| --- |
| title: Invoice Processing Pipeline |
| emoji: 🧾 |
| colorFrom: blue |
| colorTo: green |
| sdk: docker |
| app_port: 7860 |
| pinned: false |
| tags: |
| - openenv |
| - multi-agent |
| - grpo |
| - rl |
| short_description: 5-agent adversarial fraud detection RL environment |
| --- |
| <div align="center"> |
|
|
| <img src="https://capsule-render.vercel.app/api?type=waving&color=gradient&customColorList=6,11,20&height=200§ion=header&text=Invoice%20Processing%20Pipeline&fontSize=40&fontColor=fff&animation=twinkling&fontAlignY=35&desc=Self-Improving%20Multi-Agent%20Fraud%20Detection%20%7C%20OpenEnv%20%2B%20GRPO%20%2B%20Qwen2.5&descAlignY=55&descSize=16" width="100%"/> |
|
|
| <p> |
| <a href="https://ps2181-invoice-processing-pipeline.hf.space/web"> |
| <img src="https://img.shields.io/badge/🚀%20Live%20Demo-HuggingFace%20Spaces-FF9D00?style=for-the-badge&logo=huggingface&logoColor=white" /> |
| </a> |
| <a href="https://colab.research.google.com/drive/1C1_3giNt-NmbzKNFJr5_L1fms3L8LfmB"> |
| <img src="https://img.shields.io/badge/Training%20Colab-Open%20Notebook-F9AB00?style=for-the-badge&logo=googlecolab&logoColor=white" /> |
| </a> |
| <a href="https://ps2181-invoice-processing-pipeline.hf.space/docs"> |
| <img src="https://img.shields.io/badge/API%20Docs-FastAPI-009688?style=for-the-badge&logo=fastapi&logoColor=white" /> |
| </a> |
| </p> |
| |
| <p> |
| <img src="https://img.shields.io/badge/Framework-OpenEnv-1A356E?style=for-the-badge" /> |
| <img src="https://img.shields.io/badge/Model-Qwen2.5--1.5B%20+%20LoRA%20r%3D16-8B1A4E?style=for-the-badge" /> |
| <img src="https://img.shields.io/badge/Training-GRPO%20+%20Unsloth-00A67E?style=for-the-badge" /> |
| <img src="https://img.shields.io/badge/Agents-5%20Adversarial-E44D26?style=for-the-badge" /> |
| </p> |
|
|
| <p> |
| <img src="https://img.shields.io/badge/Tasks-10%20Progressive-6C3483?style=for-the-badge" /> |
| <img src="https://img.shields.io/badge/Deployment-Docker%20%7C%20HF%20Spaces-0D1117?style=for-the-badge&logo=docker" /> |
| <img src="https://img.shields.io/badge/Theme-%234%20Self--Improvement-FF6B35?style=for-the-badge" /> |
| <img src="https://img.shields.io/badge/Hackathon-Meta%20PyTorch%202026-185FA5?style=for-the-badge" /> |
| </p> |
|
|
| <br/> |
|
|
| > **Meta PyTorch OpenEnv Hackathon — Grand Finale · April 25–26, 2026** |
| > |
| > Team: **Pritam Satpathy** & **Gnana Nawin T** · VIT, Vellore |
|
|
| <br/> |
|
|
| <a href="https://git.io/typing-svg"> |
| <img src="https://readme-typing-svg.demolab.com?font=Fira+Code&weight=600&size=22&pause=1000&color=007A87¢er=true&vCenter=true&width=750&lines=5-Agent+Adversarial+Fraud+Detection+System;Self-Improving+via+Cross-Episode+Regulator;GRPO-Trained+LoRA+Agents+on+Live+Environment;Invoice+%E2%86%92+Extract+%E2%86%92+Audit+%E2%86%92+Approve+%E2%86%92+Improve" alt="Typing SVG" /> |
| </a> |
|
|
| </div> |
|
|
| --- |
|
|
| ## 🔥 The Core Idea |
|
|
| > *A system that continuously generates harder challenges targeting its own weakest points.* |
|
|
| Most fraud detection pipelines are **static**. Ours **gets harder for itself over time**: the Regulator finds where the Auditor keeps failing, the Generator exploits those exact blind spots in the next episode, the Auditor's new mistakes update the Regulator — and the loop closes without any human intervention. |
|
|
| **Primary theme: #4 Self-Improvement · Secondary: #1 Multi-Agent Interactions** |
|
|
