<p align="center">
<img src="assets/crucible_framework.svg" alt="Crucible Framework" width="150"/>
</p>
# CrucibleFramework
**A Scientifically-Rigorous Infrastructure for LLM Reliability and Performance Research**
[](https://elixir-lang.org)
[](https://www.erlang.org)
[](https://github.com/North-Shore-AI/crucible_framework/blob/main/LICENSE)
[](https://hexdocs.pm/crucible_framework)
---
## Overview
The Crucible Framework is a comprehensive infrastructure for conducting **reproducible, statistically-rigorous experiments** on large language model (LLM) reliability, performance optimization, and cost-accuracy trade-offs. Built on Elixir/OTP, it leverages the BEAM virtual machine's strengths in concurrency, fault tolerance, and distributed computing to enable research at scale.
**Target Audience:**
- PhD students and researchers studying LLM reliability
- ML engineers requiring rigorous experimental evaluation
- Research labs investigating AI system optimization
- Anyone needing publication-quality experimental infrastructure
**Key Features:**
- **Multi-model ensemble voting** with 4 voting strategies
- **Request hedging** for tail latency reduction (50-75% P99 improvement)
- **Statistical testing** with 15+ tests (parametric & non-parametric)
- **Research-grade instrumentation** with complete event capture
- **Causal transparency** for LLM decision provenance
- **Automated experiment orchestration** with checkpointing
- **Multi-format reporting** (Markdown, LaTeX, HTML, Jupyter)
---
## Quick Start
### Installation
```bash
# Clone repository
git clone https://github.com/North-Shore-AI/crucible_framework.git
cd crucible_framework
# Install dependencies
mix deps.get
# Compile
mix compile
# Run tests
mix test
```
### Your First Experiment
```elixir
defmodule MyFirstExperiment do
use ResearchHarness.Experiment
# Define experiment
name "Ensemble vs Single Model"
dataset :mmlu_stem, sample_size: 100
conditions [
%{name: "baseline", fn: &baseline/1},
%{name: "ensemble", fn: &ensemble/1}
]
metrics [:accuracy, :cost_per_query, :latency_p99]
repeat 3
# Baseline: Single model
def baseline(query) do
# Your single-model implementation
%{prediction: "answer", latency: 800, cost: 0.01}
end
# Treatment: 3-model ensemble
def ensemble(query) do
{:ok, result} = Ensemble.predict(query,
models: [:gpt4_mini, :claude_haiku, :gemini_flash],
strategy: :majority
)
%{
prediction: result.answer,
latency: result.metadata.latency_ms,
cost: result.metadata.cost_usd
}
end
end
# Run experiment
{:ok, report} = ResearchHarness.run(MyFirstExperiment, output_dir: "results")
# Results saved to:
# - results/exp_abc123_report.md
# - results/exp_abc123_results.csv
# - results/exp_abc123_analysis.json
```
**Output:**
```markdown
# Experiment Results
## Summary
- Ensemble accuracy: 96.3% (±1.2%)
- Baseline accuracy: 89.1% (±2.1%)
- Improvement: +7.2 percentage points
- Statistical significance: p < 0.001, d = 3.42
- Cost increase: 3.0× ($0.03 vs $0.01)
## Conclusion
Ensemble significantly outperforms baseline with very large effect size.
Cost-accuracy ratio: $0.29 per percentage point improvement.
```
---
## Architecture
The framework consists of **6 layers** organized as **8 independent OTP applications**:
```mermaid
graph TB
subgraph "Layer 6: Orchestration"
RH[ResearchHarness<br/>Experiment DSL]
end
subgraph "Layer 5: Analysis & Reporting"
BENCH[Bench<br/>Statistical Tests]
TEL[TelemetryResearch<br/>Metrics]
REP[Reporter<br/>Multi-Format]
end
subgraph "Layer 4: Reliability Strategies"
ENS[Ensemble<br/>Multi-Model Voting]
HEDGE[Hedging<br/>Latency Reduction]
end
subgraph "Layer 3: Transparency"
CT[CausalTrace<br/>Decision Provenance]
end
subgraph "Layer 2: Data Management"
DS[DatasetManager<br/>Benchmarks]
end
subgraph "Layer 1: Foundation"
OTP[Elixir/OTP Runtime]
end
RH --> BENCH
RH --> TEL
RH --> ENS
RH --> HEDGE
RH --> DS
ENS --> CT
BENCH --> OTP
TEL --> OTP
ENS --> OTP
HEDGE --> OTP
DS --> OTP
```
**See [ARCHITECTURE.md](./ARCHITECTURE.md) for complete system design.**
---
## Core Libraries
### Ensemble: Multi-Model Voting
Increase reliability by querying multiple models and aggregating responses.
