Audience Response Cortex Studio
A self-optimizing campaign studio that predicts how audiences will react before you publish — combining neural response prediction, multi-agent social simulation, and LLM-driven iterative optimization.
A.R.C Studio closes the gap between "we think this will work" and "here's what the data says." You paste in a piece of content — a product launch announcement, a public health PSA, a policy draft, a marketing campaign — select your target audience, and the system takes it from there.
First, it generates multiple content variants using Claude Haiku, each exploring a different persuasion strategy. Then each variant runs through two independent evaluation systems: TRIBE v2, Meta FAIR's brain-encoding model, predicts neural responses across seven dimensions (attention, emotion, memory, reward, threat, cognitive load, social relevance) by simulating how a human brain would process the content. MiroFish, a multi-agent social simulation engine, spawns 20-40 AI agents with demographically-tuned personas and watches how they share, discuss, react to, and push back against each variant across simulated social platforms.
Claude Opus then analyzes the results from both systems together — identifying why certain neural activation patterns led to specific social outcomes — and feeds that analysis back into variant generation for the next iteration. The loop continues until quality thresholds are met or improvement plateaus. The end result: optimized content, a ranked comparison of approaches, and a layered report explaining what works, what doesn't, and why.
This is the first integration of three specific technologies into a single feedback loop: Meta FAIR's TRIBE v2 (released March 2026, a brain-encoding model that predicts fMRI-level neural responses from text), MiroFish-Offline (a local fork of the multi-agent social simulation engine built on CAMEL-AI's OASIS framework), and Claude Opus as the orchestrating reasoning layer. The cognitive-social bridge — using neural prediction to inform agent personas and using simulation outcomes to refine neural targeting — doesn't exist anywhere else. Each system alone is useful; the cross-system reasoning between them is what produces insights neither could generate independently.
This entire system — real TRIBE v2 brain-encoding inference, 20-agent MiroFish social simulations, Claude Opus cross-system analysis — was built and validated on a single machine: Personal Laptop with RTX 5070 Ti (12 GB VRAM) GPU.
Real performance on that hardware: 85-minute campaigns (2 iterations, 20 agents, 3 content variants per iteration). TRIBE v2 inference takes 5–40 minutes per variant depending on text length. Not fast. Real.
Audio campaigns complete in ~9 minutes end-to-end — 4x faster than text — because TRIBE v2's Wav2Vec-BERT audio encoder uses ~2GB VRAM vs LLaMA 3.2-3B's ~6GB for text.
This should be an invitation, not an apology. If you have a desktop GPU with 16–24 GB VRAM, campaigns will be significantly faster. If you have cloud access to an A100, TRIBE v2 inference drops to seconds. The architecture is identical — only the hardware constraint changes. Built solo in ~2 weeks using Claude Code.
All 5 demo scenarios ran end-to-end through the full pipeline under the Phase 2 B.1 Option A defaults (150-word variants, 2 variants per iteration, 2 iterations, 20 MiroFish agents). The system produces real neural scores (TRIBE v2 brain-encoding model with LLaMA 3.2-3B embeddings on GPU) and real social simulation data (MiroFish multi-agent simulation with Claude Haiku agents).
All 5 scenarios now validate with 0/20 pseudo-score fallbacks after Phase 2 scope calibration (150-word variants, 2 per iteration).
The feedback loop demonstrably improves content across iterations:
| Scenario | Demographic | Duration | Pseudo | Iter 1 Avg | Iter 2 Avg | Change | MiroFish Shares |
|---|---|---|---|---|---|---|---|
| Product Launch | Tech Professionals | 36.3 min | 0/4 | 74.8 | 69.2 | -5.5 | 6 -> 14 |
| Gen Z Marketing | Gen Z Digital Natives | 35.1 min | 0/4 | 31.0 | 67.2 | +36.3 | 20 -> 16 |
| Policy Announcement | Policy-Aware Public | 51.4 min¹ | 0/4 | 79.7 | 85.7 | +6.0 | 22 -> 30 |
| Price Increase | Enterprise Decision-Makers | 37.9 min | 0/4 | 77.3 | 82.9 | +5.6 | 14 -> 6 |
| Public Health PSA | General Consumer | 46.7 min¹ | 0/4 | 37.6 | 73.4 | +35.8 | 18 -> 20 |
4/5 scenarios improved composite attention across iterations; 0/20 variants fell back to pseudo-scores. Product Launch shows the nuance of real neural prediction: composite scores decreased (the optimizer explored a more technical framing) while MiroFish shares more than doubled (6 → 14) — a neural-social divergence that Claude Opus flagged as "high threat_detection activation suppressing attention but increasing peer discussion" among CTOs.
