feat(benchmark): add support for evaluation on futurex

.gitignore

@@ -208,6 +208,7 @@ marimo/_lsp/
 __marimo__/
 
 logs/
+tmp/
 
 data/*
 !data/README.md

config/benchmark/futurex.yaml

@@ -0,0 +1,20 @@
+# config/benchmark/futurex.yaml
+defaults:
+  - default
+  - _self_
+
+name: "futurex"
+
+data:
+  data_dir: "${data_dir}/futurex"  # Path to your dataset
+  metadata_file: "standardized_data.jsonl"  # Metadata filename
+  whitelist: []  # Optional: List of specific task_ids to run
+
+execution:
+  max_tasks: null      # null = no limit, or specify a number
+  max_concurrent: 5    # Number of parallel tasks
+  pass_at_k: 1         # Number of attempts per task
+
+# Set to skip evaluation since we don't have ground truth
+openai_api_key: "skip_evaluation"
+

docs/mkdocs/docs/download_datasets.md

@@ -79,6 +79,7 @@ uv run main.py prepare-benchmark get browsecomp-test
 uv run main.py prepare-benchmark get browsecomp-zh-test
 uv run main.py prepare-benchmark get hle
 uv run main.py prepare-benchmark get xbench-ds
+uv run main.py prepare-benchmark get futurex
 ```
 
 ### What This Script Does
@@ -94,6 +95,7 @@ uv run main.py prepare-benchmark get xbench-ds
         - `browsecomp-zh-test` - Chinese BrowseComp test set
         - `hle` - HLE dataset
         - `xbench-ds` - xbench-DeepSearch dataset
+        - `futurex` - Futurex-Online dataset
 
 ### Customizing Dataset Selection
 

docs/mkdocs/docs/futurex.md

@@ -0,0 +1,267 @@
+# Futurex-Online
+
+MiroFlow's evaluation on the Futurex-Online benchmark demonstrates capabilities in future event prediction tasks.
+
+---
+
+## Dataset Overview
+
+!!! info "Futurex-Online Dataset"
+    The Futurex-Online dataset consists of 61 prediction tasks covering various future events including:
+
+    - Political events (referendums, elections)
+    - Sports outcomes (football matches)
+    - Legal proceedings
+    - Economic indicators
+
+
+!!! abstract "Key Dataset Characteristics"
+
+    - **Total Tasks**: 61
+    - **Task Type**: Future event prediction
+    - **Answer Format**: Boxed answers (\\boxed{Yes/No} or \\boxed{A/B/C})
+    - **Ground Truth**: Not available (prediction tasks)
+    - **Resolution Date**: Around 2025-09-21 (GMT+8)
+
+---
+
+## Quick Start Guide
+
+!!! note "Quick Start Instructions"
+    This section provides step-by-step instructions to run the Futurex-Online benchmark and prepare submission results. Since this is a prediction dataset without ground truth, we focus on execution traces and response generation. **Note**: This is a quick start guide for running the benchmark, not for reproducing exact submitted results.
+
+### Step 1: Prepare the Futurex-Online Dataset
+
+!!! tip "Dataset Setup"
+    Use the integrated prepare-benchmark command to download and process the dataset:
+
+```bash title="Download Futurex-Online Dataset"
+uv run main.py prepare-benchmark get futurex
+```
+
+This will create the standardized dataset at `data/futurex/standardized_data.jsonl`.
+
+### Step 2: Configure API Keys
+
+!!! warning "API Key Configuration"
+    Set up the required API keys for model access and tool functionality. Update the `.env` file to include the following keys:
+
+```env title=".env Configuration"
+# For searching and web scraping
+SERPER_API_KEY="xxx"
+JINA_API_KEY="xxx"
+
+# For Linux sandbox (code execution environment)
+E2B_API_KEY="xxx"
+
+# We use Claude-3.7-Sonnet with OpenRouter backend to initialize the LLM
+OPENROUTER_API_KEY="xxx"
+OPENROUTER_BASE_URL="https://openrouter.ai/api/v1"
+
+# Used for Claude vision understanding
+ANTHROPIC_API_KEY="xxx"
+
+# Used for Gemini vision
+GEMINI_API_KEY="xxx"
+
+# Use for llm judge, reasoning, o3 hints, etc.