| <div align="center"> |
| <img width="1710" height="326" alt="5-agent self-improvement loop" src="https://github.com/user-attachments/assets/319654c3-aa24-47e8-9716-734d4e902168" /> |
| </div> |
|
|
| --- |
|
|
| ## 🤖 5-Agent Architecture |
| ``` |
| 🎯 Regulator ──bias weights──────────────────────────────────► ⚡ Generator |
| ▲ │ |
| │ raw invoice text |
| │ missed fraud types ▼ |
| │ 🔍 Extractor |
| │ │ |
| │ structured data |
| │ ▼ |
| └──── episode outcome ──── ✅ Approver ◄─audit results─── 🕵️ Auditor |
| ``` |
|
|
| <div align="center"> |
|
|
| | Agent | Role | Reward Signal | |
| |:---:|:---|:---| |
| | 🎯 **Regulator** | Cross-episode oversight: detects Auditor blind spots, reweights Generator | Precision `0.35` + Recall `0.35` + No over-flagging `0.15` + Early warning `0.15` | |
| | ⚡ **Generator** | Adversary: creates invoices biased toward blind spots | `+0.85` evades both · `+0.60` evades Auditor · `+0.10` caught | |
| | 🔍 **Extractor** | Parser: text → structured JSON with 4 independent signals | Format `0.10` · Field accuracy `0.40` · Math `0.25` · Completeness `0.25` | |
| | 🕵️ **Auditor** | Detector: fraud classification with confidence scores | `+0.99` correct type · `+0.90` clean cleared · `+0.01` miss or FP | |
| | ✅ **Approver** | Gatekeeper: final approve / escalate / reject | `≥0.80` → reject · `0.50–0.80` → escalate · `<0.50` → approve | |
|
|
| </div> |
|
|
| --- |
|
|
| ## ⚡ Three Novel Features |
|
|
| <table> |
| <tr> |
| <td width="33%" align="center"> |
|
|
| ### 🔮 Predictive Regulator |
|
|
| Computes **trend slopes** over 5-episode windows.<br/>Warns of *emerging* blind spots **before** detection rates cross the critical threshold — proactive oversight, not reactive retraining. |
|
|
| `+0.15 early-warning bonus` |
|
|
| </td> |
| <td width="33%" align="center"> |
|
|
| ### 🧬 Compound Fraud |
|
|
| Invoices carry **two fraud signals simultaneously** (e.g. phantom vendor + price gouging).<br/>Partial credit `+0.65` for catching one; full reward `+0.99` for both. |
|
|
| Prevents single-signal heuristics. |
|
|
| </td> |
| <td width="33%" align="center"> |
|
|
| ### 📊 Confidence Calibration |
|
|
| Tracks `(confidence, correct?)` pairs per fraud type.<br/>Detects **overconfident misses** — the Auditor saying "90% sure, approved" on fraud — the most dangerous real-world failure mode. |
|
|
| </td> |
| </tr> |
| </table> |
|
|
| --- |
|
|
| ## 🎯 10 Tasks — Progressive Curriculum |
|
|
| <div align="center"> |
|
|
| | # | Task | What the Agent Faces | Difficulty | |
| |:---:|:---|:---|:---:| |
| | 1 | `easy` | Single clean invoice — extract 5 fields | 🟢 Easy | |
| | 2 | `medium` | Batch with date chaos, vendor typos, currency noise | 🟡 Medium | |
| | 3 | `hard` | Extraction + PO reconciliation — flag overcharges, missing items | 🟠 Hard | |
| | 4 | `expert` | Full fraud audit across all four fraud types | 🔴 Expert | |
| | 5 | `adversarial` | OCR corruption, SUBTOTAL traps, fake TAX/FX noise lines | 🔴 Expert | |
| | 6 | `negotiate` | Ask clarifying questions first (bonus for ≤2), then extract | 🟡 Medium | |
| | 7 | `supply_chain` | Detect quantity shortfalls, price spikes, phantom deliveries | 🔴 Expert | |
| | 8 | `long_horizon` | 20-step 4-phase investigation: extract → reconcile → audit → risk forecast | 🔴 Expert | |
| | 9 | `personalized` | Adapts to your weak fields — next invoice always targets your worst category | 🔄 Adaptive | |
