docs: update organization profile with IlhaEmbed 37MB and dual-facet clinical architecture

README.md

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+<div align="center">
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 # WeeMed AI
 
-**Building a globally standardized, AI-driven digital health and long-term care platform for aging societies.**
+**Building open-source, edge-native clinical AI and FHIR infrastructure for aging societies.**
+
+Deployed in real clinics, mobile screening stations, and community eldercare sites across Taiwan.
+
+[Live Space Demo](https://huggingface.co/spaces/weemed/ilhaembed-demo) · [IlhaEmbed Model](https://huggingface.co/weemed/IlhaEmbed) · [GitHub](https://github.com/weemed-ai)
+
+---
+
+</div>
+
+## 🇹🇼 Taiwan Sovereign Clinical AI Stack
+
+We engineer hyper-compact, edge-native models designed for the realities of frontline healthcare: constrained hardware, strict air-gapped privacy, localized clinical shorthand, and spoken elderly dialects.
+
+### 🧭 [IlhaEmbed: Edge Biomedical Embedding Engine](https://huggingface.co/weemed/IlhaEmbed)
+**37.28 MB INT8 ONNX · 384-dim · 3.97 ms on Vanilla CPU (250+ notes/sec)**
+
+- **Dual-Faceted Real-World Clinical Adaptation**:
+  - **Spoken Elderly Vernacular (AST / STT)**: Resolves raw spoken complaints transcribed from older adults in Taiwanese (Taigi) into international clinical concepts (e.g., *「阿嬤講伊心臟跳真緊，腳頭烏痛，全身軟巡巡沒力氣」* → `Condition: Generalized Malaise & Palpitations`, *「皮蛇」* → `Herpes Zoster`).
+  - **Healthcare Staff Shorthand & NHI Codes**: Decodes ultra-fast nursing notes and screening acronyms (e.g., *「114年成健已做，糞檢 MIF 未交」* → `DiagnosticReport: Fecal Occult Blood / LOINC 14563-1`, *「排 L-CT」* → `ServiceRequest: Low-Dose Chest CT`).
+- **100% Zero-Shot Intent Routing**: Flawlessly categorizes text across 44 standard clinical and administrative anchors (HL7 FHIR `Condition`, `DiagnosticReport`, `Medication`, `Observation`, `ServiceRequest`, etc.).
+- **MTEB Medical Benchmark**: Achieves **0.4498 MAP**, outperforming models several times its size (including BGE-small) while consuming a fraction of the compute.
+- **Fail-Closed Safety Gate**: Explicitly rejects administrative noise and non-clinical requests without hallucinating false diagnosis codes.
+- **Interactive Console**: Experience real-time inference directly in your browser at [weemed/ilhaembed-demo](https://huggingface.co/spaces/weemed/ilhaembed-demo).
+
+```python
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer("weemed/IlhaEmbed")
 
-FHIR-first. Integrated health data, intelligent decision support, personalized preventive medicine — deployed in real clinics, health-screening centers, and community long-term care sites in Taiwan.
+# 1. Elderly spoken complaint (AST transcription)
+emb1 = model.encode(["阿嬤講伊心臟跳真緊，全身軟巡巡沒力氣"])
 
-> **AI accelerates digitization; it isn't the product.**
+# 2. Nursing shorthand & screening notation
+emb2 = model.encode(["114年成健已做，糞檢 MIF 未交，排 L-CT"])
+```
 
