I document my notes and writings on AI research, LLMs, and engineering here. A mix of long-form posts hosted on this site and selected external articles.
A pedagogical tour of how the LLM-agent community turns environments into scalable, verifiable RL training signal — the recurring pipeline, the design axes, and the open challenges.
一篇关于 LLM-agent 社区如何把环境变成可扩展、可验证的 RL 训练信号的教学式导览——反复出现的流水线、设计轴与开放挑战。
Deep Research is about understanding, reasoning, and synthesis—combining adaptive planning, retrieval, analysis, and context engineering to produce long-form, well-cited research outputs. This article explores how Enterprise Deep Research bridges internal knowledge and external insights to serve strategic business goals.
A novel approach to enhancing Large Language Models through synthetic knowledge ingestion, presented at EMNLP 2024 from Intuit AI Research.
Our work on document enhancement using diffusion models, presented at WACV 2024 from Intuit AI Research.
An interactive framework for cost-effective fine-tuning of language models with sparse human supervision, presented at NeurIPS 2023.
Reliable hallucination detection in black-box language models via semantic-aware cross-check consistency (SAC³), accepted by EMNLP 2023.