Final-year Mechanical Engineering student at National Cheng Kung University, moving into research on trustworthy AI agents for finance — making AI-generated strategies and analysis reproducible, verifiable, and auditable.

The question I keep coming back to: when an AI can generate code, generate analysis, and even operate a system directly, how do we confirm its output is trustworthy? In finance this bites hard — a backtest that overstates performance, or an analysis that quietly invents a number, costs real money.

I work agent-first: I design the systems, specify the validation that decides accept-or-reject, and judge the results; AI coding agents (Claude Code / Codex) do the implementation. The part I care about — and want to research — is the checking layer.

flowchart LR
    A["AI agent<br/>strategy · analysis · actions"] --> V{"Verification layer<br/>gates · provenance · holdout"}
    V -->|passes| OK["Trustworthy output"]
    V -->|can't prove| STOP["Reject / abstain"]

Trustworthy, auditable AI agents in quantitative finance, along two lines I'm prototyping:

• Strategy trustworthiness — can overfitting be caught reliably? Validation gates (trial registration · PBO · DSR · single-use holdout · no-look-ahead contract) against a naïve-backtest baseline.
• Analysis faithfulness — a retrieval finance agent with a number-provenance verifier: every figure must trace to a source, or the agent abstains. Evaluated against public benchmarks (FinanceBench, DocFinQA, FinBen, Finance Agent Benchmark).

Research prototypes for the question above — built to be evaluated, not shipped:

• crypto-quant-signal — spot, long-only, public-data daily signals behind six validation gates, with a paper-trading scoreboard. Paper-only; never touches a live account.
• trialgate — that validation gate extracted into a zero-dependency package on PyPI (trial registry · CSCV/PBO · DSR · single-use holdout): the working prototype of the “strategy trustworthiness” line above. It has already rejected one of my own strategies on a locked holdout.
• legal-agent — retrieval-first Taiwan legal assistant: five anti-hallucination gates + a time-sliced statute store that cites the law in force at the event's date. My deepest system-design work, and the seed of this research direction.
• otto — a financial terminal an AI operates end-to-end over MCP, with hard paper/live safety isolation — a testbed for auditable agent-operated systems. v1.0.0 ships a 20-task agent-operability benchmark graded programmatically (state assertions, artifacts, refusal-with-state-unchanged — no LLM judge): claude-sonnet-5 20/20, claude-haiku-4-5 19/20.
• report-workflow · OpenRead — deterministic source-to-DOCX reporting with traceable provenance; a bring-your-own-key web/PDF translation extension.

Self-taught career-changer (mechanical engineering → AI / quant finance). Strong at decomposing problems, prototyping fast with AI tooling, and validation / risk thinking; seeking graduate research training to deepen the statistics and evaluation methodology behind all of it. 中文（母語）· English (C1) · 日本語（基礎）.