¹ Durations include Anthropic Opus HTTP 429 backoff (~7-15 min per cascade); pipeline compute alone stayed in the 30-35 min budget across all scenarios.
All 5 scenarios ran with full GPU-accelerated TRIBE v2 inference (LLaMA 3.2-3B brain-encoding on RTX 5070 Ti). Real TRIBE scores produce meaningfully different results from the text-feature pseudo-scorer fallback:
| Metric | Pseudo-Scorer | Real TRIBE v2 (5 scenarios, 20 variants) |
|---|---|---|
| Attention score range | Narrow (~65-75) | Wide (12.3 - 86.1) — 73.8-point spread |
| Per-variant differentiation | Low | High — within-scenario ranges of 4 to 68 points |
| Cross-iteration sensitivity | Deterministic (no change) | Responsive to content changes |
| Neural dimension correlation | Artificial | Reflects actual brain-region activation patterns |
For example, in the Gen Z Marketing scenario, iter 1 variant v2 scored 12.3 attention while iter 2 variant v2 hit 80.7 — a 68-point gap on the same seed content, driven entirely by the cross-system feedback loop recommending simpler language and authentic peer-proof framing.
The improvement mechanism works through cross-system feedback:
- TRIBE v2 scores each variant on 7 neural dimensions (attention, emotion, memory, reward, threat, cognitive load, social relevance) using brain-encoding predictions
- MiroFish simulates how 20 agents share, discuss, and react to each variant across social platforms
- Claude Opus analyzes both — identifying why certain neural patterns led to specific social outcomes
- Claude Haiku generates improved variants using the analysis as improvement instructions
- Repeat — each iteration has more context about what works
Gen Z Marketing (+36.3 composite improvement):
- Iter 1 averaged 31.0 — one variant scored 12.3 attention, crushed by technical framing
- Cross-system analysis identified that high cognitive load suppressed sharing among digital natives and that authenticity outperformed polished marketing language
- Iter 2 variants used peer-specific proof and simpler emotional hooks, driving the composite to 67.2 (best variant: 81.1)
Public Health PSA (+35.8 composite improvement):
- Iter 1 averaged 37.6 — clinical-trial-statistics framing scored only 36.3 attention
- Cross-system analysis identified that statistics triggered cognitive load in non-scientific audiences, while community-protection framing activated social relevance
- Iter 2's "urgent community action" variant pushed attention to 82.8 by wrapping prosocial messaging inside concrete narrative structure
5/5 scenarios (100%) produced analysis that references both TRIBE neural scores AND MiroFish social metrics. This validates the core hypothesis — the combined lens produces insights neither system alone could generate.
Key cross-system discoveries:
- Neural-social divergence: High attention doesn't always predict high sharing. Product Launch variants with 81.3 attention generated fewer shares (6) than iter 2's 59.5-attention variant (14 shares) — because CTOs discuss threats rather than share polished announcements.
- Demographic-specific thresholds: Enterprise decision-makers converge around 77-85 attention (high baseline engagement); Gen Z audiences span 12-81 (punished for cognitive load). Same content scores very differently across audiences.
- Coalition formation in policy content: Policy Announcement generated the highest MiroFish shares (30 in iter 2) with iter 2's coalition-framing variant — Claude Opus linked this to 86.1 attention + lowered threat_detection through partisan accountability framing.
- Enterprise privacy pattern: Price Increase composite improved +5.6 while MiroFish shares dropped 14 → 6. Enterprise decision-makers engaged more privately but shared less publicly — executives don't broadcast vendor pricing conversations. A generic share-rate metric would misread this as regression; the cross-system view identifies it as a real behavioral pattern specific to the demographic.
- Emotional-resonance demographics show the steepest gains: Gen Z Marketing (+36.3) and Public Health PSA (+35.8) produced the largest iteration improvements — both target audiences where emotional resonance drives behavior more than rational evaluation. The feedback loop identified and amplified the neural dimensions (emotion, social relevance) that actually move these groups.
- Optimization trajectories diverge by audience: The same loop produces fundamentally different strategies across demographics. Enterprise content optimizes toward private engagement and threat-reward balancing; consumer content optimizes toward emotional resonance and shareability. There is no single "good content" template — the system discovers what works per audience.
Score variance across demographics (iter 2 average): 16.5 points (67.2 Gen Z → 85.7 Policy), threshold: >5.0. With real TRIBE v2 scores, demographic differentiation is significantly more pronounced — policy-aware public audiences produce fundamentally different neural activation patterns than Gen Z digital natives, leading to different optimization trajectories and content strategies.
