Zhao Cao

Reinforcement Learning with Verifiable Rewards (RLVR) is a promising approach for enhancing agentic search. However, its performance is often hindered by reward sparsity, whereby agents receive very limited positive feedback despite incurring significant exploration costs. In this paper, we forma...

Information seeking bridges the knowledge gap between a query and its answer. Although LLMs perform well broadly, their ability to close this gap is limited by pretraining and degrades on specialized or up-to-date queries. A common remedy augments LLMs with external knowledge, either by injecting...

Complex reasoning in tool-augmented agent frameworks is inherently long-horizon, causing reasoning traces and transient tool artifacts to accumulate and strain the bounded working context of large language models. Without explicit memory mechanisms, such accumulation disrupts logical continuity a...

Leveraging Multi-modal Large Language Models (MLLMs) to accelerate frontier scientific research is promising, yet how to rigorously evaluate such systems remains unclear. Existing benchmarks mainly focus on single-document understanding, whereas real scientific workflows require integrating evide...