+OPENAI_API_KEY="xxx"
+OPENAI_BASE_URL="https://api.openai.com/v1"
+```
+
+### Step 3: Run the Evaluation
+
+!!! example "Evaluation Execution"
+    Execute the following command to run evaluation on the Futurex-Online dataset. This uses the basic `agent_quickstart_1` configuration for quick start purposes.
+
+```bash title="Run Futurex-Online Evaluation"
+uv run main.py common-benchmark --config_file_name=agent_quickstart_1 benchmark=futurex output_dir="logs/futurex/$(date +"%Y%m%d_%H%M")"
+```
+
+!!! tip "Progress Monitoring and Resume"
+    To check the progress while running:
+
+    ```bash title="Check Progress"
+    uv run utils/progress_check/check_futurex_progress.py $PATH_TO_LOG
+    ```
+
+    If you need to resume an interrupted evaluation, specify the same output directory to continue from where you left off.
+
+    ```bash title="Resume Evaluation, e.g."
+    uv run main.py common-benchmark --config_file_name=agent_quickstart_1 benchmark=futurex output_dir="logs/futurex/20250918_1010"
+    ```
+
+### Step 4: Extract Results
+
+!!! example "Result Extraction"
+    After evaluation completion, extract the results using the provided utility:
+
+```bash title="Extract Results"
+uv run utils/extract_futurex_results.py logs/futurex/$(date +"%Y%m%d_%H%M")
+```
+
+This will generate:
+
+- `futurex_results.json`: Detailed results for each task
+- `futurex_summary.json`: Summary statistics
+- `futurex_predictions.csv`: Predictions in CSV format
+
+---
+
+## Sample Task Examples
+
+### Political Prediction
+```
+Task: "Will the 2025 Guinea referendum pass? (resolved around 2025-09-21 (GMT+8))"
+Expected Format: \boxed{Yes} or \boxed{No}
+```
+
+### Sports Prediction
+```
+Task: "Brighton vs. Tottenham (resolved around 2025-09-21 (GMT+8))
+A. Brighton win on 2025-09-20
+B. Brighton vs. Tottenham end in a draw  
+C. Tottenham win on 2025-09-20"
+Expected Format: \boxed{A}, \boxed{B}, or \boxed{C}
+```
+
+---
+
+## Multiple Runs and Voting
+
+!!! tip "Improving Prediction Accuracy"
+    For better prediction accuracy, you can run multiple evaluations and use voting mechanisms to aggregate results. This approach helps reduce randomness and improve the reliability of predictions. **Note**: This is a quick start approach; production submissions may use more sophisticated configurations.
+
+### Step 1: Run Multiple Evaluations
+
+Use the multiple runs script to execute several independent evaluations:
+
+```bash title="Run Multiple Evaluations"
+./scripts/run_evaluate_multiple_runs_futurex.sh
+```
+
+This script will:
+
+- Run 3 independent evaluations by default (configurable with `NUM_RUNS`)
+- Execute all tasks in parallel for efficiency
+- Generate separate result files for each run in `run_1/`, `run_2/`, etc.
+- Create a consolidated `futurex_submission.jsonl` file with voting results
+
+### Step 2: Customize Multiple Runs
+
+You can customize the evaluation parameters:
+
+```bash title="Custom Multiple Runs"
+# Run 5 evaluations with limited tasks for testing
+NUM_RUNS=5 MAX_TASKS=10 ./scripts/run_evaluate_multiple_runs_futurex.sh
+
+# Use different agent configuration
+AGENT_SET=agent_gaia-validation ./scripts/run_evaluate_multiple_runs_futurex.sh
+
+# Adjust concurrency for resource management
+MAX_CONCURRENT=3 ./scripts/run_evaluate_multiple_runs_futurex.sh
+```
+
+### Step 3: Voting and Aggregation
+
+After multiple runs, the system automatically:
+
+1. **Extracts predictions** from all runs using `utils/extract_futurex_results.py`
+2. **Applies majority voting** to aggregate predictions across runs
+3. **Generates submission file** in the format required by FutureX platform
