learn from those virtual trips helps us improve how the vehicles drive on real roads.