bMSnvqVWaB/source_audit.json

{
  "openreview_id": "bMSnvqVWaB",
  "arxiv": "2606.12691",
  "claims": [
    {
      "claim": 1,
      "source_status": "confirmed",
      "source_page": 5,
      "anchor": "Theorem 1 (Main result - Informal)",
      "note": "The source states latent-state recovery up to a similarity transform for the hidden representation of the two-layer autoregressive model."
    },
    {
      "claim": 2,
      "source_status": "confirmed",
      "source_page": 8,
      "anchor": "Theorem 4",
      "note": "The formal theorem provides the finite-sample latent-recovery bound up to a similarity transform."
    },
    {
      "claim": 3,
      "source_status": "confirmed",
      "source_page": 7,
      "anchor": "Theorem 2",
      "note": "The in-sample prediction bound has the registered H/T leading dependence, with dimensional and logarithmic factors."
    },
    {
      "claim": 4,
      "source_status": "confirmed",
      "source_page": 8,
      "anchor": "Theorem 3",
      "note": "The theorem bounds the Frobenius parameter error with inverse-T leading dependence."
    },
    {
      "claim": 5,
      "source_status": "confirmed",
      "source_page": 6,
      "anchor": "Proposition 1",
      "note": "The finite-history Kalman approximation has squared error proportional to rho^(2L), hence exponential decay with history."
    },
    {
      "claim": 6,
      "source_status": "confirmed",
      "source_page": 10,
      "anchor": "Table 1; Figure 2; Table 2",
      "note": "Table 1 reports h=4 as the lowest mean training-loss grid point and Table 2 reports mean R-squared values from 0.979 to 0.999. The clean-room unregularized rank proxy did not reproduce the unique h=4 optimum, so that proxy result is a failed replication rather than a source contradiction."
    }
  ]
}