```elixir
{:ok, result} = Ensemble.predict("What is 2+2?",
models: [:gpt4, :claude, :gemini],
strategy: :majority,
execution: :parallel
)
result.answer # => "4"
result.metadata.consensus # => 1.0 (100% agreement)
result.metadata.cost_usd # => 0.045
```
**Voting Strategies:**
- `:majority` - Most common answer wins (default)
- `:weighted` - Weight by confidence scores
- `:best_confidence` - Highest confidence answer
- `:unanimous` - All must agree
**Execution Strategies:**
- `:parallel` - All models simultaneously (fastest)
- `:sequential` - One at a time until consensus (cheapest)
- `:hedged` - Primary with backups (balanced)
- `:cascade` - Fast/cheap → slow/expensive
**Expected Results:**
- **Reliability:** 96-99% accuracy (vs 89-92% single model)
- **Cost:** 3-5× single model cost
- **Latency:** = slowest model in parallel mode
**See [ENSEMBLE_GUIDE.md](./ENSEMBLE_GUIDE.md) for deep dive.**
### Hedging: Tail Latency Reduction
Reduce P99 latency by sending backup requests after a delay.
```elixir
Hedging.request(fn ->
call_api()
end,
strategy: :percentile,
percentile: 95,
enable_cancellation: true
)
```
**Strategies:**
- `:fixed` - Fixed delay (e.g., 100ms)
- `:percentile` - Delay = Pth percentile latency
- `:adaptive` - Learn optimal delay over time
- `:workload_aware` - Different delays per workload type
**Expected Results:**
- **P99 latency:** 50-75% reduction
- **Cost:** 5-15% increase (hedge fires ~10% of time)
- **Based on:** Google's "Tail at Scale" research
**See [HEDGING_GUIDE.md](./HEDGING_GUIDE.md) for theory and practice.**
### Bench: Statistical Testing
Rigorous statistical analysis for publication-quality results.
```elixir
control = [0.89, 0.87, 0.90, 0.88, 0.91]
treatment = [0.96, 0.97, 0.94, 0.95, 0.98]
result = Bench.compare(control, treatment)
```
**Output:**
```elixir
%Bench.Result{
test: :welch_t_test,
p_value: 0.00012,
effect_size: %{cohens_d: 4.52, interpretation: "very large"},
confidence_interval: %{interval: {0.051, 0.089}, level: 0.95},
interpretation: """
Treatment group shows significantly higher accuracy (M=0.96, SD=0.015)
compared to control (M=0.89, SD=0.015), t(7.98)=8.45, p<0.001, d=4.52.
"""
}
```
**Features:**
- **15+ statistical tests:** t-tests, ANOVA, Mann-Whitney, Wilcoxon, Kruskal-Wallis
- **Automatic test selection:** Checks assumptions, selects appropriate test
- **Effect sizes:** Cohen's d, η², ω², odds ratios
- **Power analysis:** A priori and post-hoc
- **Multiple comparison correction:** Bonferroni, Holm, Benjamini-Hochberg
**See [STATISTICAL_TESTING.md](./STATISTICAL_TESTING.md) for complete guide.**
### TelemetryResearch: Instrumentation
Complete event capture for reproducible research.
```elixir
# Start experiment
{:ok, exp} = TelemetryResearch.start_experiment(
name: "ensemble_vs_single",
condition: "treatment",
tags: ["accuracy", "h1"]
)
# All events automatically captured
# Stop and export
{:ok, exp} = TelemetryResearch.stop_experiment(exp.id)
{:ok, path} = TelemetryResearch.export(exp.id, :csv)
```
**Features:**
- **Experiment isolation:** Multiple concurrent experiments, no cross-contamination
- **Event enrichment:** Automatic metadata (timestamp, experiment context, process info)
- **Storage backends:** ETS (in-memory) or PostgreSQL (persistent)
- **Export formats:** CSV, JSON Lines, Parquet
- **Metrics calculation:** Latency percentiles, cost, reliability
**See [INSTRUMENTATION.md](./INSTRUMENTATION.md) for complete guide.**
### DatasetManager: Benchmark Datasets
Unified interface to standard benchmarks with caching.
