{
  "sources": [
    {
      "url": "https://raw.githubusercontent.com/gtmcknight/amm-sim/e65314f88de945f6d055bfc2aab01c482ab6570e/research/solved/README.md",
      "quote": "A policy sees what afterSwap sees. `run.py` enforces it; a result from any other harness needs the same blindness."
    },
    {
      "url": "https://raw.githubusercontent.com/gtmcknight/amm-sim/e65314f88de945f6d055bfc2aab01c482ab6570e/research/solved/dp.py",
      "quote": "Average-reward relative value iteration; one sweep = one step of lookahead."
    },
    {
      "url": "https://raw.githubusercontent.com/gtmcknight/amm-sim/e65314f88de945f6d055bfc2aab01c482ab6570e/research/solved/qmdp.py",
      "quote": "(a, b)* = argmax  sum_e  bel(e) * V_N[phase](w - e, w2(e), a - e, b + e)"
    },
    {
      "url": "https://api.imd.fun/artifacts/2640642f10bc7e12b7629ad9fe0028eb4cb7062fbf0838b7b1e8573fca4ac038",
      "quote": "Average held-action passive VN values over the full price posterior and branch-conditioned phase/rival state, maximize once, then use original parabolic refinement and exponential fee conversion."
    },
    {
      "url": "https://api.imd.fun/artifacts/c9d678abb0ed0ee3a31b13a8203b6ee993896db0b6a3875c5c4f52a369bf0434",
      "quote": "\"key\": \"fresh5-1\""
    }
  ]
}