README.md

metadata

title: Cognitive Load Manager
emoji: 🧠
colorFrom: yellow
colorTo: red
sdk: docker
app_file: server/app.py
pinned: false
tags:
  - openenv
  - rl
  - scheduling
  - agent-eval
  - productivity
  - multi-agent
  - grpo
  - reinforcement-learning

🧠 Cognitive Load Manager

An AI that schedules work like a good manager — one that actually cares if you're tired.

🎥 See It In 2 Minutes

🎬 Project walkthrough	👉 Watch on Loom
📊 Live dashboard demo	👉 Watch the demo

🤔 The Problem

Most productivity tools tell you what to do. None of them care how you're feeling while doing it.

• Running on 4 hours of sleep? Doesn't matter.
• Just finished three back-to-back meetings? Doesn't matter.
• Operating at 40% because the last task drained you? Doesn't matter.

Real performance isn't a straight line. Fatigue piles up. Stress carries over. Switching between tasks costs you more than you think.

We built an AI that learns to notice all of that — and schedule around it.

✨ What Makes It Special

This is the moment that made the whole project worth it:

The AI started giving workers breaks before they burned out — not after.

Nobody told it to do that. It figured it out on its own.

That's the difference between a scheduler that optimizes hours and a manager that actually understands people.

🛠️ How It Works (In Plain English)

Imagine a simulated office with:

• 👥 Three workers — each with their own energy, stress, and fatigue
• 🧑‍💼 One manager (the AI) — deciding who does what, and when to call a break
• 📋 A pile of tasks — emails, code reviews, reports, meetings, with real deadlines

The AI plays the manager role. Push too hard, workers burn out and quality crashes. Push too soft, deadlines slip. The AI has to find the sweet spot — and keep finding it as the day changes.

And the day does change. Mid-shift, a "Production server down!" alert can fire and suddenly every code review is critical. The AI has to adapt on the fly.

🗺️ How The Pieces Fit Together

flowchart TB
    AI["🧠 <b>AI Manager</b><br/><i>Qwen 1.5B</i><br/>decides who does what"]

    subgraph SIM["🏢 Simulated Workday"]
        direction LR
        W1["👤 <b>Worker 1</b><br/>energy · stress · fatigue"]
        W2["👤 <b>Worker 2</b><br/>energy · stress · fatigue"]
        W3["👤 <b>Worker 3</b><br/>energy · stress · fatigue"]
        TP["📋 <b>Task Pool</b><br/>emails · reviews<br/>reports · meetings"]
        EV["⚡ <b>Live Events</b><br/>deadline shifts<br/>urgent interrupts"]
    end

    DASH["📊 <b>Live Dashboard</b><br/>watch it think<br/>in real time"]

    TR["🎯 <b>GRPO Training</b><br/><i>Hugging Face TRL</i><br/>1000 steps · +163% lift"]

    AI -- "assigns · focuses<br/>breaks · delays" --> SIM
    SIM -- "observation +<br/>reward signal" --> AI
    SIM -- "live state" --> DASH
    AI -. "rollouts" .-> TR
    TR -. "smarter weights" .-> AI

    classDef ai fill:#9b87f5,stroke:#5b3fc4,stroke-width:3px,color:#fff
    classDef worker fill:#dbeafe,stroke:#3b82f6,stroke-width:2px,color:#1e3a8a
    classDef task fill:#fce7f3,stroke:#ec4899,stroke-width:2px,color:#831843
    classDef event fill:#fee2e2,stroke:#ef4444,stroke-width:2px,color:#7f1d1d
    classDef train fill:#d1fae5,stroke:#10b981,stroke-width:2px,color:#064e3b
    classDef dash fill:#e0e7ff,stroke:#6366f1,stroke-width:2px,color:#312e81
    classDef sim fill:#fef9c3,stroke:#eab308,stroke-width:2px,color:#713f12

    class AI ai
    class W1,W2,W3 worker
    class TP task
    class EV event
    class TR train
    class DASH dash
    class SIM sim

The loop in plain English:

1. 🧠 The AI looks at the workday — who's tired, what's due, what just blew up.
2. 🎯 It makes a call — assign, focus, break, switch, or wait.
3. 🏢 The simulated office reacts — workers gain progress or burn out, deadlines pass.
4. ↩️ A reward comes back — high if the call was wise, low if it wasn't.
5. 🔁 GRPO uses those rewards to nudge the AI toward better decisions next time.

After 1000 loops, the AI is 5× better than random guessing.

📈 The Results

After training the AI for 1000 steps:

	Score	What it means
🎲 Random guessing	~0.05	Total chaos
🤖 Untrained AI	0.101	Mediocre
✅ Our trained AI	0.265	5× better than random — +163% lift

What it learned without being told:

• ⏸️ Insert breaks before burnout, not after
• 🎯 Protect deep-focus time — don't yank workers off mid-task
• 🚨 Adapt instantly when priorities flip mid-day

👉 Watch the full dashboard demo

🔭 Why This Matters

Today, AI tools schedule meetings and triage tickets — but they treat people like robots. CLM is a step toward AI that schedules for humans, not over them.

The same idea plugs into:

• 📅 Work tools — Slack, Linear, Notion that understand worker capacity
• 🎓 Education — tutors that notice when a student is overloaded, not just behind
• 🏥 Healthcare — staff schedulers that catch fatigue before it becomes errors

🚀 Try It

📓 Re-run our training in your browser	👉 Open in Colab
🤗 Live environment	This Hugging Face Space
📝 The full build story	blog.md

🛠️ For Developers — Technical Details

Stack

• Environment: OpenEnv-compatible RL environment (FastAPI backend, Docker)
• Training: Hugging Face TRL with GRPO on Qwen 1.5B
• Frontend: React live dashboard
• Difficulty levels: easy, medium, hard, expert (with deadlines, dependency chains, mid-episode interruptions)

Actions

Action	Description
work	Work on a task at normal pace
focus	Deep-work mode: 2× progress, 2× energy cost
break	Rest: +energy, −stress
switch	Change active task (small penalty)
delay	Wait one step

Scoring Formula

score = completion×0.60 + deadline×0.22 + energy×0.10 + dependency×0.05 + interruption×0.03

Score is always in (0.01, 0.99).

Quick Setup

# Docker
docker build -t clm-env . && docker run -p 7860:7860 clm-env

# Local
pip install -r requirements.txt
uvicorn server.app:app --port 7860 --reload

# React dashboard
cd frontend && npm install && npm run dev

Environment Variables

Variable	Description
API_BASE_URL	LLM API endpoint
MODEL_NAME	Model identifier
HF_TOKEN	Hugging Face API token

Project Structure

cognitive-load-manager/
├── models.py          ← Core environment
├── inference.py       ← Baseline LLM agent
├── openenv.yaml       ← OpenEnv spec
├── backend/main.py    ← FastAPI server
├── grader/            ← Difficulty graders
└── frontend/          ← React dashboard

For the full technical write-up — observation space, reward shaping table, training loop, and the v1→v2→v3 reward-tuning story — see blog.md.

Built for the OpenEnv Hackathon, April 2026.
🧠 Scheduling that respects the humans doing the work.