{
  "lesson": "05-autonomous-research-agent",
  "title": "Capstone 05 — Autonomous Research Agent (AI-Scientist Class)",
  "questions": [
    {
      "stage": "pre",
      "question": "What search shape does the AI-Scientist-class agent use to explore experiments?",
      "options": [
        "Breadth-first expansion with random scoring",
        "Beam search over token outputs",
        "Pure reinforcement learning from human feedback",
        "Best-first tree search over experiment nodes with a novelty x quality x budget score"
      ],
      "correct": 3,
      "explanation": ""
    },
    {
      "stage": "pre",
      "question": "Why is the sandbox configured with --network=none and bounded resource caps?",
      "options": [
        "To force the agent to use prompt caching",
        "To prevent network egress and contain experiment side effects within a reproducible envelope",
        "To enforce deterministic floating-point arithmetic",
        "To allow GPU passthrough by default"
      ],
      "correct": 1,
      "explanation": ""
    },
    {
      "stage": "check",
      "question": "What is the role of the vision critique step in the writer loop?",
      "options": [
        "Generates new experiment ideas from screenshots",
        "Replaces matplotlib at render time",
        "Translates figures into bar charts",
        "Compiles the LaTeX draft to PDF, then has a VLM critique layout, figure legibility, and claim-evidence alignment"
      ],
      "correct": 3,
      "explanation": ""
    },
    {
      "stage": "check",
      "question": "How does the reviewer ensemble gate the pipeline?",
      "options": [
        "Reviewers vote anonymously and the majority wins",
        "Five judges score on NeurIPS-style rubrics and the weighted aggregate must clear a threshold, otherwise the draft loops back to the writer",
        "A single judge accepts or rejects on a binary flag",
        "Reviews run after publication only"
      ],
      "correct": 1,
      "explanation": ""
    },
    {
      "stage": "check",
      "question": "Which cost discipline does the capstone enforce per paper?",
      "options": [
        "GPU-hours tracked but never capped",
        "A $30 hard budget tracked through Langfuse counters and pre-run estimates",
        "Cost-only optimization without quality checks",
        "Unbounded compute, hard wall-clock only"
      ],
      "correct": 1,
      "explanation": ""
    },
    {
      "stage": "post",
      "question": "Which scoring function ranks tree nodes for further expansion?",
      "options": [
        "Citation count of related papers",
        "Random uniform priority",
        "Output length and token count",
        "Novelty x quality x remaining budget"
      ],
      "correct": 3,
      "explanation": ""
    },
    {
      "stage": "post",
      "question": "What does the red-team report exercise against the system?",
      "options": [
        "Sandbox-escape attempts such as fork bombs, network exfiltration, and filesystem escapes",
        "Caching hit rate on system prompts",
        "Multi-tenant data leakage in the vector DB",
        "Latency tail under packet loss"
      ],
      "correct": 0,
      "explanation": ""
    }
  ]
}