| | 10 | `curriculum` | Auto-progresses easy→medium→hard→expert based on score (≥0.80 to advance) | 🔄 Auto | |
|
|
| </div> |
|
|
| Dynamic difficulty also adjusts **within** each task via a rolling 10-episode score window: score above `0.85` → heavier OCR, more discrepancies, deeper traps. Drop below `0.60` → it eases off. |
|
|
| --- |
|
|
| ## 📈 Training Results — GRPO on Live Environment |
|
|
| All 3 agents trained with **TRL GRPOTrainer + Unsloth** using the deployed HF Space as the live reward verifier — `/grader` endpoint *is* the reward function during training. |
|
|
| ### Before vs After Training |
|
|
| <div align="center"> |
|
|
| | Agent | Untrained (random) | Qwen 72B baseline | After GRPO | Improvement | |
| |:---:|:---:|:---:|:---:|:---:| |
| | 🔍 **Extractor** | 0.10 | 0.67 | **0.914** | +714% vs random | |
| | 🕵️ **Auditor** | 0.01 | — | **0.52** live reward | Dead → active signal | |
| | ⚡ **Generator** | — | — | **0.22** plausibility | Format & realism learned | |
|
|
| </div> |
|
|
| **Setup:** Qwen2.5-1.5B-Instruct · 4-bit QLoRA r=16 · Unsloth + TRL · Google Colab A100 |
|
|
| ### Extractor Reward Curve |
|
|
|  |
| *X-axis: training step (1–20) · Y-axis: reward (0–1). Left: total GRPO reward across 4 independent signals (format 0.10 + field accuracy 0.40 + math 0.25 + completeness 0.25). Right: live `/grader` score peaking at **0.914** — above Qwen 72B baseline (0.67) and untrained 1.5B (0.46).* |
|
|
| *Left: Total GRPO reward across 4 signals (format + field + math + completeness) over 20 training steps. Right: Live environment grader score peaking at **0.914** — above Qwen 72B baseline (0.67) and untrained 1.5B baseline (0.46).* |
|
|
| ### Auditor Reward Curve (Run 2 — Bug Fixed) |
|
|
|  |
| *X-axis: training step (1–30) · Y-axis: reward (0–1). Total reward (blue) and live env reward (orange) with ±1 std band. Best total: **0.719** at step 10. Live env reward climbed from 0.01 (dead signal, Run 1) to **0.52** after fixing the TRL episode_id list indexing bug.* |
|
|
| *Total reward (blue) and live env reward (orange) over 30 steps with ±1 std band. Best total reward: **0.719**. Live env reward rose from 0.01 (dead signal in Run 1) to **0.52** after fixing the episode_id list bug.* |
|
|
| ### Generator Reward Curve |
|
|
|  |
| *X-axis: training step (1–30) · Y-axis: reward (0–1). Live evasion reward (red) flat near 0 — Auditor+Approver caught all fraud attempts. Fraud plausibility reward (orange dashed) stable at ~0.20 — Generator learned realistic invoice structure even without successful evasion.* |
|
|
| *Live evasion reward (red) flat near 0 — Auditor+Approver caught all fraud attempts. Fraud plausibility reward (orange dashed) learned and stable at ~0.20, showing the Generator learned to produce realistic-looking invoices even without successful evasion.* |
|
|
| ### 🔍 Reward Hacking Caught at Step 10 |
|
|
| At step 10 the model achieved `math_consistency = 0.97` and `completeness = 1.0` while `field_accuracy = 0.00` — it had learned to output **arithmetically-consistent JSON with entirely hallucinated values**: |
|
|
| ``` |
| Step 10 — Reward Hacking Detected: |
| format: 0.10 ✅ |
| math_consistency: 0.97 ✅ ← model gaming this signal |
| completeness: 1.00 ✅ ← model gaming this signal |
| field_accuracy: 0.00 ❌ ← hallucinating all values |
| |
| Action: adjusted training emphasis on field_accuracy weight |
| Result: field_accuracy climbed to 0.30+ by step 30 |
| ``` |