```elixir
# Load dataset
{:ok, dataset} = DatasetManager.load(:mmlu_stem, sample_size: 200)
# Evaluate predictions
{:ok, results} = DatasetManager.evaluate(predictions,
dataset: dataset,
metrics: [:exact_match, :f1]
)
results.accuracy # => 0.96
```
**Supported Datasets:**
- **MMLU:** 15,908 questions across 57 subjects
- **HumanEval:** 164 Python programming problems
- **GSM8K:** 8,500 grade school math problems
- **Custom:** Load from JSONL files
**Features:**
- **Automatic caching:** First load downloads, subsequent loads from cache
- **Version tracking:** Dataset versions locked for reproducibility
- **Multiple metrics:** Exact match, F1, BLEU, CodeBLEU
- **Sampling:** Random, stratified, k-fold cross-validation
**See [DATASETS.md](./DATASETS.md) for details.**
### CausalTrace: Decision Provenance
Capture and visualize LLM decision-making for transparency.
```elixir
# Parse LLM output with event tags
{:ok, chain} = CausalTrace.parse_llm_output(llm_response, "Task Name")
# Save and visualize
CausalTrace.save(chain)
CausalTrace.open_visualization(chain)
```
**Event Types:**
- Task decomposition, hypothesis formation, pattern application
- Alternative consideration, constraint identification
- Decision making, uncertainty flagging
**Features:**
- **Interactive HTML visualization:** Timeline, alternatives, confidence levels
- **Storage:** JSON format on disk
- **Search:** Query chains by criteria
- **Export:** Markdown, CSV for analysis
**Use Cases:**
- Debugging LLM code generation
- User trust studies
- Model comparison
- Prompt engineering
**See [CAUSAL_TRANSPARENCY.md](./CAUSAL_TRANSPARENCY.md) for details.**
### ResearchHarness: Experiment Orchestration
High-level DSL for defining and running complete experiments.
```elixir
defmodule MyExperiment do
use ResearchHarness.Experiment
name "Hypothesis 1: Ensemble Reliability"
dataset :mmlu_stem, sample_size: 200
conditions [
%{name: "baseline", fn: &baseline/1},
%{name: "ensemble", fn: &ensemble/1}
]
metrics [:accuracy, :latency_p99, :cost_per_query]
repeat 3
seed 42 # For reproducibility
end
{:ok, report} = ResearchHarness.run(MyExperiment)
```
**Features:**
- **Declarative DSL:** Define experiments clearly
- **Automatic execution:** Parallel processing with GenStage
- **Fault tolerance:** Checkpointing every N queries
- **Cost estimation:** Preview before running
- **Statistical analysis:** Automatic with Bench
- **Multi-format reports:** Markdown, LaTeX, HTML, Jupyter
---
## Research Methodology
The framework supports rigorous experimental research with **6 core hypotheses**:
### H1: Ensemble Reliability (Primary)
> A 5-model ensemble achieves ≥99% accuracy on MMLU-STEM, significantly higher than best single model (≤92%).
**Design:** Randomized controlled trial
- Control: GPT-4 single model
- Treatment: 5-model majority vote ensemble
- n=200 queries, 3 repetitions
**Analysis:** Independent t-test, Cohen's d effect size
**Expected Result:** d > 1.0 (very large effect)
### H2: Hedging Latency Reduction (Primary)
> Request hedging reduces P99 latency by ≥50% with <15% cost increase.
**Design:** Paired comparison
- Baseline vs P95 hedging
- n=1000 API calls, 3 repetitions
**Analysis:** Paired t-test on log-transformed latencies
**Expected Result:** 60% P99 reduction, 10% cost increase
### H3-H6: See [RESEARCH_METHODOLOGY.md](./RESEARCH_METHODOLOGY.md)
**Complete methodology includes:**
- Experimental designs (factorial, repeated measures, time series)
- Statistical methods (parametric & non-parametric tests)
- Power analysis for sample size determination
- Multiple comparison correction
- Reproducibility protocols
- Publication guidelines
---
## Installation & Setup
### Prerequisites
- **Elixir:** 1.14 or higher
- **Erlang/OTP:** 25 or higher
- **PostgreSQL:** 14+ (optional, for persistent telemetry storage)
### Install Elixir
**macOS:**
```bash
brew install elixir
```
**Ubuntu/Debian:**
```bash
wget https://packages.erlang-solutions.com/erlang-solutions_2.0_all.deb
sudo dpkg -i erlang-solutions_2.0_all.deb
sudo apt-get update
sudo apt-get install elixir
```
**From source:**
```bash
git clone https://github.com/elixir-lang/elixir.git
cd elixir
make clean test
```
### Framework Installation
```bash
# Clone repository
git clone https://github.com/North-Shore-AI/crucible_framework.git
cd crucible_framework
# Install dependencies
mix deps.get
# Compile all apps
mix compile
# Run tests
mix test
# Generate documentation
mix docs
# View docs
open doc/index.html
```
### Configuration
Create `config/config.exs`:
```elixir
import Config
# API Keys
config :ensemble,
openai_api_key: System.get_env("OPENAI_API_KEY"),
anthropic_api_key: System.get_env("ANTHROPIC_API_KEY"),
google_api_key: System.get_env("GOOGLE_API_KEY")
# Dataset caching
config :dataset_manager,
cache_dir: "~/.cache/crucible_framework/datasets"
# Telemetry storage
config :telemetry_research,
storage_backend: :ets # or :postgres
# Research harness
config :research_harness,
checkpoint_dir: "./checkpoints",
results_dir: "./results"
```
### Set API Keys
```bash
export OPENAI_API_KEY="sk-..."
