feat: initial structure

Dockerfile

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+FROM python:3.11-slim
+WORKDIR /app
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+COPY . .
+RUN python -c "from data.loader import build_fact_database; build_fact_database()" || true
+EXPOSE 7860
+CMD ["uvicorn", "environment.server:app", "--host", "0.0.0.0", "--port", "7860"]

openenv.yaml

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+name: context-corruption-env
+version: "1.0.0"
+description: >
+  OpenEnv environment for training epistemic robustness in LLMs.
+  Agents identify correct answers and flag corrupted documents
+  in a multi-doc QA setting with verifiable, objective rewards.
+author: "Your Team Name"
+license: MIT
+
+environment:
+  entrypoint: "environment.server:app"
+  action_schema: "environment.actions.ContextCorruptionAction"
+  observation_schema: "environment.actions.EpisodeObservation"
+  max_concurrent_sessions: 64
+
+reward:
+  type: "objective"
+  range: [-0.5, 1.05]
+  components:
+    - {name: answer_correctness,      weight: 0.4}
+    - {name: corruption_detection,    weight: 0.3}
+    - {name: false_positive_penalty,  weight: 0.2}
+    - {name: confidence_calibration,  weight: 0.1}
+
+datasets:
+  - {name: Natural Questions, url: "https://huggingface.co/datasets/google-research-datasets/natural_questions"}
+  - {name: PopQA,             url: "https://huggingface.co/datasets/akariasai/PopQA"}
+  - {name: FaithEval,         url: "https://github.com/SalesforceAIResearch/FaithEval"}
+
+citation: |
+  @misc{contextcorruption2026,
+    title={ContextCorruption-Env: Training Epistemic Robustness in LLMs},
+    year={2026},
+    note={OpenEnv Hackathon Submission}
+  }