🧑‍⚖️ For Judges — Review in 5 Steps

Offline multi-agent document council on @qvac/sdk. Zero cloud — verifiable.

1. ▶ Watch the 3-min demo (network off on camera): https://youtu.be/tnVqrbXNMco
2. Run it locally (first launch downloads models ~2 GB):

   npm install
   npm run start          # backend :3001 + web app :5173  (keep running)
   make seed              # 2nd terminal — ingest the dossier (curl -X POST :3001/api/seed)
   # open http://localhost:5173  →  status pill reads "LIVE · QVAC"

3. Ask the demo query: “Who authorized the Entity X payment and was it legitimate?” → watch the Researcher → Skeptic → Synthesizer debate stream in. The Skeptic catches the planted contradiction (VP Chen "authorized" a payment while on PTO); the Synthesizer returns a cited, disputed verdict with lowered confidence.
4. Verify the claims:
   • 🔒 No remote APIs — zero cloud calls: docs/REMOTE_APIS.md
   • 📋 Structured audit log — model loads/unloads + per-inference TTFT / tokens / tokens-per-sec: docs/AUDIT_LOG.md → real run in docs/audit-log.jsonl
   • python3 scripts/verify_offline.py — 0 outbound (disconnect network first) · 18/18 checks
   • npm run bench — real on-device latency + contradiction recall → data/bench_results.json (p50 ≈ 2.1 s, peak RAM ≈ 180 MB, citation coverage 1.0)
   • npm run ci — 163 unit tests, 100% core coverage + lint + typecheck
5. Why only QVAC / no remote APIs (docs/REMOTE_APIS.md): all inference is local (Llama 3.2 1B + GTE-Large via @qvac/sdk). See Why ONLY QVAC? — remove QVAC and you'd need a cloud LLM + hosted vector DB, and the confidentiality premise is gone.

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Quorum 🏛️

Offline multi-agent document council — 3 AI agents debate your documents to a cited answer. The visible disagreement IS the trust mechanism.

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💡 The Problem & Solution

When analyzing confidential documents — legal dossiers, financial audits, HR records — you can't upload them to cloud AI. But a single LLM will just parrot the first document it reads, missing contradictions.

Quorum solves this with a 3-agent council that cross-examines your corpus entirely offline:

Key Features:

• 🔍 Researcher — Retrieves relevant documents, proposes initial answer with citations
• ⚡ Skeptic — Counter-retrieves to challenge claims, finds planted contradictions
• 🧩 Synthesizer — Reconciles viewpoints, assigns HIGH/MEDIUM/LOW confidence
• 📚 Every claim cited — Source chunk mapped to exact document
• 🔴 Contradiction detection — Skeptic catches what a single LLM would miss

🎥 See It In Action

Real local inference — the network is off the entire time (note the ● LIVE · QVAC pill).

The contradiction catch. Ask "Who authorized the Entity X payment and was it legitimate?" — a naive RAG repeats the memo ("VP Chen, March 12"), but Quorum's Skeptic re-queries and surfaces the conflicting HR access logs and board minutes, so the Synthesizer returns a cited, disputed verdict with lowered confidence:

📚 Every claim maps to an exact source chunk	⚖️ A second debate — governance §4.2 compliance

🏗️ Architecture & Tech Stack

flowchart LR
    Q["🗣️ User Query"] --> R["🔍 Researcher"]
    R --> S["⚡ Skeptic"]
    S --> Y["🧩 Synthesizer"]
    Y --> A["📋 Cited Answer"]

    R -.- R1["RAG retrieve\n+ propose"]
    S -.- S1["Counter-retrieve\n+ challenge"]
    Y -.- Y1["Reconcile\n+ confidence"]

    style Q fill:#1e293b,stroke:#06b6d4,color:#f1f5f9
    style R fill:#0e2a30,stroke:#06b6d4,color:#06b6d4
    style S fill:#2a1f0e,stroke:#f59e0b,color:#f59e0b
    style Y fill:#0e2a14,stroke:#22c55e,color:#22c55e
    style A fill:#1e293b,stroke:#06b6d4,color:#f1f5f9
    style R1 fill:none,stroke:#06b6d4,color:#94a3b8,stroke-dasharray:4
    style S1 fill:none,stroke:#f59e0b,color:#94a3b8,stroke-dasharray:4
    style Y1 fill:none,stroke:#22c55e,color:#94a3b8,stroke-dasharray:4

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Layer	Technology
Frontend	Vite 8, React 19, TypeScript
AI Engine	@qvac/sdk (completion, RAG)
Embeddings	GTE-Large-FP16 via @qvac/sdk
LLM	Llama 3.2 1B (local)

🏆 Why ONLY QVAC?

QVAC SDK Method	Quorum Usage	Cloud Alternative You'd Need
loadModel() + completion()	Runs all 3 agents (Researcher, Skeptic, Synthesizer)	OpenAI API ($0.03/query × 3 agents)
ragIngest() + ragSearch()	Embeds & searches private dossier locally	Pinecone + OpenAI Embeddings API
loadModel(GTE_LARGE_FP16)	1024-dim embeddings for citation matching	Cohere Embed API
unloadModel()	Memory lifecycle — load once, 3 agents, unload	N/A (cloud doesn't care)

Take QVAC out and you'd need 3 separate cloud services (OpenAI + Pinecone + Cohere), a network connection, and your confidential documents would leave your machine.

