Undergraduate researcher working on deep research systems and agent memory — how LLM agents gather, verify, and synthesize information at expert level.
I'm Ruizhe Li (李睿哲), an undergraduate at USTC majoring in Data Science. I'm interested in LLM agents, deep research, and agent memory.
arXiv:2601.08536 · 2026
A benchmark of 132 expert-grounded tasks across 22 domains and 9,430 fully-verifiable binary rubrics, scoring deep-research reports on information recall, analysis, and presentation. Built with a four-stage LLM+human pipeline and 400+ expert-hours — even the strongest systems satisfy fewer than 50% of rubrics.
EMNLP 2025 · arXiv:2504.15784
A reference-based method that scores the creativity of large-language-model outputs against human references, offering a more reliable and automatic alternative to costly human creativity ratings.
CIKM 2024 · arXiv:2408.06809
Recasts conversational recommendation as a three-phase offline policy-learning problem, decoupling interaction-policy learning from costly online exploration.
Evaluation suite and leaderboard for deep-research agents, grounded in expert-written reports. Rubric extraction, batched LLM-as-judge scoring, and token accounting.