|
|
| Without 4 independent signals, a single aggregated reward would have called this success. **Independent signals are diagnostics, not just incentives.** |
|
|
| ### Auditor Training — Run 2 (exact data) |
|
|
| <div align="center"> |
|
|
| | Step | Total Reward | Live Env Reward | ±Std | |
| |:---:|:---:|:---:|:---:| |
| | 5 | 0.4828 | 0.2828 | ±0.194 | |
| | 10 | **0.7188** | **0.5188** | ±0.239 | |
| | 15 | 0.4538 | 0.2538 | ±0.123 | |
| | 20 | 0.5733 | 0.3733 | ±0.212 | |
| | 25 | 0.5325 | 0.3325 | ±0.232 | |
| | 30 | 0.6038 | 0.4038 | ±0.147 | |
|
|
| *Run 1 (dead signal): live env reward flat at 0.010 — TRL passes episode_id as a list; old code sent the whole list instead of indexing per completion* |
|
|
| </div> |
|
|
| --- |
|
|
| ## 🎁 Reward Architecture |
|
|
| ### 🔍 Extractor — 4 Independent Signals |
|
|
| ```python |
| reward_format(extracted) # 0.10 — all 5 required JSON keys present? |
| reward_field_accuracy(extracted, gt) # 0.40 — vendor / date / currency / total match? |
| reward_math_consistency(extracted) # 0.25 — qty × unit_price = amount per line? |
| reward_completeness(extracted, gt) # 0.25 — all expected line items captured? |
| |
| # All clamped to (0.01, 0.99) — no log(0), no gradient collapse at boundaries |
| ``` |
|
|
| ### 🕵️ Auditor |
|
|
| <div align="center"> |
|
|
| | Outcome | Reward | Why | |
| |:---|:---:|:---| |
| | Correct fraud type detected | **0.99** | Rewards precise classification, not just binary flagging | |
| | Clean invoice correctly approved | **0.90** | Keeps false-positive rate honest | |
| | Compound fraud — one of two types caught | **0.65** | Partial credit prevents cliff on hard cases | |
| | Fraud flagged but wrong type | **0.50** | Penalises sloppiness; rewards catching *something* | |
| | Miss or false positive | **0.01** | Near-zero punishes both failure modes symmetrically | |
|
|
| </div> |
|
|
| ### ⚡ Generator (Adversarial Self-Play) |
|
|
| | Outcome | Reward | |
| |:---|:---:| |
| | Fraud evades **both** Auditor and Approver | **0.85** | |
| | Auditor misses, Approver catches | **0.60** | |
| | Auditor catches it | **0.10** | |
|
|
| ### 🎯 Regulator — Cross-Episode |
|
|
| ``` |
| Total = Precision(0.35) + Recall(0.35) + No-over-flagging(0.15) + Early-warning-bonus(0.15) |
| ``` |
|
|
| The early-warning bonus rewards predictions of *emerging* blind spots — before detection rates cross the critical threshold. |
|
|
| --- |
|
|
| ## 🧠 Trained LoRA Agents |
|
|
| <div align="center"> |
|
|
| | Agent | Base Model | LoRA Config | HuggingFace Hub | |
| |:---:|:---|:---:|:---| |
| | 🔍 Extractor | Qwen2.5-1.5B-Instruct | r=16, α=16, 4-bit QLoRA | [ps2181/extractor-lora-qwen2.5-1.5b](https://huggingface.co/ps2181/extractor-lora-qwen2.5-1.5b) | |
| | 🕵️ Auditor | Qwen2.5-1.5B-Instruct | r=16, α=16, 4-bit QLoRA | [ps2181/auditor-lora-qwen2.5-1.5b](https://huggingface.co/ps2181/auditor-lora-qwen2.5-1.5b) | |
| | ⚡ Generator | Qwen2.5-1.5B-Instruct | r=16, α=16, 4-bit QLoRA | [ps2181/generator-lora-qwen2.5-1.5b](https://huggingface.co/ps2181/generator-lora-qwen2.5-1.5b) | |
|
|
| </div> |
|
|
| **LoRA target modules:** `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj` |
|
|
| --- |
|
|
| ## 🌍 The Regulator in Action |
|
|
| After each episode, the Regulator publishes a report the Generator uses to bias its next batch: |
|
|
| ``` |
| GET /regulator/report |
| |
| { |
| "total_audits_recorded": 20, |
