Update README

README.md

@@ -39,8 +39,8 @@ The dataset is shipped as **a single SQLite database file** named
 * `dataset_metadata` — a one-row table holding the canonical prompt-reconstruction recipe
   (prompt template, output formats, agent role, answer-encoding rules, provenance).
 
-The dataset is designed to be the **dataset $`\mathcal{D}`$** in the OracleProto run unit
-$`\mathcal{R} = (\mathcal{D}, M, \kappa_M, \delta, T, C, R, \Psi, \phi, \Gamma)`$: every column,
+The dataset is designed to be the **dataset $\mathcal{D}$** in the OracleProto run unit
+$\mathcal{R} = (\mathcal{D}, M, \kappa_M, \delta, T, C, R, \Psi, \phi, \Gamma)$: every column,
 prompt template, and answer-encoding rule below is byte-stable and round-trip parsed by the
 reference parser, so a forecasting run on this set is auditable, replayable, and comparable
 across models and across calendar years.
@@ -334,7 +334,7 @@ The rules are:
    parsed from `answer`. A missing or unparsed boxed answer is recorded as `parse_ok = 0`
    and is **not** an error of the parser — the run records it and moves on.
 
-> The parser is the formal answer-validator $`\Psi`$ in the OracleProto run unit. Re-using
+> The parser is the formal answer-validator $\Psi$ in the OracleProto run unit. Re-using
 > it (rather than rolling your own regex) is the easiest way to get bit-identical scores
 > across implementations.
 
@@ -456,16 +456,16 @@ This dataset is meant to be paired with the **OracleProto** evaluation harness,
 information-boundary discipline on top of the bare prompt-and-score loop. The headline
 recommendations are:
 
-1. **Declare a knowledge cutoff $`\kappa_M`$ for every model.** OracleProto admits a question
-   for model $`M`$ only when its prediction cutoff $`\chi_i`$ satisfies
-   $`\kappa_M \le \chi_i < \tau_i`$, where $`\tau_i`$ is the resolution time. Inadmissible
+1. **Declare a knowledge cutoff $\kappa_M$ for every model.** OracleProto admits a question
+   for model $M$ only when its prediction cutoff $\chi_i$ satisfies
+   $\kappa_M \le \chi_i < \tau_i$, where $\tau_i$ is the resolution time. Inadmissible
    questions are filtered upstream (not counted as model errors). This separates *"the model
    failed to forecast"* from *"the model already knew the answer"*. Models with no declared
    cutoff cannot be fairly compared to those with one.
 
 2. **Time-mask any retrieval / browsing tool.** If your harness lets the model issue web
-   searches (e.g. via Tavily), pin the search-side `end_date` to $`\chi_i + \delta`$ with a
-   conservative offset (OracleProto defaults to $`\delta = -1`$ day). This is the L2
+   searches (e.g. via Tavily), pin the search-side `end_date` to $\chi_i + \delta$ with a
+   conservative offset (OracleProto defaults to $\delta = -1$ day). This is the L2
    "tool-mediated" leakage barrier.
 
 3. **Run an independent retrieval-content auditor.** Each retrieved snippet is passed to a
@@ -552,7 +552,7 @@ ability**: any model that can browse the open web or that was trained past a que
 * **Provider-side residual leakage (L4 channel).** Any LLM that has ingested the upstream
   HuggingFace dataset, or that was trained past the resolution window, can recover ground
   truths from parametric memory. The dataset cannot patch this on its own — it relies on the
-  harness to enforce admissibility ($`\kappa_M`$).
+  harness to enforce admissibility ($\kappa_M$).
 * **Snapshot of a moving label space.** A few questions ("none of the above", "all of the
   above") interact non-trivially with multi-select scoring; the curation pass fixed the one
   S&P 500 case but the convention for similar questions in future revisions may shift. Pin