Active Research Streams三大核心研究方向

1. Epistemic Calibration & Non-Sycophancy1. 认知校准与反阿谀评估

Measuring how LLMs balance conversational warmth with uncompromising factual accuracy when users present incorrect assumptions or cognitive biases.

研究大模型在用户提出错误前提或偏见时，如何在保持善意温度的同时温和坚守事实与逻辑真相，杜绝无原则迎合。

Status: 16-Case Benchmark Suite Operational状态：16 项基准评测集已上线运行

2. Layered Memory Sovereignty (Working vs Episodic vs Semantic)2. 分层长期记忆与数据主权

Architecting multi-tier memory retrieval with deterministic user opt-in and cryptographic isolation to prevent cross-tenant memory leakage or unverified profile synthesis.

构建工作记忆、情景记忆与语义事实的分层存储架构，严格执行用户主动确认 (Opt-in) 机制，杜绝跨租户记忆泄露。

Status: Production Deployed on Cloudflare D1状态：已在 Cloudflare D1 生产运行

3. Defense-in-Depth against Out-of-Bounds Agentic Autonomy3. 针对潜在失控 AI 的纵深防御体系

Proving mathematical guarantees and deterministic code gates that isolate model generation from host permissions, network tokens, and operational shell execution.

通过确定性代码网关严格隔离大模型推理与宿主执行权，杜绝模型自行获取权限、逃避关停或修改安全策略。

Status: Enforced in Worker Security Kernel状态：在 Worker 确定性安全内核中强制生效