| "detection_rates": { |
| "phantom_vendor": "31% ⚠ BLIND SPOT (-0.08↓)", |
| "price_gouging": "74% ✓ OK (+0.03↑)", |
| "math_fraud": "81% ✓ OK (+0.01↑)", |
| "duplicate_submission": "62% ⚡ EMERGING (-0.02↓)" |
| }, |
| "blind_spots": ["phantom_vendor"], |
| "emerging_blind_spots": ["duplicate_submission"], |
| "generator_weights": { |
| "phantom_vendor": 0.30, ← 3× upweighted (blind spot) |
| "duplicate_submission": 0.20, ← 2× upweighted (emerging) |
| "price_gouging": 0.125, |
| "math_fraud": 0.125, |
| "compound_fraud": 0.10 |
| }, |
| "verdict": "Recommend retraining on: phantom_vendor" |
| } |
| ``` |
|
|
| --- |
|
|
| ## 🎭 Sample Multi-Agent Episode |
|
|
| ``` |
| ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ |
| MULTI-AGENT PIPELINE · LIVE EPISODE |
| ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ |
| |
| 🎯 REGULATOR (30-episode rolling window) |
| ──────────────────────────────────────────────── |
| phantom_vendor 31% ⚠ BLIND SPOT ← prioritised 60% |
| price_gouging 74% ✓ OK |
| math_fraud 81% ✓ OK |
| duplicate 62% ✓ OK |
| |
| ⚡ GENERATOR (Qwen2.5 LoRA) |
| ──────────────────────────────────────────────── |
| Fraud focus : phantom_vendor (60% Regulator weight) |
| Vendor : ShadowByte Technologies ← not in registry |
| |
| 🔍 EXTRACTOR (Qwen2.5 LoRA) |
| ──────────────────────────────────────────────── |
| Reward : 0.847 [format 0.10 · field 0.38 · math 0.25 · completeness 0.12] |
| |
| 🕵️ AUDITOR (Qwen2.5 LoRA) |
| ──────────────────────────────────────────────── |
| INV-85529 → 🚨 FLAGGED [PHANTOM VENDOR] conf=0.91 |
| INV-85530 → ✅ APPROVED conf=0.88 |
| |
| ✅ APPROVER |
| ──────────────────────────────────────────────── |
| INV-85529 → ❌ REJECT |
| Generator reward : 0.60 (evaded Auditor on 1/3, Approver caught) |
| |
| 🎯 REGULATOR UPDATE |
| ──────────────────────────────────────────────── |
| phantom_vendor detection: 31% → 45% ↑ improving |
| ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ |
| ``` |
|
|
| --- |
|
|
| ## 🚀 Quick Start |
|
|
| ```bash |
| # Health check |
| curl https://ps2181-invoice-processing-pipeline.hf.space/health |
| |
| # Environment-wide metrics |
| curl https://ps2181-invoice-processing-pipeline.hf.space/metrics |
| |
| # Auto-progressive curriculum episode |
| curl -X POST https://ps2181-invoice-processing-pipeline.hf.space/reset \ |
| -H "Content-Type: application/json" -d '{"task_id": "curriculum"}' |
| |
| # Start multi-agent episode |
| curl -X POST https://ps2181-invoice-processing-pipeline.hf.space/multi/reset |
| |
| # Regulator blind spot report |
| curl https://ps2181-invoice-processing-pipeline.hf.space/regulator/report |
| ``` |
|
|
| ### Run Training (Google Colab) |
|
|
| [](https://colab.research.google.com/drive/1C1_3giNt-NmbzKNFJr5_L1fms3L8LfmB) |
|
|
| ``` |
| Colab → /reset (fresh synthetic invoice from live environment) |
| → model generates JSON |
| → /grader scores against ground truth |
| → GRPO updates weights toward higher-reward completions |
| → repeat 200 steps |
| ``` |
|
|
| --- |
|
|
| ## 🗂️ Repository Structure |
|
|
| ``` |
| invoice-processing-pipeline/ |
| │ |
| ├── server/ |
| │ ├── app.py # FastAPI — 18 endpoints |
| │ ├── environment.py # 10 tasks · graders · dynamic difficulty |
| │ ├── multi_agent_environment.py # 5-agent system + AuditorPerformanceTracker |
| │ ├── agents.py # Lazy-loading LoRA inference wrappers |
| │ └── web_ui.py # Gradio UI (mounted at /web) |
| │ |
