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| 1 |
+
---
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+
title: ImmunoOrg 2.0
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+
emoji: π‘οΈ
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colorFrom: blue
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colorTo: green
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sdk: docker
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app_port: 7860
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pinned: false
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---
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# ImmunoOrg 2.0 β The Autonomous, Self-Healing Enterprise
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> An OpenEnv RL environment where an LLM defender learns to contain
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> cyber-attacks **and** restructure the organization that lets them
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> succeed. Built for the OpenEnv Hackathon (India 2026).
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### For judges (60 s)
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β **[`JUDGES_60_SECONDS.md`](./JUDGES_60_SECONDS.md)** Β· Live app: https://hirann-immunoorg-v3.hf.space/demo (War Room + episode demo on **one** page).
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**β± Crunch time?** **[`WIN_30MIN.md`](./WIN_30MIN.md)** (fastest calm path) β then **[`SUBMIT_NOW.md`](./SUBMIT_NOW.md)** for the full checklist.
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Run **`python scripts/make_hackathon_training_figure.py`** to create **`evidence_grpo_training.png`** in ~2 minutes (real env curve + Colab pointer).
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| Resource | Link |
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| --- | --- |
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| π’ **Live Space (direct host)** | https://hirann-immunoorg-v3.hf.space |
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| π€ **HF Space card** | https://huggingface.co/spaces/hirann/immunoorg-v3 |
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| π **War Room (Theme #1, inside `/demo`)** | Same page as episode demo β **Live LLM War Room** section |
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| π©ββοΈ **Judges β 60 s** | [`JUDGES_60_SECONDS.md`](./JUDGES_60_SECONDS.md) |
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| π **Problem statement (Round 2 formal)** | [`PROBLEM_STATEMENT.md`](./PROBLEM_STATEMENT.md) |
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| π **Mini-blog (Writeup)** | [`Blog.MD`](./Blog.MD) |
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| βοΈ **Publish HF post + YouTube** | [`PUBLISH_HACKATHON.md`](./PUBLISH_HACKATHON.md) |
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| π **HF mini-blog (public URL)** | *Replace after publishing:* `HF_MINI_BLOG_URL` |
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| βΆοΈ **YouTube demo (< 2 min)** | *Replace after upload:* `YOUTUBE_DEMO_URL` |
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| π **Judgesβ guide (official)** | [What judges look for](https://docs.google.com/document/d/1Odznuzwtb1ecDOm2t6ToZd4MuMXXfO6vWUGcxbC6mFs/edit?tab=t.0#bookmark=kix.2dz0x0nie3me) |
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| π¬ **Video script (90 sec)** | [`VIDEO_SCRIPT.md`](./VIDEO_SCRIPT.md) |
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| π **Training notebook (Colab + TRL GRPO)** | [Open in Colab](https://colab.research.google.com/github/Charannoo/immunoorg/blob/master/ImmunoOrg_Training_Colab.ipynb) Β· [`ImmunoOrg_Training_Colab.ipynb`](./ImmunoOrg_Training_Colab.ipynb) |
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| β‘ **Win in ~30 min (start here if stressed)** | [`WIN_30MIN.md`](./WIN_30MIN.md) |
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| β‘ **Deadline playbook (~5 h)** | [`SUBMIT_NOW.md`](./SUBMIT_NOW.md) |
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| π₯οΈ **HPC training pipeline** | [`scripts/hpc/HANDOFF.md`](./scripts/hpc/HANDOFF.md) |
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| β
**Pre-submit checklist script** | `python scripts/verify_hackathon_submission.py` |
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| π¬ **Research notes** | [`RESEARCH.md`](./RESEARCH.md) |
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| π§ͺ **Judges' walkthrough** | [`JUDGING_GUIDE.md`](./JUDGING_GUIDE.md) |
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| π» **GitHub source** | https://github.com/Charannoo/immunoorg |
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**Before you submit:** publish a Hugging Face **post** or **YouTube** link (see [`PUBLISH_HACKATHON.md`](./PUBLISH_HACKATHON.md)), replace the two placeholder rows above with real URLs, run `python scripts/verify_hackathon_submission.py`, then push GitHub + Space.
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**Windows + TRL:** if `import trl` fails with `UnicodeDecodeError`, run with UTF-8:
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`set PYTHONUTF8=1` (cmd) or `$env:PYTHONUTF8=1` (PowerShell).