📋 Dossier — Planted Contradictions

The demo includes a 5-document Northwind dossier with deliberate contradictions:

Document	Claims	Contradiction
memo_ref_4821.txt	VP Chen authorized $2.4M payment March 12	Chen was on PTO
board_minutes_march.txt	Chen PTO March 11-15, no Entity X discussion	Memo claims March 12
q1_financial_report.txt	Audit flags: no SOW, no deliverables	Payment processed
hr_access_logs.txt	No badge/VPN access March 12	Memo timestamped March 12
governance_charter.txt	>$1M needs board resolution	No board approval

🚀 Getting Started

git clone https://github.com/edycutjong/quorum.git
cd quorum
npm install
npm run start          # backend (:3001) + web app (:5173) — keep this running (make start)

# then, in a SECOND terminal, ingest the dossier into the running backend:
curl -X POST http://localhost:3001/api/seed        # or: make seed
# open http://localhost:5173 — status pill should read "LIVE · QVAC"

First launch downloads the local models. The status pill reads DEMO · OFFLINE until the backend is reachable; once it's up and seeded it switches to LIVE · QVAC.

Devastating Demo Query: "Who authorized the Entity X payment and was it legitimate?"

📊 Benchmarks

Run npm run bench to reproduce. This runs the real 3-agent council over the dossier via @qvac/sdk and writes data/bench_results.json (latency, contradiction recall, citation coverage). Use npm run bench -- --assert to fail on budget regressions.

Representative run on an Apple M1 Max (32 GB) — reproduce with npm run bench:

Metric	Measured	Budget
Full Council Round (p50 / p95)	~2.5s / ~2.7s	<15,000ms
Model Load (cold)	~1.2s	<10,000ms
Corpus Ingest (5 docs → 9 chunks)	~1.8s	—
Citation coverage	1.0	≥0.95
Contradiction recall (planted set)	0.67–1.0¹	1.0
Peak RAM	~196 MB	<4,096MB

¹ Recall varies run-to-run: the Skeptic reliably retrieves the conflicting documents, but Llama-3.2-1B is non-deterministic and doesn't always phrase an explicit objection — an honest limitation of a 1B model on-device. npm run bench records real measurements from your hardware into data/bench_results.json. (The legacy scripts/bench.py is a deterministic simulation kept only as a CI smoke test.)

🧪 Testing & CI

171 tests · 100% core coverage: 163 unit tests (Vitest) covering RAG citation mapping & chunking, agent orchestration, contradiction-driven confidence, the audit log, and the offline SDK wrappers, plus 8 E2E specs (Playwright) — backed by 18 offline-verification checks (verify_offline.py).

🔍 Verification & Compliance

Everything the judges' verification asks for, as concrete artifacts:

Gate	Where	How to reproduce
No remote APIs — zero cloud calls	docs/REMOTE_APIS.md	python3 scripts/verify_offline.py (scans for banned cloud SDKs; 18/18)
Structured audit log — model loads/unloads + inference perf (prompt, tokens, TTFT, tokens/sec)	docs/AUDIT_LOG.md → docs/audit-log.jsonl	on by default; npm run start + a query writes it
Offline proof — 0 outbound connections	scripts/verify_offline.py	disconnect network, then run
Real on-device benchmarks — latency, recall, RAM	data/bench_results.json	npm run bench

7-stage pipeline: Quality → Security → Build → E2E → Performance → Offline → Deploy

# ── Code Quality ────────────────────────────
npm run lint           # ESLint
npm run typecheck      # TypeScript check
npm run ci             # Full quality gate

# ── E2E & Performance ──────────────────────
npm run e2e            # Playwright E2E (3 suites)
npm run lighthouse     # Lighthouse CI audit

# ── Evidence Bundle ─────────────────────────
python3 scripts/verify_offline.py     # airgapped run — disconnect network first
npm run bench                         # real council latency + contradiction recall
python3 scripts/check_submission_readiness.py

Layer	Tool	Status
Code Quality	ESLint + TypeScript	✅
E2E Testing	Playwright (3 suites)	✅
Security (SAST)	CodeQL	✅
Security (SCA)	Dependabot + npm audit	✅
Secret Scanning	TruffleHog	✅
Performance	Lighthouse CI	✅
Offline Verification	verify_offline.py (18/18)	✅

📁 Project Structure

quorum/
├── docs/                   # README assets
├── data/fixtures/
│   └── northwind_dossier/  # 5 docs with planted contradictions
├── e2e/                    # Playwright E2E tests
├── scripts/                # seed, bench, verify, readiness
├── src/
│   ├── core/
│   │   ├── qvac.ts         # @qvac/sdk wrapper
│   │   ├── rag.ts          # Corpus RAG pipeline
│   │   └── council.ts      # 3-agent council orchestration
│   ├── App.tsx             # Debate transcript viewer
│   └── App.css             # Dark mode theme
├── .github/                # CI/CD + CodeQL + Dependabot
├── playwright.config.ts
├── lighthouserc.json
└── README.md

⚠️ Honest Limitations

1. Small model — limited reasoning depth vs cloud LLMs
2. Sequential agents — no true parallel debate
3. English only
4. Fixed dossier — no live document upload yet
5. Mock inference in demo mode

📄 License

MIT © 2026 Edy Cu

🙏 Acknowledgments

Built for QVAC Hackathon I — Unleash Edge AI (DoraHacks). Thank you to the QVAC team for making multi-agent AI possible on the edge.