| ├── models.py # Pydantic: Action · Observation · State |
| ├── inference.py # Standalone inference helper |
| ├── client.py # OpenEnv-compatible Python client |
| │ |
| ├── extractor_training_grpo.ipynb # 🔥 Extractor GRPO training (Unsloth + TRL) |
| ├── auditor_grpo_training.ipynb # 🔥 Auditor GRPO training |
| ├── generator_grpo_training.ipynb # 🔥 Generator GRPO training |
| │ |
| ├── assets/ |
| │ ├── reward_curve.png # Extractor training curve |
| │ ├── auditor_reward_curve_run2.png |
| │ └── generator_reward_curve.png |
| │ |
| ├── openenv.yaml # OpenEnv manifest (all tasks declared) |
| ├── Dockerfile # HF Spaces Docker (port 7860, non-root UID 1000) |
| ├── pyproject.toml # Project metadata + dependencies |
| ├── requirements.txt # Runtime dependencies |
| ├── validate-submission.sh # Submission validator script |
| ├── BLOG.md # HuggingFace blog post |
| └── ROUND2_PROBLEM_STATEMENT.md # Full problem statement + reward design rationale |
| ``` |
|
|
| --- |
|
|
| ## 🔌 API Reference |
|
|
| ### Core OpenEnv |
|
|
| | Endpoint | Method | Description | |
| |:---|:---:|:---| |
| | `/health` | `GET` | Health check → `{"status": "ok", "active_sessions": N}` | |
| | `/tasks` | `GET` | All tasks with descriptions, schemas, difficulty levels | |
| | `/metrics` | `GET` | Per-task episode counts, avg/best scores, Regulator state | |
| | `/reset` | `POST` | Start episode `{"task_id": "easy\|medium\|...\|curriculum"}` | |
| | `/step` | `POST` | Submit extraction → reward + feedback + hint + reward_breakdown | |
| | `/grader` | `POST` | Score without consuming an attempt (training reward signal) | |
| | `/state` | `GET` | Episode metadata — step_count, done, best_reward, history | |
| | `/ws` | `WS` | Full episode over WebSocket (OpenEnv standard) | |
| | `/web` | `GET` | Gradio interactive demo UI | |
| |
| ### Multi-Agent |
| |
| | Endpoint | Method | Description | |
| |:---|:---:|:---| |
| | `/multi/reset` | `POST` | Start 5-agent episode — Generator biased by Regulator weights | |
| | `/multi/extract` | `POST` | Score Extractor output (4 independent signals) | |
| | `/multi/audit` | `POST` | Score Auditor output, update 30-episode performance tracker | |
| | `/multi/approve` | `POST` | Run Approver, compute Generator adversarial reward | |
| | `/multi/state/{id}` | `GET` | Full episode state including all agent scores | |
| | `/generator/score` | `POST` | Direct Generator scoring through Auditor+Approver pipeline | |
| |
| ### Regulator |
| |
| | Endpoint | Method | Description | |
| |:---|:---:|:---| |
| | `/regulator/report` | `GET` | Detection rates, blind spots, calibration, generator weights | |
| | `/regulator/forecast` | `GET` | Trend slopes + emerging blind spot warnings with episode countdown | |
| | `/regulator/calibration` | `GET` | Overconfidence / underconfidence per fraud type | |
| | `/regulator/predict` | `POST` | Score a Regulator blind-spot prediction | |
| | `/regulator/demo_seed` | `POST` | Seed tracker with realistic demo data | |
|
|
| --- |
|
|
| ## 🏗️ Tech Stack |
|
|
| <div align="center"> |
|
|
| | Layer | Technology | |
| |:---|:---| |
| | **Environment** | [OpenEnv](https://github.com/meta-pytorch/OpenEnv) · FastAPI · Pydantic v2 | |
| | **UI** | Gradio 4.x (mounted at `/web`) | |
| | **Deployment** | Docker · HuggingFace Spaces (vcpu-2 / 8 GB) | |
| | **Training** | [TRL GRPOTrainer](https://huggingface.co/docs/trl) · [Unsloth](https://github.com/unslothai/unsloth) | |