+---------------------------------------------------------+
| React Dashboard |
| Campaign Form -> Progress (SSE) -> Results (3 tabs) |
+------------------------+--------------------------------+
| REST API
+------------------------v--------------------------------+
| Orchestrator (FastAPI) |
| Campaign CRUD . Variant Generation . Composite Scoring |
| Optimization Loop . Report Generation . SSE Streaming |
| SQLite persistence . Graceful degradation |
+--------------+-----------------+------------------------+
| Claude API | TRIBE v2 Scorer | MiroFish-Offline |
| (Haiku + | (LLaMA 3.2-3B | (Neo4j + Ollama + |
| Opus) | brain encoding) | Claude Haiku agents)|
+--------------+-----------------+------------------------+
| Service | Port | Role |
|---|---|---|
| Orchestrator | 8000 | FastAPI — campaign pipeline, API, SSE |
| TRIBE v2 | 8001 | Neural scoring — text->TTS->WhisperX->LLaMA->brain encoding |
| MiroFish | 5001 | Social simulation — multi-agent with Claude Haiku |
| LiteLLM | 4000 | OpenAI->Anthropic proxy for MiroFish agents |
| Neo4j | 7687 | Knowledge graph for MiroFish |
| Ollama | 11434 | Local embeddings (nomic-embed-text) |
| UI | 5173 | React + Vite dev server |
- Python 3.11+ (TRIBE v2 requires 3.11 specifically due to pyannote.audio)
- Python 3.13+ (orchestrator, system Python)
- Node.js 18+
- Docker Desktop
- NVIDIA GPU (optional — CPU inference works for POC)
- HuggingFace account with LLaMA 3.2-3B access
# 1. Clone with submodules
git clone --recursive https://github.com/AR6420/ARC_Studio.git
cd ARC_Studio
# 2. Configure environment
cp .env.example .env
# Edit .env: set ANTHROPIC_API_KEY, TRIBE_DEVICE=cpu (or cuda)
# 3. Start Docker services (Neo4j, LiteLLM, MiroFish)
docker compose up -d
# 4. Set up TRIBE v2 (Python 3.11 venv)
py -3.11 -m venv tribe_scorer/.venv
tribe_scorer/.venv/Scripts/pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
tribe_scorer/.venv/Scripts/pip install -r tribe_scorer/requirements.txt
tribe_scorer/.venv/Scripts/pip install -e tribe_scorer/vendor/tribev2
tribe_scorer/.venv/Scripts/pip install pyannote.audio whisperx
# 5. Download TRIBE v2 model weights (requires HuggingFace access)
python -c "from huggingface_hub import login; login(token='YOUR_HF_TOKEN')"
python -c "from huggingface_hub import snapshot_download; snapshot_download('facebook/tribev2', local_dir='./models/tribev2', token='YOUR_HF_TOKEN')"
# 6. Install orchestrator dependencies
pip install -r orchestrator/requirements.txt
# 7. Install UI dependencies
cd ui && npm install && cd ..bash scripts/start_all.sh# Terminal 1: TRIBE v2 scorer
bash tribe_scorer/start.sh
# Wait ~5 min for model load + baseline seeding
# Terminal 2: Orchestrator API (run from project root)
python -m uvicorn orchestrator.api:create_app --factory --port 8000
# Terminal 3: UI
cd ui && npm run dev
# Terminal 4: Run a campaign via CLI
python -m orchestrator.cli \
--seed-content "Your content here..." \
--prediction-question "How will the audience react?" \
--demographic tech_professionals \
--max-iterations 2 \
--output results/my_campaign.jsonARC_Studio/
├── orchestrator/ # FastAPI backend (Python)
│ ├── api/ # REST endpoints, SSE, schemas
│ ├── clients/ # HTTP clients for TRIBE + MiroFish
│ ├── engine/ # Variant gen, scoring, analysis, campaign runner
│ ├── prompts/ # Claude prompt templates
│ ├── storage/ # SQLite persistence
│ └── tests/ # 205 tests
├── tribe_scorer/ # TRIBE v2 neural scoring service
│ ├── scoring/ # Model loader, text scorer, ROI extractor, normalizer
│ ├── vendor/tribev2/ # Vendored TRIBE v2 (Git submodule)
│ └── .venv/ # Python 3.11 venv (gitignored)
├── mirofish/ # MiroFish-Offline (Git submodule)
├── ui/ # React 19 + Vite + TypeScript + shadcn/ui
│ └── src/
│ ├── api/ # TypeScript types + API client
│ ├── components/ # Layout, campaign, results, simulation, progress
│ ├── hooks/ # React Query hooks, SSE, reports
│ └── pages/ # CampaignList, NewCampaign, CampaignDetail
├── scenarios/ # 5 JSON demo scenario briefs
├── scripts/ # Validation runner + results checker
├── results/ # Campaign result JSON files
├── models/ # TRIBE v2 weights (gitignored)
└── docker-compose.yml # Neo4j + LiteLLM + MiroFish
7 composite scores blend TRIBE neural dimensions with MiroFish social metrics:
| Score | Formula | What it measures |
|---|---|---|
| Attention | 0.6attention + 0.4emotion | Will people notice this? |
| Virality | (emotion * social) / cognitive * share_rate | Will people share this? |
| Backlash Risk | threat / (reward + social) * counter_narratives | Will this blow up negatively? |
