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KeithXD/
Team_Sparks

Name: Team_Sparks
Availability: InStock

openenv

ledger-repair

reinforcement-learning

dependency-propagation

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AuditRepairEnv++ — Cost-Constrained Iterative Ledger Repair

OpenEnv Environment | RL for Financial Ledger Auditing

An RL environment where an AI agent must repair inconsistencies in a financial ledger. Errors are interdependent — fixing one entry may introduce new errors in dependent entries. The agent must maximize ledger correctness while minimizing cost and avoiding overcorrection, all under a limited budget.

Problem Description

A financial ledger contains entries where value ≠ expected_value (errors). These errors are interconnected through a hidden dependency graph — fixing one entry can cascade changes to the expected_value of dependent entries, potentially creating new errors.

The agent has a limited action budget and must strategically choose which entries to fix and in what order to:

Maximize consistency — fix as many errors as possible
Minimize cost — use the fewest actions possible
Avoid overcorrection — don't fix entries that are already correct

Solution Approach

AuditRepairEnv++ addresses this challenge by:

Modeling Real Dependencies — Entries are linked through a dependency DAG, simulating cascading effects in real ledgers
Cost-Constrained Optimization — Agents must repair ledgers within a limited budget, forcing strategic decisions
Multi-Objective Scoring — Balances correctness, efficiency, and overcorrection penalties
Scalable Difficulty — Three task levels (easy/medium/hard) with increasing complexity
OpenEnv-Compatible API — Standard HTTP endpoints for seamless integration with any LLM agent

This environment tests an LLM agent's ability to:

Parse complex structured state (ledger + dependencies)
Reason about side effects (dependency propagation)
Plan multi-step actions under uncertainty
Handle budget constraints and trade-offs

RL Reasoning

This environment tests multi-step decision making under uncertainty:

State: The current ledger, errors, remaining budget, and step count
Actions: FIX_ENTRY, ADJUST_ENTRY, REVERT_ENTRY, NO_OP
Transitions: Non-trivial due to dependency propagation
Reward: Composite score based on consistency, efficiency, budget usage, and overcorrection penalties

The key challenge is that actions have side effects (dependency propagation), requiring the agent to plan ahead and reason about cascading consequences.

Action Space

Action	Description	Cost
`FIX_ENTRY <id>`	Sets `value = expected_value` for the entry. Triggers dependency updates.	1
`ADJUST_ENTRY <id> <delta>`	Increments/decrements the entry's value by delta.	1
`REVERT_ENTRY <id>`	Undoes the last change to an entry.	1
`NO_OP`	Does nothing. No budget cost.	0

Action Model (Pydantic)

python

class AuditAction(BaseModel):
    action_type: str   # FIX_ENTRY | ADJUST_ENTRY | REVERT_ENTRY | NO_OP
    target_id: int     # ID of the ledger entry (not needed for NO_OP)
    adjust_delta: int  # +/- value for ADJUST_ENTRY

Observation Space

json

{
  "task_id": "medium",
  "task_description": "Repair a financial ledger with 8 entries...",
  "ledger": [
    {"id": 0, "value": 100, "expected_value": 100, "dependencies": []},
    {"id": 1, "value": 180, "expected_value": 200, "dependencies": [3, 5]}
  ],
  "errors": [
    {"entry_id": 1, "current_value": 180, "expected_value": 200, "delta": -20}
  ],
  "remaining_budget": 12,
  "initial_budget": 12,
  "step": 0,
  "max_steps": 15,
  "done": false
}

Note: In hard mode, the dependencies list is hidden (shown as []), requiring the agent to discover dependency effects through interaction.

Tasks

Task 1 — Easy Ledger Repair · `easy` · max 10 steps · budget 10

5 independent entries, 3 errors, no dependencies.

The simplest tier — errors are independent and can be fixed in any order. Tests basic comprehension and action selection.

Task 2 — Medium Ledger Repair · `medium` · max 15 steps · budget 12

8 entries with visible dependencies and moderate budget.