+4. **Provides voting statistics** showing prediction distribution across runs
+
+The voting process works as follows:
+
+- **Majority Vote**: Most common prediction across all runs wins
+- **Tie-breaking**: If tied, chooses the prediction that appeared earliest across all runs
+- **Vote Counts**: Tracks how many runs predicted each option
+- **Confidence Indicators**: High agreement indicates more reliable predictions
+
+### Step 4: Analyze Voting Results
+
+Check the generated files for voting analysis:
+
+```bash title="Check Voting Results"
+# View submission file with voting results
+cat logs/futurex/agent_quickstart_1_*/futurex_submission.jsonl
+
+# Check individual run results
+ls logs/futurex/agent_quickstart_1_*/run_*/
+
+# Check progress and voting statistics
+uv run python utils/progress_check/check_futurex_progress.py logs/futurex/agent_quickstart_1_*
+```
+
+### Manual Voting Aggregation
+
+You can also manually run the voting aggregation:
+
+```bash title="Manual Voting Aggregation"
+# Aggregate multiple runs with majority voting
+uv run python utils/extract_futurex_results.py logs/futurex/agent_quickstart_1_* --aggregate
+
+# Force single run mode (if needed)
+uv run python utils/extract_futurex_results.py logs/futurex/agent_quickstart_1_*/run_1 --single
+
+# Specify custom output file
+uv run python utils/extract_futurex_results.py logs/futurex/agent_quickstart_1_* -o my_voted_predictions.jsonl
+```
+
+### Voting Output Format
+
+The voting aggregation generates a submission file with the following format:
+
+```json
+{"id": "687104310a994c0060ef87a9", "prediction": "No", "vote_counts": {"No": 2}}
+{"id": "68a9b46e961bd3003c8f006b", "prediction": "Yes", "vote_counts": {"Yes": 2}}
+```
+
+The output includes:
+
+- **`id`**: Task identifier
+- **`prediction`**: Final voted prediction (without `\boxed{}` wrapper)
+- **`vote_counts`**: Dictionary showing how many runs predicted each option
+
+For example, `"vote_counts": {"No": 2}` means 2 out of 2 runs predicted "No", indicating high confidence.
+
+---
+
+## Evaluation Notes
+
+!!! warning "No Ground Truth Available"
+    Since Futurex-Online is a prediction dataset, there are no ground truth answers available for evaluation. The focus is on:
+
+    - Response generation quality
+    - Reasoning process documentation
+    - Prediction confidence and methodology
+
+!!! info "Output Analysis"
+    The evaluation generates detailed execution traces showing:
+
+    - Research process for each prediction
+    - Information gathering from web sources
+    - Reasoning chains leading to predictions
+    - Final boxed answers in required format
+
+### Directory Structure
+
+After running multiple evaluations, you'll find the following structure:
+
+```
+logs/futurex/agent_quickstart_1_YYYYMMDD_HHMM/
+├── futurex_submission.jsonl          # Final voted predictions
+├── run_1/                            # First run results
+│   ├── benchmark_results.jsonl       # Individual task results
+│   ├── benchmark_results_pass_at_1_accuracy.txt
+│   └── task_*_attempt_1.json        # Detailed execution traces
+├── run_2/                            # Second run results
+│   └── ... (same structure as run_1)
+├── run_1_output.log                  # Run 1 execution log
+└── run_2_output.log                  # Run 2 execution log
+```
+
+---
+
+!!! info "Documentation Info"
+    **Last Updated:** September 2025 · **Doc Contributor:** Team @ MiroMind AI

docs/mkdocs/mkdocs.yml

@@ -52,6 +52,7 @@ nav:
     - Benchmarks: 
       - GAIA-Validation: gaia_validation.md
       - GAIA-Test: gaia_test.md
+      - FutureX: futurex.md
     - Add New Benchmarks: contribute_benchmarks.md
 
   - Tools: