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README.md

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-title: README
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-sdk: static
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+title: Lightning Rod Labs
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-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Lightning Rod Labs
+
+**Train with Timestamps, Not Labels.**
+
+Lightning Rod Labs automatically generates high-quality training data from your documents or public sources — no labeling or extraction required. Define your criteria in Python, and our SDK treats real-world outcomes as the label, producing high-signal supervision at scale. Models learn causal factors, not just tokens. Raw data to deployable specialized models in hours.
+
+[Website](https://lightningrod.ai/) · [SDK](https://github.com/lightning-rod-labs/lightningrod-python-sdk) · [Blog](https://blog.lightningrod.ai/)
+
+---
+
+## How It Works
+
+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.
+
+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.
+
+---
+
+## 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.
+- **[Future-as-Label](https://arxiv.org/abs/2601.06336)**: AI learns directly from raw chronological news data at unlimited scale, no human annotation.
+- **[Outcome-based RL](https://arxiv.org/abs/2505.17989)** (TMLR): Using RL to improve LLM forecasting ability from real-world outcomes.
+- **[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).
+
+---
+
+## Models & Datasets
+
+| 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. |