Update README.md

README.md

@@ -18,14 +18,6 @@ Define your criteria in Python, and our SDK treats real-world outcomes as the la
 
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-## How It Works
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-We generate grounded, model-ready training data from documents or public sources (Google News, SEC filings, market data). You define your criteria in Python, and our SDK uses the **future as the label** — turning messy, timestamped history into training signal automatically. No labeling pipelines, no extraction, no human annotation.
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-This approach has been used to beat frontier AIs 100x larger on prediction-market benchmarks, and has demonstrated success in financial forecasting, risk estimation, and policy prediction.
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 ## Research & Results
 
 - **[SEC Risk Prediction](https://arxiv.org/abs/2601.19189)**: Foresight learning on raw SEC filings trains a 32B model to outperform GPT-5 at predicting public company risks.
@@ -34,12 +26,3 @@ This approach has been used to beat frontier AIs 100x larger on prediction-marke
 - **[Foresight-32B vs. Frontier LLMs](https://blog.lightningrod.ai/p/foresight-32b-beats-frontier-llms-on-live-polymarket-predictions)**: Live demonstration beating frontier models on Polymarket predictions.
 
 Foresight-32B is consistently top-ranked on [ForecastBench](https://www.forecastbench.org/tournament/) and [ProphetArena Sports](https://www.prophetarena.co/leaderboard).
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-## Models & Datasets
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-| Resource | Description |
-|----------|-------------|
-| [Trump-Forecaster](https://huggingface.co/LightningRodLabs/Trump-Forecaster) | RL-tuned gpt-oss-120b LoRA adapter for predicting Trump administration actions. Beats GPT-5 (Brier 0.194 vs 0.200). |
-| [WWTD-2025](https://huggingface.co/datasets/LightningRodLabs/WWTD-2025) | 2,790 binary forecasting questions about U.S. policy under the Trump administration, with news context and ground-truth resolutions. |