Jiangwei Zhang

Jiangwei Zhang, Wen Sun, Chong Wang et al.

Artificial intelligence increasingly drives automated scientific discovery, yet contemporary generalist agents lack physical grounding, frequently hallucinating hardware-incompatible designs. Here, we present a physically grounded, multi-agent discovery engine that autonomously architects hardware-compliant computing systems. Anchored by an Evolutionary Knowledge Graph structuring past scientific innovations, the framework extracts an "algorithmic Chain-of-Thought" to transform blind stochastic search into directed structural evolution. Applied to the extreme testbed of foundation model deployment, the engine evolved two hardware-aware compression methodologies surpassing human-engineered heuristics: Q-Enhance mitigates long-context accuracy loss in dense models, and MoE-Salient-AQ outperforms state-of-the-art manual sparse Mixture-of-Experts designs by 3.7% at sub-3-bit regimes. Utilizing a bandwidth-efficient Sensitivity Profile, we successfully deployed a massive 235-billion-parameter model onto a constrained dual-A100 server, reducing memory requirements by 75% with a marginal 0.64% accuracy degradation. By transforming unconstrained combinatorial search into knowledge-driven autonomy, this establishes a scalable hardware-software co-design paradigm for machine-driven discovery within strict physical boundaries.

Jiangwei Zhang, Li Zhang, Vigneshwaran Raveendran et al.

This paper describes the hotel price aggregation system - PriceAggregator, deployed at Agoda, a global online travel agency for hotels, vacation rentals, flights and airport transfer. Agoda aggregates non-direct suppliers' hotel rooms to ensure that Agoda's customers always have the widest selection of hotels, room types and packages. As of today, Agoda aggregates millions of hotels. The major challenge is that each supplier only allows Agoda to fetch for the hotel price with a limited amount of Queries Per Second (QPS). Due to the sheer volume of Agoda's user search traffic, this limited amount of QPS is never enough to cover all user searches. Inevitably, many user searches have to be ignored. Hence, booking lost. To overcome the challenge, we built PriceAggregator. PriceAggregator intelligently determines when, how and what to send to the suppliers to fetch for price. In this paper, we not only prove PriceAggregator is optimal theoretically but also demonstrate that PriceAggregator performs well in practice. PriceAggregator has been deployed in Agoda. Extensive online A/B experimentation have shown that PriceAggregator increases Agoda's bookings significantly.