export ANTHROPIC_API_KEY="sk-ant-..."
export GOOGLE_API_KEY="..."
```
Or create `.env` file and use `dotenv`:
```bash
# .env
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...
GOOGLE_API_KEY=...
```
---
## Usage Examples
### Example 1: Simple Ensemble Comparison
```elixir
# Test if 3-model ensemble beats single model
alias Ensemble
alias DatasetManager
# Load dataset
{:ok, dataset} = DatasetManager.load(:mmlu_stem, sample_size: 100)
# Run baseline (single model)
baseline_results =
Enum.map(dataset.items, fn item ->
# Call GPT-4
{:ok, answer} = call_gpt4(item.input)
%{predicted: answer, expected: item.expected}
end)
# Run ensemble (3 models)
ensemble_results =
Enum.map(dataset.items, fn item ->
{:ok, result} = Ensemble.predict(item.input,
models: [:gpt4_mini, :claude_haiku, :gemini_flash],
strategy: :majority
)
%{predicted: result.answer, expected: item.expected}
end)
# Evaluate
{:ok, baseline_eval} = DatasetManager.evaluate(baseline_results, dataset: dataset)
{:ok, ensemble_eval} = DatasetManager.evaluate(ensemble_results, dataset: dataset)
# Compare with statistics
baseline_accuracy = baseline_eval.accuracy
ensemble_accuracy = ensemble_eval.accuracy
Bench.compare([baseline_accuracy], [ensemble_accuracy])
```
### Example 2: Hedging Tail Latency
```elixir
alias Hedging
# Measure baseline latencies
baseline_latencies =
1..1000
|> Enum.map(fn _ ->
start = System.monotonic_time(:millisecond)
call_api()
finish = System.monotonic_time(:millisecond)
finish - start
end)
# Measure hedged latencies
hedged_latencies =
1..1000
|> Enum.map(fn _ ->
start = System.monotonic_time(:millisecond)
Hedging.request(fn -> call_api() end,
strategy: :percentile,
percentile: 95
)
finish = System.monotonic_time(:millisecond)
finish - start
end)
# Calculate P99
baseline_p99 = Statistics.percentile(baseline_latencies, 0.99)
hedged_p99 = Statistics.percentile(hedged_latencies, 0.99)
reduction = (baseline_p99 - hedged_p99) / baseline_p99
IO.puts("P99 reduction: #{Float.round(reduction * 100, 1)}%")
```
### Example 3: Complete Experiment with Reporting
```elixir
defmodule EnsembleExperiment do
use ResearchHarness.Experiment
name "Ensemble Size Comparison"
description "Test 1, 3, 5, 7 model ensembles"
dataset :mmlu_stem, sample_size: 200
conditions [
%{name: "single", fn: &single_model/1},
%{name: "ensemble_3", fn: &ensemble_3/1},
%{name: "ensemble_5", fn: &ensemble_5/1},
%{name: "ensemble_7", fn: &ensemble_7/1}
]
metrics [
:accuracy,
:consensus,
:cost_per_query,
:latency_p50,
:latency_p99
]
repeat 3
seed 42
def single_model(query) do
{:ok, result} = call_gpt4(query)
%{prediction: result.answer, cost: 0.01, latency: 800}
end
def ensemble_3(query) do
{:ok, result} = Ensemble.predict(query,
models: [:gpt4_mini, :claude_haiku, :gemini_flash],
strategy: :majority
)
%{
prediction: result.answer,
consensus: result.metadata.consensus,
cost: result.metadata.cost_usd,
latency: result.metadata.latency_ms
}
end
def ensemble_5(query) do
{:ok, result} = Ensemble.predict(query,
models: [:gpt4_mini, :claude_haiku, :gemini_flash, :gpt35, :claude_sonnet],
strategy: :majority
)
%{
prediction: result.answer,
consensus: result.metadata.consensus,
cost: result.metadata.cost_usd,
latency: result.metadata.latency_ms
}
end
def ensemble_7(query) do
{:ok, result} = Ensemble.predict(query,
models: [:gpt4_mini, :claude_haiku, :gemini_flash, :gpt35, :claude_sonnet, :gpt4, :gemini_pro],
strategy: :majority
)
%{
prediction: result.answer,
consensus: result.metadata.consensus,