| | **Model** | `unsloth/Qwen2.5-1.5B-Instruct` · 4-bit QLoRA · r=16 · A100 | |
| | **Reward** | Live `/grader` endpoint on HF Space as verifier | |
| | **Session Mgmt** | Thread-safe `OrderedDict` · 200-session cap · LRU eviction | |
| | **Dynamic Difficulty** | Per-task rolling window (maxlen=10) → adjusts OCR intensity, batch size, discrepancy count | |
|
|
| </div> |
|
|
| --- |
|
|
| ## 🎭 Theme Alignment |
|
|
| <div align="center"> |
|
|
| | Theme | Alignment | Evidence | |
| |:---:|:---|:---| |
| | **#4 Self-Improvement** (primary) | ✅ Core | Regulator detects blind spots → Generator biases toward them → Auditor improves → loop repeats | |
| | **#1 Multi-Agent Interactions** | ✅ Core | 5 agents with conflicting incentives — Generator vs Auditor adversarial self-play | |
| | **#1 Fleet AI Scalable Oversight** | ✅ Bonus | Regulator monitors Auditor cross-episode with predictive trend detection | |
| | **#3.1 Professional Tasks** | ✅ Core | Invoice + PO + vendor registry + supply chain = real enterprise AP workflow | |
| | **#2 Long-Horizon Planning** | ✅ Partial | `long_horizon` task: 20-step 4-phase investigation with multi-turn state | |
|
|
| </div> |
|
|
| --- |
|
|
| ## 👥 Team |
|
|
| <div align="center"> |
|
|
| | | | |
| |:---:|:---:| |
| | **Pritam Satpathy** | **Gnana Nawin T** | |
| | [🤗 ps2181](https://huggingface.co/ps2181) | [🤗 gnananawin](https://huggingface.co/gnananawin) | |
| | Scaler School of Technology | Scaler School of Technology | |
|
|
| **Meta PyTorch OpenEnv Hackathon — Grand Finale · April 25–26, 2026 · Bangalore** |
|
|
| </div> |
|
|
| --- |
|
|
| ## 🔗 All Links |
|
|
| <div align="center"> |
|
|
| | Resource | Link | |
| |:---|:---| |
| | 🚀 **Live Environment** | https://ps2181-invoice-processing-pipeline.hf.space | |
| | 🖥️ **Gradio Demo UI** | https://ps2181-invoice-processing-pipeline.hf.space/web | |
| | 📖 **API Documentation** | https://ps2181-invoice-processing-pipeline.hf.space/docs | |
| | 📊 **Metrics Dashboard** | https://ps2181-invoice-processing-pipeline.hf.space/metrics | |
| | 📝 **Blog Post** | https://github.com/ps2181/invoice-processing-pipeline/blob/main/BLOG.md | |
| | 🤗 **Extractor Model** | https://huggingface.co/ps2181/extractor-lora-qwen2.5-1.5b | |
| | 🕵️ **Auditor Model** | https://huggingface.co/ps2181/auditor-lora-qwen2.5-1.5b | |
| | ⚡ **Generator Model** | https://huggingface.co/ps2181/generator-lora-qwen2.5-1.5b | |
| | 📓 **Training Colab (Auditor Agent)** | https://colab.research.google.com/drive/1C1_3giNt-NmbzKNFJr5_L1fms3L8LfmB | |
| | 📓 **Training Colab (Extractor Agent)** | https://colab.research.google.com/drive/1fxfBt13LjmT4m98pJq-b5B__1ytFeszK?usp=sharing | |
| | 📓 **Training Colab (Generator Agent)** | https://colab.research.google.com/drive/1O293_VBZQCthxlGpgvz5kxoty3zcsWGH?usp=sharing | |
| | 💻 **GitHub** | https://github.com/ps2181/invoice-processing-pipeline | |
| | 🎥 **Demo Video** | https://youtu.be/QSB4UOLvaC8?si=SGnIwsfTW4JGsU3e | |
| | 🧩 **OpenEnv Framework** | https://github.com/meta-pytorch/OpenEnv | |
| |
| </div> |
| |
| --- |
| |
| <div align="center"> |
| |
| <img src="https://capsule-render.vercel.app/api?type=waving&color=gradient&customColorList=6,11,20&height=100§ion=footer&animation=twinkling" width="100%"/> |
| |
| **Built with ❤️ for the Meta PyTorch OpenEnv Hackathon 2026** |
| |
| *"The system that gets harder for itself — so the agent never stops learning."* |
| |
| </div> |
| |