Fixing entry 1 changes expected_value of entries 3 and 5. The agent must reason about repair ordering to avoid creating new errors.

Task 3 — Hard Ledger Repair · `hard` · max 12 steps · budget 8

10 entries with HIDDEN dependency graph. Cascading errors. Tight budget.

Dependencies are not visible in observations. Fixing entries triggers hidden cascades. Overcorrection is heavily penalized. Requires exploration and strategic planning.

Reward / Scoring Logic

Final score is computed deterministically (no randomness):

score = 0.5 × consistency_score
      + 0.3 × efficiency_score
      + 0.2 × budget_remaining_ratio
      − overcorrection_penalty

Where:

consistency_score = correct_entries / total_entries
efficiency_score = optimal_steps / actual_steps (capped at 1.0)
budget_remaining_ratio = remaining_budget / initial_budget
overcorrection_penalty = 0.05 × overcorrection_count

Final score is clamped to [0.0, 1.0].

Setup & Running

Local

bash

# 1. Install dependencies
pip install -r requirements.txt

# 2. Start the environment server
python server.py

# 3. Set env vars for inference
export API_BASE_URL="https://router.huggingface.co/v1"
export MODEL_NAME="Qwen/Qwen2.5-72B-Instruct"
export HF_TOKEN="hf_..."

# 4. Run the inference agent
python inference.py

Docker

bash

docker build -t auditrepairenv .

docker run -p 7860:7860 \
  -e HF_TOKEN=hf_... \
  auditrepairenv

How to run inference.py

bash

# Set required environment variables
export API_BASE_URL="https://router.huggingface.co/v1"
export MODEL_NAME="Qwen/Qwen2.5-72B-Instruct"
export HF_TOKEN="hf_..."
export ENV_BASE_URL="http://localhost:7860"

# Run the agent (runs all 3 tasks: easy, medium, hard)
python inference.py

The inference script will:

Connect to the environment server at ENV_BASE_URL
Run each task (easy → medium → hard) sequentially
Use the LLM to decide repair actions at each step
Print structured logs in the required format
Output final scores for each task

Validate

bash

# Verify the space is running
curl -X POST http://localhost:7860/reset -d '{"task_id":"easy"}' -H "Content-Type: application/json"

# Check health
curl http://localhost:7860/health

Baseline Results

Baseline agent: inference.py with Qwen/Qwen2.5-72B-Instruct

Task	Score
easy	0.90
medium	0.70
hard	0.55

Deployment & Submission

📋 Submission Checklist

Before submitting, verify:

✅ Files at root:

inference.py — exactly at root (not in subfolder)
requirements.txt — all dependencies listed
README.md — clear setup instructions
demo.py — working Gradio UI
Dockerfile — builds successfully

✅ inference.py Requirements:

Reads HF_TOKEN env variable
Reads API_BASE_URL with default
Reads MODEL_NAME with default
Validates HF_TOKEN and raises error if missing
Uses OpenAI Python client (not raw HTTP)
Prints [START] at beginning
Prints [STEP] per step with action and reward
Prints [END] at end (even on error)
Formats rewards to 2 decimal places
Prints booleans as lowercase (true/false)
Step count matches actual steps taken

✅ Output Format:

[START]
Task: easy

[STEP]
Action: FIX_ENTRY 1
Reward: 0.20

[STEP]
Action: NO_OP
Reward: 0.00

[END]
Final Score: 0.85

✅ Public GitHub Repo:

Repository is public
All code is committed
README has clear instructions
Dockerfile is present and works

✅ Hugging Face Spaces Demo:

Space URL is public
Space is built and running (not broken)
demo.py loads successfully
Inference runs end-to-end
HF_TOKEN secret is set

✅ Resource Limits (Free Tier):

Model size fits in 8GB RAM
Dockerfile doesn't exceed 2 vCPU usage
App starts in <60 seconds
No unnecessary background services

🚀 HuggingFace Spaces Deployment

For detailed deployment instructions, see HF_SPACES_GUIDE.md

Quick Start:

Prepare GitHub Repo

bash
1git add . 2git commit -m "Ready for submission" 3git push origin main
Create HF Space
- Go to huggingface.co/spaces/create
- Choose Docker SDK
- Link your GitHub repo
- Set HF_TOKEN secret in Settings
Monitor Build
- Watch Logs tab for build status
- Wait for "Running" status
- Access app via public URL
Test

bash
1curl -X POST https://your-space.hf.space/reset \ 2 -d '{"task_id":"easy"}' \ 3 -H "Content-Type: application/json"

📝 Project Pitch

For pitching at hackathons, see PITCH.md

30-second pitch:

"We built AuditRepairEnv++, an RL environment where AI agents repair financial ledgers with interdependent errors under budget constraints. Fixing one entry cascades changes to others, forcing agents to plan strategically. It benchmarks LLM reasoning on cost-constrained optimization."

🔧 Troubleshooting

Issue: inference.py fails with "module not found"

Verify requirements.txt is installed: pip install -r requirements.txt

Issue: HF_TOKEN error

Generate token at huggingface.co/settings/tokens
Export: export HF_TOKEN="hf_..."

Issue: Space shows "Application Error"

Check Logs tab in HF Spaces
Verify app listens on 0.0.0.0:7860
Ensure HF_TOKEN secret is set

Issue: "Out of memory" on Spaces

Use smaller model or quantized version
Reduce MAX_TOKENS in inference.py
Consider upgrading Space tier

See HF_SPACES_GUIDE.md for detailed troubleshooting.

Project Structure

audit-repair-env/
├── inference.py          ← Main submission file (MUST be at root)
├── server.py             ← OpenEnv environment server
├── tasks.py              ← Task definitions & environment logic
├── demo.py               ← Gradio UI (minimal black aesthetic)
├── requirements.txt      ← Python dependencies
├── Dockerfile            ← Docker image definition
├── README.md             ← This file
├── HF_SPACES_GUIDE.md    ← Deployment instructions
├── PITCH.md              ← Project pitch & overview
└── auditrepairenv/       ← Python package (optional)
    └── __init__.py

Documentation

README.md — This file; environment overview
PITCH.md — Project pitch, problem statement, comparison to other benchmarks
HF_SPACES_GUIDE.md — Step-by-step Spaces deployment, troubleshooting, how HF Spaces works
inference.py — Submission script with HF_TOKEN validation
demo.py — Live Gradio demo with dark theme

Community & Support

GitHub Issues: Report bugs or suggest features
Discussions: Ask questions about the environment
Spaces Discussions: Comment on the demo

License

MIT License — see LICENSE file

Citation

If you use AuditRepairEnv++ in your research, please cite:

bibtex

@misc{auditrepairenv2024,
  title={AuditRepairEnv++: Cost-Constrained Iterative Ledger Repair},
  author={Your Name},
  year={2024},
  howpublished={Hugging Face Spaces},
  url={https://huggingface.co/spaces/username/audit-repair-env}
}

Good luck with your submission! 🚀

KeithXD/Team_Sparks

AuditRepairEnv++ — Cost-Constrained Iterative Ledger Repair

Problem Description

Solution Approach

RL Reasoning

Action Space

Action Model (Pydantic)

Observation Space

Tasks

Task 1 — Easy Ledger Repair · easy · max 10 steps · budget 10

Task 2 — Medium Ledger Repair · medium · max 15 steps · budget 12

Task 3 — Hard Ledger Repair · hard · max 12 steps · budget 8

Reward / Scoring Logic

Setup & Running

Local

Docker

How to run inference.py

Validate

Baseline Results

Deployment & Submission

📋 Submission Checklist

🚀 HuggingFace Spaces Deployment

📝 Project Pitch

🔧 Troubleshooting

Project Structure

Documentation

Community & Support

License

Citation

KeithXD/
Team_Sparks

Task 1 — Easy Ledger Repair · `easy` · max 10 steps · budget 10

Task 2 — Medium Ledger Repair · `medium` · max 15 steps · budget 12

Task 3 — Hard Ledger Repair · `hard` · max 12 steps · budget 8