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---
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## TL;DR
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Two graphs run in parallel inside one episode:
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1. **A technical network** β 7-23 nodes (web servers, DBs, CI/CD, DNS) with
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real vulnerability scores.
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2. **An organizational graph** β departments with approval chains, trust
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scores, and political deadlocks.
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The agent has 28 actions across 3 categories (tactical / strategic /
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diagnostic) and must fix both layers simultaneously, against an adversary
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that adapts to its policy, under conflicting board directives, with a
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**5-track composable reward** that no single signal can hack.
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Read [`PROBLEM_STATEMENT.md`](./PROBLEM_STATEMENT.md) for the formal
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Round 2 definition (problem / env / capabilities / tasks / reward /
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post-training).
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---
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## Evidence (committed PNGs β judges scan these in seconds)
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All charts are produced by `python generate_evidence.py` and
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`python scripts/generate_training_evidence.py` and committed to the repo.
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*Random vs Heuristic across all 4 difficulty levels β Heuristic policy
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(gold standard for reward shaping) beats Random by 4-11 points,
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proving the env is learnable and reward shaping has signal.*
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*Per-family reward (10 episodes each, real env rollouts). The heuristic
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policy beats the random baseline in **every** scenario family β that
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lift is the signal the GRPO trainer climbs.*
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*6 generations of self-improvement: reward-per-step trends up, time-to-
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containment trends down, org efficiency rises as mutations accumulate.*
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*Per-step contribution of the 5 reward tracks. No single track dominates
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β anti-reward-hacking property called out in the brief.*
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*The "self-healed enterprise" moment: org graph after the agent
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restructures it via `ESTABLISH_DEVSECOPS` + `REDUCE_BUREAUCRACY`.
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Approval latency drops from 72h to 4h.*
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*Multi-agent War Room consensus dynamics + 4-gate DevSecOps Mesh event counts.*
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**GRPO training curve (`evidence_grpo_training.png`):** generate from a real TRL run, then:
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```bash
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python scripts/plot_grpo_log_history.py immunoorg-defender/grpo_log_history.json
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```
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Or run **Colab Step 4b**, which saves the figure directly. See [`training_logs/README.md`](./training_logs/README.md).
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Additional eval PNGs from the full HPC pipeline may be uploaded to
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[`hirann/immunoorg-grpo-defender`](https://huggingface.co/hirann/immunoorg-grpo-defender).
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---
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## Quick start
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### Click the live demo
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β https://hirann-immunoorg-v3.hf.space β **βΆ Launch interactive demo**
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### Run the OpenEnv environment locally
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```bash
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git clone https://github.com/Charannoo/immunoorg
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cd immunoorg
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python -m venv .venv && . .venv/Scripts/activate # PowerShell on Windows
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pip install -r requirements.txt
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uvicorn server.main:app --reload --port 7860
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```
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Then visit http://localhost:7860 (landing) or http://localhost:7860/demo (Gradio UI).
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### Train with GRPO (3 paths)
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| Where | When to use | Time |
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| --- | --- | --- |
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| **HPC** (`scripts/hpc/run_all.sh`) | Best evidence: full datasets + SFT + GRPO + 100-ep eval, all chained via SLURM, auto-pushes to HF Hub | ~3-4 hr (1Γ A100) / ~1-1.5 hr (4Γ A100) |
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| **Colab T4** (`ImmunoOrg_Training_Colab.ipynb`) | Free, browser-only, Qwen2.5-3B | ~30-45 min |
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| **Local CPU smoke** (`python -m training.train_grpo --smoke-test`) | Sanity check only | very slow |
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See [`scripts/hpc/HANDOFF.md`](./scripts/hpc/HANDOFF.md) for the friend-facing
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HPC instructions.
