Zhewei Yao

Bridging Databases and Documents: Data-Algorithm Co-Design for Hybrid Question Answering

MidTool: Mid-training Data Synthesis for Agentic Tool Use

Mid-training is increasingly recognized as a critical stage for shaping the capabilities of large language models. Recent work has shown that targeted mid-training can strengthen reasoning-intensive abilities such as math and science, and can also improve agentic capabilities in software-engineering settings. In this w...

Benchmarking Hybrid Deep Research Across Database Querying and Web Search

While autonomous agents have made significant strides in"deep research"by iteratively navigating the open web to synthesize information, real-world problem-solving is rarely confined to a single environment. Complex analytical tasks inherently require agents to weave together evidence from both ambiguous unstructured t...

ArcticSwarm: Deferring Early Consensus in Long-Horizon Multi-Agent Research

The results show that restricting peer reads during evidence gathering and strengthening commitment boundaries before a hypothesis is shared can broaden search and improve long-horizon multi-agent deep research.

Spider 2.0-AIFunc: Extending Real-World Text-to-SQL to AI-Native SQL Workflows

This work introduces Spider 2.0-AIFunc, a benchmark of 465 verified instances across 125 real-world databases covering six types of AI functions on the Snowflake platform, and finds that the strongest proprietary models reach 67-70% execution accuracy while the best open-source model achieves 58.1%, a gap driven primar...

The Optimizer Is the Agent: Reasoning-Driven Search across Prompts, Programs, and ML Workflows

ReASearch is presented, a unified framework for reasoning-driven optimization in which the agent autonomously decides what to evaluate, how to diagnose failures, which edits to make, and when to verify or restart.