| Memory | memory * emotion * sentiment_stability | Will people remember this? |
| Conversion | reward * attention / threat | Will people take action? |
| Audience Fit | demographic-weighted composite | How well does this match the audience? |
| Polarization | coalitions * platform_divergence * (1 - stability) | Does this unify or divide? |
| Method | Path | Description |
|---|---|---|
| POST | /api/campaigns | Create + optionally start campaign |
| GET | /api/campaigns | List all campaigns |
| GET | /api/campaigns/{id} | Get campaign with iterations |
| DELETE | /api/campaigns/{id} | Delete campaign |
| GET | /api/campaigns/{id}/progress | SSE progress stream |
| GET | /api/campaigns/{id}/report | Get 4-layer report |
| GET | /api/campaigns/{id}/export/json | Download full results JSON |
| GET | /api/campaigns/{id}/export/markdown | Download markdown summary |
| POST | /api/estimate | Pre-run time estimate |
| GET | /api/health | System health check |
| GET | /api/demographics | Available demographic presets |
Phase 1 POC — complete and validated
- 205 tests passing
- Three-phase hardening completed:
- Phase 0 — Silent failures eliminated. Pseudo-score fallbacks are now always flagged with
is_pseudo_score, never invisible. - Phase 1 — Operational stability. Campaigns went from 4 hours to 85 minutes via a ThreadPoolExecutor fix, stale campaign cleanup, and a unified start script.
- Phase 2 — Data integrity.
data_completenessreporting shows exactly which systems contributed real data to each result.
- Phase 0 — Silent failures eliminated. Pseudo-score fallbacks are now always flagged with
- 5/5 demo scenarios run end-to-end with real TRIBE v2 + MiroFish
- Built solo in ~2 weeks using Claude Code
- TRIBE v2 variants limited to 150 words, 2 per iteration to match inference speed on laptop-class hardware (RTX 5070 Ti). The TRIBE v2 pipeline (TTS → WhisperX → LLaMA 3.2-3B word embeddings → brain encoding) takes ~20 min per variant. Faster GPUs or cloud inference (A100) allow larger scope. See
docs/phase2_b1_root_cause_analysis.md. - RTX 5070 Ti (Blackwell, sm_120) requires PyTorch 2.6 + CUDA 12.6. The GPU architecture is newer than what the latest PyTorch nightly supports natively. RTX 30/40 series have zero compatibility issues.
- Single-user only. No concurrent campaign support.
- Claude API credentials from subscription rotate periodically. LiteLLM auto-refreshes but long campaigns may encounter brief interruptions.
- MiroFish simulation quality scales with agent count. 20 agents is the minimum for meaningful social dynamics. 100+ agents produce richer results but proportionally longer runtimes.
- Video input support is implemented but requires a GPU with 24GB+ VRAM for real TRIBE v2 inference (V-JEPA2 ViT-Giant peaks at ~14GB). On the RTX 5070 Ti, video campaigns run with pseudo-score fallback. Text and audio produce real neural scores on laptop hardware.
Phase 1 (current): Text-only optimization, single-user local deployment, working POC with validated feedback loop across 5 demo scenarios.
Phase 2: Full multimodal inputs (audio + video via TRIBE v2's complete trimodal pipeline), expanded demographic personalization, hosted deployment option, and calibration against real-world campaign performance data.
Phase 3: General-purpose simulation platform — extending beyond campaign optimization to any "how will humans respond to X" question.
Contributions are welcome. Whether it's a bug fix, a new demographic profile, improved scoring formulas, or documentation — open an issue or submit a PR.
See CONTRIBUTING.md for development setup, testing, and PR guidelines.
Highest-value contribution areas: TRIBE v2 inference speed optimization, additional demographic presets, MiroFish agent behavior quality, UI/UX improvements, and documentation.
This project is licensed under the GNU Affero General Public License v3.0, matching MiroFish-Offline's license. This means derivative works must also be open-source — protecting the project from being absorbed into closed commercial products without contributing back.
- Meta FAIR for TRIBE v2, the brain-encoding model that makes neural response prediction possible
- nikmcfly and the MiroFish team for MiroFish-Offline, the multi-agent social simulation engine
- CAMEL-AI for the OASIS simulation framework that MiroFish builds on
- Anthropic for Claude, which serves as both the orchestrating reasoning layer and the agent backbone
Adarsh Reddy Balanolla — GitHub