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### Run the test suite
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```bash
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pytest tests -q # 32 passed, 1 skipped (live API, only runs when uvicorn is up)
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```
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---
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## OpenEnv API surface
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| Endpoint | Method | Purpose |
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| --- | --- | --- |
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| `/` | GET | Landing page (HTML) with link to /demo |
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| `/demo` | (Gradio) | Interactive visual demo |
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| `/health` | GET | Liveness + version |
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| `/reset` | POST | Start a fresh episode (`{"difficulty": 1, "seed": 42}`) |
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| `/step` | POST | Apply an action (`{"action": {...}}`) |
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| `/state` | GET | Full server-side state (debug / dashboard) |
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| `/directive` | POST | Inject a Board Directive mid-episode |
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| `/trained_status` | GET | Is the trained LoRA loaded yet? |
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| `/openenv.yaml` | GET | Serve the manifest |
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| `/demo` | GET | Gradio: episode demo + **War Room** accordion (Theme #1 LLM debate) |
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| `/api/war-room` | POST | Optional JSON API for the same debate backend |
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| `/admin/training/start` | GET | Kick off GRPO training (token-gated) |
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| `/admin/training/status` | GET | JSON status of the training job |
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| `/admin/training/log` | GET | Tail the training log |
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Action schema lives in [`openenv.yaml`](./openenv.yaml) and matches
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`immunoorg.models.ImmunoAction`.
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---
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## How this maps to the judging criteria
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| Criterion | Weight | Where to look |
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| --- | ---: | --- |
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| **Environment Innovation** | 40% | Socio-technical RL, 5-track reward, War Room, DevSecOps Mesh, 50-step Polymorphic Migration. See [`PROBLEM_STATEMENT.md`](./PROBLEM_STATEMENT.md) Β§1. |
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| **Storytelling** | 30% | Live demo on the Space + [`BLOG_POST.md`](./BLOG_POST.md) + 6 evidence PNGs above + [`VIDEO_SCRIPT.md`](./VIDEO_SCRIPT.md). |
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| **Improvement in Rewards** | 20% | `evidence_*.png` files committed; HPC pipeline produces `evidence_grpo_training.png` + `evidence_eval_per_family.png` from a real Qwen2.5-7B GRPO run. |
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| **Reward & Training Pipeline** | 10% | [`training/train_grpo.py`](./training/train_grpo.py) (3 verifiable reward fns), [`training/dataset_generator.py`](./training/dataset_generator.py) (1700+ scenarios), [`training/scenario_hooks.py`](./training/scenario_hooks.py) (5 elite families), [`scripts/hpc/`](./scripts/hpc/) (full SFTβGRPOβeval pipeline). |
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---
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## Anti-reward-hacking measures (judge guidance Β§7 + Β§21)
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- 3 independent reward functions at the trainer + 5-track composable reward in the env.
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- False-positive isolation penalty (burns half the uptime budget).
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- Phase-gated transitions require *real work*, not step counts.
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- Org friction β tactical spam denied; agent must do strategic work.
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- War-Room hallucination flagging via shared FactStore.
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- Per-step training penalties for ignoring board directives or retrying denied isolations.
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Full details in [`PROBLEM_STATEMENT.md`](./PROBLEM_STATEMENT.md) Β§5c and
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[`RESEARCH.md`](./RESEARCH.md).
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---
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## Status
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- β
OpenEnv: `openenv-core>=0.2.3` (PyPI latest) in Space `requirements.txt` + `openenv.yaml` + HTTP `reset`/`step`/`state`; `import openenv.core` verified at runtime
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- β
Hugging Face Space: https://huggingface.co/spaces/hirann/immunoorg-v3
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- β
Gradio `/demo` includes **War Room** accordion (LLM debate β supports `GROQ_API_KEY`, `OPENAI_API_KEY`, or `ANTHROPIC_API_KEY`)
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- β
2x A10G Hardware: Configured for fast LoRA inference in the live demo.
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- β
Colab + TRL GRPO + Unsloth; `training/train_grpo.py` exports `grpo_log_history.json` for plots
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- β
Evidence PNGs (env rollouts + rewards) committed; add `evidence_grpo_training.png` from Colab or `scripts/plot_grpo_log_history.py`
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- β
Writeups: [`Blog.MD`](./Blog.MD), [`VIDEO_SCRIPT.md`](./VIDEO_SCRIPT.md) β **publish** per [`PUBLISH_HACKATHON.md`](./PUBLISH_HACKATHON.md)
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- β
Training: Logs and scripts shared in [`training/`](./training/) and [`training_logs/`](./training_logs/)
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- β
`python scripts/verify_hackathon_submission.py` for a quick checklist
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Built for the OpenEnv Hackathon (India 2026).
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