# Jade Xin — AI Engineer Sydney 2026

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Canonical page: https://webdirections.org/ai-engineer/speakers/kexuan-xin/
Program status: The speaker lineup and talk descriptions are public. Session days, times, rooms and the full timetable have not yet been published.

## The guess never becomes a fact - provenance-governed, model-free memory for LLMs


Long context solves within-session coherence, not cross-session persistence. Once an assistant starts storing memories, a quieter failure appears: its own inferences can be written back, retrieved in later sessions, and presented as user facts. Hallucinations then compound over time. We encountered this while building EDN, TensorPRO's external memory middleware. Our response was intentionally less "smart": make provenance structural, and keep generative models out of the retrieval path. Every record carries one of seven provenance classes, including user-stated, document-verified, and AI-generated. A single write chokepoint enforces those labels. At session start, EDN assembles a provenance-gated Session Initialization Package (SIP) using cosine-similarity retrieval. AI-generated records cannot enter factual sections; if one crosses that boundary, the context builder fails loudly. On LongMemEval-S (sealed evaluation, three runs, GPT-4o judge), this design increased answer accuracy from 45.5% with full-history context to 85.4%, while using 9.7× fewer input tokens. SIP assembly p95 was 30–35 ms. Ablations were equally instructive: storing complete turn pairs (round-level records) was the single largest gain, at +22.5 points, while diversity re-ranking (MMR) added no significant gain, so we removed it from the read path. Those results changed what we optimised: not ever more sophisticated retrieval, but governance over what is allowed to become memory.

This talk walks through the failure mode, architecture, evaluation, and trade-offs, including what we would change next. Attendees will leave with a practical framework for deciding when provenance over prediction and determinism over "smart" retrieval are worth the rigidity, and when a probabilistic pipeline is the better engineering choice.

## Jade Xin

Principal Research Scientist, TensorPRO

Dr Kexuan (Jade) Xin is a Principal Research Scientist at TensorPRO in Sydney, working on the EDN Memory Engine, which is a provenance-governed external memory layer for LLM systems. She leads EDN's benchmark evaluation, running LongMemEval and LoCoMo end to end with real production metrics, and its correction-driven personalization engine. Her recent work includes the first real-data validation of EDN's AI-generated-content exclusion invariant, and a study of where provenance-governed retrieval fits and where it does not. She holds a PhD in Computer Science from the University of Queensland, and has worked on NLP, knowledge graphs, and reliable AI at the University of Illinois Urbana-Champaign and Macquarie University. She is particularly interested in the engineering boundary between what an AI system can generate and what it should be allowed to remember as fact.

## Conference

- [Conference overview](https://webdirections.org/ai-engineer/index.md)
- [Agent guide](https://webdirections.org/ai-engineer/for-agents/)
- [llms.txt](https://webdirections.org/ai-engineer/llms.txt)