From industry to academia — building AI that works.
MS in Artificial Intelligence from Hawaii Pacific University (2026). A decade in tech. Pivoted to AI in 2021. Now formalizing production experience through academic research.
About Tao An (安涛)
Founder of FIM Labs Pte Ltd (🇸🇬 Singapore · 🇨🇳 Beijing), building FIM One — an open-source AI connector hub that links agents to enterprise systems (Feishu, Slack, Teams & more). Also serving government and enterprise clients in China, with production AI systems focused on legal and healthcare domains.
Research interests: Retrieval-Augmented Generation, LLM memory architectures, knowledge graphs.
Featured Project
FIM One
LLM-powered agent runtime that bridges disconnected enterprise systems — ERP, CRM, OA, finance, databases — without modifying existing infrastructure. Features intelligent DAG planning, ReAct reasoning, full RAG pipeline, visual workflow editor, and MCP protocol support.
View on GitHub →Publications
CogCanvas: Verbatim-Grounded Artifact Extraction for Long LLM Conversations
A training-free framework that extracts verbatim-grounded cognitive artifacts from LLM conversations and organizes them into a queryable graph. Achieves 32.4% accuracy on LoCoMo (+7.8pp vs RAG) with decisive advantages on temporal reasoning (+20.6pp) and multi-hop questions.
AI as Equalizer or Amplifier? Task Complexity as the Moderating Factor for Human Expertise in Hybrid Intelligence Systems
Accepted at the 5th International Conference on Hybrid Human-Artificial Intelligence (HHAI 2026, Brussels); proceedings to appear in IOS Press Frontiers in Artificial Intelligence and Applications (in press). Drawing on structured field observations of colleagues at a single organization since mid-2024, this position paper reconciles the “AI as equalizer” and “AI as amplifier” debates: AI narrows novice–expert gaps on routine, well-structured tasks, but amplifies them on complex tasks requiring deep judgment. Argues that domain expertise—not prompt engineering—determines who benefits most from AI.
Cognitive Workspace: Active Memory Management for LLMs
Proposes Cognitive Workspace, a paradigm transcending traditional RAG by emulating human cognitive mechanisms. Features active memory management, hierarchical cognitive buffers, and task-driven context optimization. Achieves 58.6% memory reuse rate (vs 0% for RAG) with 17-18% net efficiency gain.