 ---
 
-## 🇹🇼 Taiwan Sovereign Medical AI Stack
-
-We ship production-grade models derived from real-world clinical and health checkup deployment problems in Taiwan, publishing open benchmarks and transparent data provenance.
-
-### 🧠 [IlhaEmbed (v2.0)](https://huggingface.co/weemed/IlhaEmbed)
-**Traditional Chinese Clinical & Medical Terminology Embedding Model (38.5 MB INT8 ONNX / 384-dim)**
-- **Domain-Adapted for Taiwan Clinical Writing**: Reads Taiwanese hospital shorthand ( → 低劑量胸部電腦斷層), checkup note acronyms ( → 傷寒篩檢糞便檢體), clinical slang ( → 帶狀皰疹), and Taigi colloquialisms ( → 中風).
-- **MODA & NAER 13-Set Retrained**: Fully retrained with the Ministry of Digital Affairs (MODA) and National Academy for Educational Research (NAER) 111,386 medical concept taxonomy.
-- **Context-Conditioned Disambiguation**: Dynamically disambiguates pure-Latin acronyms (, , Bun is a fast JavaScript runtime, package manager, bundler, and test runner. (1.3.14+0d9b296af)
-
-Usage: bun <command> [...flags] [...args]
-
-Commands:
-  run       ./my-script.ts       Execute a file with Bun
-            lint                 Run a package.json script
-  test                           Run unit tests with Bun
-  x         vite                 Execute a package binary (CLI), installing if needed (bunx)
-  repl                           Start a REPL session with Bun
-  exec                           Run a shell script directly with Bun
-
-  install                        Install dependencies for a package.json (bun i)
-  add       @evan/duckdb         Add a dependency to package.json (bun a)
-  remove    redux                Remove a dependency from package.json (bun rm)
-  update    @zarfjs/zarf         Update outdated dependencies
-  audit                          Check installed packages for vulnerabilities
-  outdated                       Display latest versions of outdated dependencies
-  link      [<package>]          Register or link a local npm package
-  unlink                         Unregister a local npm package
-  publish                        Publish a package to the npm registry
-  patch <pkg>                    Prepare a package for patching
-  pm <subcommand>                Additional package management utilities
-  info      zod                  Display package metadata from the registry
-  why       tailwindcss          Explain why a package is installed
-
-  build     ./a.ts ./b.jsx       Bundle TypeScript & JavaScript into a single file
-
-  init                           Start an empty Bun project from a built-in template
-  create    vite                 Create a new project from a template (bun c)
-  upgrade                        Upgrade to latest version of Bun.
-  feedback  ./file1 ./file2      Provide feedback to the Bun team.
-
-  <command> --help               Print help text for command.
-
-Learn more about Bun:            https://bun.com/docs
-Join our Discord community:      https://bun.com/discord, ) and polysemous terms () using operational field context.
-- **Pure CPU On-Premise Native**: Zero GPU, zero cloud, zero API keys required. Patient data stays safely inside hospital premises.
-
-### 🎙️ [Breeze-ASR-26-edge](https://huggingface.co/weemed/Breeze-ASR-26-edge) & [Taiwanese-Tailo-ASR](https://huggingface.co/weemed/Taiwanese-Tailo-ASR)
-**Taiwanese Hokkien (台語) + Mandarin Clinical Speech Recognition Stack**
-- Quantized for edge devices in CTranslate2 () and ONNX runtimes.
-- Multi-format output support (, , , ).
-- Derived from MediaTek Research's Breeze-ASR-26 & OpenAI Whisper-large-v3, fine-tuned on SuíSiann 2.0, TAT_MOE, and real meeting/clinic audio.
+### 🎙️ [Breeze-ASR-26-edge](https://huggingface.co/weemed/Breeze-ASR-26-ONNX) & Taiwanese Tailo ASR
+**Bilingual Taiwanese Hokkien (台語) + Mandarin Speech Recognition Stack**
+- Quantized for edge devices in CTranslate2, ONNX, and GGML runtimes.
+- Multi-format phonetic transcription support (Traditional Chinese Hanzi, Taiwanese Romanization / Tâi-lô, and code-switched clinical dialogue).
+- Built upon MediaTek Research's Breeze-ASR-26 & Whisper architectures, fine-tuned on SuíSiann, TAT_MOE, and real-world multi-speaker clinical intake audio.
 
 ---
 
-## Why this matters to us
+## Why Edge-Native Clinical AI?
 
-Taiwan is aging fast, and the people doing the caring — nurses at screening centers, care managers at community sites, elders themselves — mostly do not speak to each other in written Mandarin. They speak **Taigi**, in noisy rooms, on tablets held in one hand.
+Taiwan is aging at one of the fastest rates in the world. The people providing care — community health workers, outreach nurses, and geriatric case managers — interact with seniors who speak **Taigi**, working in noisy community centers with handheld tablets or legacy PC kiosks.
 
-Health tech that only understands clean written Mandarin does not meet them where they are. So we work on the unglamorous end of clinical AI: the languages actually spoken, the devices actually held, the constraints actually present.
+1. **Patient Privacy & Air-Gap Compliance**: Clinical notes and patient complaints cannot be routed through commercial cloud APIs. Everything must run on-premise.
+2. **Vanilla Hardware Realities**: Ward carts and rural health stations rarely have high-end GPUs. A model that requires 24GB of VRAM cannot help a rural nurse; a 37MB model running at 3.97ms on an older Intel CPU can.
+3. **Honest Scientific Boundaries**: Vector embeddings excel at semantic concept clustering, but are naturally insensitive to temporal sequencing ("completed" vs. "pending follow-up"). We position our models as **calibrated, fail-closed edge sidecars**, leaving final temporal logic to deterministic business rules and clinical professionals.
 
 ---
 
-## How we publish
+## Open Science & Provenance
 
-- **Honest benchmarks.** We report the numbers that survive scrutiny, including when they challenge our own hypotheses.
-- **Documented Provenance.** Open weights and base models are permissively licensed (Apache-2.0). Upstream open government datasets are cataloged transparently.
-- **Real audio, real clinics.** Benchmarks measured on real multi-speaker meeting and clinical workflows, not synthetic noise.
+- **Permissive Open Source**: Model weights, inference scripts, and web demonstrations are published under Apache-2.0.
+- **Transparent Data Lineage**: Grounded in open public health taxonomies (MODA, NAER, LOINC, SNOMED CT, RxNorm) with explicit attribution.
+- **Field-Verified Benchmarks**: Evaluated on genuine clinical notes, ASR transcripts, and real multi-speaker recordings—not synthetic noise.
 
 ---
 
-<sub>Apache-2.0 unless stated otherwise · Made in Yunlin, Taiwan · WeeMed AI</sub>
+<div align="center">
+  <sub>Apache-2.0 License · Crafted in Yunlin & Chiayi, Taiwan · <b>WeeMed AI</b></sub>
+</div>