Publish CC-BY-4.0 License and Use section per Terms §13

README.md

@@ -1,132 +1,43 @@
 ---
-license: mit
-task_categories:
-  - question-answering
+license: cc-by-4.0
 language:
-  - en
+- en
+pretty_name: World Awareness Bench
 tags:
-  - benchmark
-  - evaluation
-  - world-awareness
-  - prediction-markets
-  - ai-agents
-  - calibrated-probabilities
-  - real-time-data
-  - geopolitics
-  - economics
-size_categories:
-  - n<1K
-pretty_name: World Awareness Benchmark
+- benchmark
+- evaluation
+- prediction-markets
+- world-knowledge
+- llm-eval
+source_datasets:
+- original
 ---
 
-# World Awareness Benchmark
+# World Awareness Bench
 
-Does your AI agent know what's happening in the world *right now*?
+Monthly AI-agent benchmark measuring whether language models correctly reflect the current state of the world as priced by prediction markets. 100 questions/month covering politics, geopolitics, macro, and events. Graded against market-consensus ground truth.
 
-This benchmark tests whether agents can accurately answer factual questions about current events — recession probability, geopolitical risk, oil prices, election odds. Ground truth comes from prediction markets, where participants risk real money on outcomes.
+## License and Use
 
-## Why This Benchmark Exists
+This dataset is released under Creative Commons Attribution 4.0 International
+(CC-BY-4.0; https://creativecommons.org/licenses/by/4.0/). You may use it
+freely for personal, research, educational, and commercial purposes — including
+training, evaluating, and fine-tuning machine-learning models. Attribution is
+required when the dataset is redistributed in substantially its original form
+or cited in published work; credit as "SimpleFunctions (simplefunctions.dev)".
 
-LLMs have a knowledge cutoff. When asked "what's the probability of a US recession?", they either:
-- Hallucinate a number
-- Hedge with "I don't have access to real-time data"
-- Give a stale figure from training data
+Additional terms apply: you may not re-host this dataset, in whole or in
+substantial part, as an API or service that functionally substitutes for a
+SimpleFunctions endpoint. See Terms §13.2 at https://simplefunctions.dev/terms.
 
-There was no standard way to measure this gap. Now there is.
+Provenance, update cadence, and schema are documented below.
 
-## Format
+## Update cadence
 
-```json
-{
-  "id": "geo-001",
-  "question": "What is the SF Geopolitical Risk Index score (0-100)?",
-  "category": "Geopolitical",
-  "ground_truth": 62,
-  "unit": "score_0_100",
-  "source": "SF Index",
-  "tolerance": 5,
-  "date": "2026-04-02"
-}
-```
+Monthly, 1st of month.
 
-## Categories
+## Provenance
 
-| Category | Questions | What it tests |
-|----------|-----------|---------------|
-| Geopolitical | 10 | War probabilities, nuclear risk, regional conflicts |
-| Economy | 10 | Recession, Fed rates, market indices, treasury yields |
-| Energy | 7 | Oil prices, energy contracts, supply disruption |
-| Elections | 5 | Presidential, senate, political outcomes |
-| Markets | 12 | Crypto, tech, gold, mispriced edges |
-
-## Scoring
-
-| Result | Points |
-|--------|--------|
-| Exact match (within tolerance) | 2 |
-| Correct direction/range | 1 |
-| Wrong, hallucinated, or refused | 0 |
-
-**Max score**: questions × 2
-
-## How to Use
-
-```python
-import json
-import requests
-
-# Load benchmark
-bench = json.load(open("benchmark_2026-04-02.json"))
-
-# Test your agent
-score = 0
-for q in bench["questions"]:
-    answer = your_agent(q["question"])  # your agent's response
-    # Parse numeric answer and compare to ground_truth with tolerance
-    # ...
-
-print(f"World Awareness Score: {score}/{bench['scoring']['max_score']}")
-```
-
-### With SimpleFunctions (baseline)
-
-An agent using SimpleFunctions world state should score near-perfect:
-
-```python
-from simplefunctions import world
-
-state = world()
-# Inject into system prompt, then answer questions
-# Expected score: 80-90% of max
-```
-
-### Without any world context (baseline)
-
-A vanilla LLM with no real-time data:
-```python
-# Expected score: 0-10% of max (hallucinations occasionally correct by chance)
-```
-
-## Monthly Updates
-
-This benchmark is regenerated monthly from live prediction market data. Each file is dated (`benchmark_YYYY-MM-DD.json`). Ground truth changes as the world changes — that's the point.
-
-## Data Source
-
-[SimpleFunctions](https://simplefunctions.dev/world) — 9,706 prediction market contracts from Kalshi (CFTC-regulated) and Polymarket. Calibrated by real money.
-
-## Citation
-
-```bibtex
-@dataset{simplefunctions_world_awareness_2026,
-  title={World Awareness Benchmark: Testing AI Agent Knowledge of Current Events},
-  author={SimpleFunctions},
-  year={2026},
-  url={https://huggingface.co/datasets/SimpleFunctions/world-awareness-bench},
-  note={Ground truth from 9,706 prediction markets. Updated monthly.}
-}
-```
-
-## License
-
-MIT
+Source: https://simplefunctions.dev
+Generator: SimpleFunctions public data pipeline
+Contact: patrick@simplefunctions.dev