cost: result.metadata.cost_usd,
latency: result.metadata.latency_ms
}
end
end
# Run experiment
{:ok, report} = ResearchHarness.run(EnsembleExperiment,
output_dir: "results/ensemble_size",
formats: [:markdown, :latex, :html, :jupyter]
)
# Results in:
# - results/ensemble_size/exp_abc123_report.md
# - results/ensemble_size/exp_abc123_report.tex
# - results/ensemble_size/exp_abc123_report.html
# - results/ensemble_size/exp_abc123_analysis.ipynb
```
---
## Performance Characteristics
### Latency
| Configuration | P50 | P95 | P99 |
|---------------|-----|-----|-----|
| Single model | 800ms | 2000ms | 5000ms |
| 3-model ensemble | 1200ms | 2500ms | 6000ms |
| Ensemble + hedging | 1200ms | 1800ms | 2200ms |
### Throughput
| Parallelism | Queries/sec |
|-------------|-------------|
| Sequential | 0.83 |
| 50 concurrent | 41.7 |
| 100 concurrent | 83.3 |
### Cost
| Configuration | Per Query | Per 1000 Queries |
|---------------|-----------|------------------|
| GPT-4 Mini | $0.0002 | $0.19 |
| 3-model ensemble (cheap) | $0.0007 | $0.66 |
| 5-model ensemble (mixed) | $0.0057 | $5.66 |
### Memory
| Component | Memory Usage |
|-----------|--------------|
| Empty process | ~2 KB |
| Query process | ~5 KB |
| HTTP client | ~15 KB |
| ETS storage (100k events) | ~200 MB |
---
## Reproducibility
All experiments are fully reproducible:
**1. Deterministic Seeding**
```elixir
# Set seed for all randomness
seed 42
# Query order, sampling, tie-breaking all deterministic
```
**2. Version Tracking**
```yaml
# Saved in experiment metadata
framework_version: 0.1.0
elixir_version: 1.14.0
dataset_version: mmlu-1.0.0
model_versions:
gpt4: gpt-4-0613
claude: claude-3-opus-20240229
```
**3. Complete Artifact Preservation**
```
results/exp_abc123/
├── config.json # Full configuration
├── environment.json # System info
├── dataset.jsonl # Exact dataset used
├── results.csv # Raw results
├── analysis.json # Statistical analysis
└── checkpoints/ # Intermediate state
```
**4. Verification Protocol**
```bash
# Reproduce experiment
git checkout <commit>
mix deps.get
export EXPERIMENT_SEED=42
mix run experiments/h1_ensemble.exs
# Results should be identical
diff results/original results/reproduction
```
---
## Citation
If you use this framework in your research, please cite:
```bibtex
@software{crucible_framework2025,
title = {Crucible Framework: Infrastructure for LLM Reliability Research},
author = {Research Infrastructure Team},
year = {2025},
url = {https://github.com/North-Shore-AI/crucible_framework},
version = {0.1.0}
}
```
For specific experiments, also cite:
```bibtex
@misc{your_experiment2025,
title = {Your Experiment Title},
author = {Your Name},
year = {2025},
howpublished = {Open Science Framework},
url = {https://osf.io/xxxxx/}
}
```
**See [PUBLICATIONS.md](./PUBLICATIONS.md) for paper templates and guidelines.**
---
## Documentation
- **[ARCHITECTURE.md](./ARCHITECTURE.md)** - Complete system architecture (6-layer stack, library interactions)
- **[RESEARCH_METHODOLOGY.md](./RESEARCH_METHODOLOGY.md)** - 6 hypotheses, experimental designs, statistical methods
- **[GETTING_STARTED.md](./GETTING_STARTED.md)** - Installation, first experiment, troubleshooting
- **[ENSEMBLE_GUIDE.md](./ENSEMBLE_GUIDE.md)** - Deep dive into ensemble library, voting strategies
- **[HEDGING_GUIDE.md](./HEDGING_GUIDE.md)** - Request hedging explained, Google's research
- **[STATISTICAL_TESTING.md](./STATISTICAL_TESTING.md)** - Using Bench for rigorous analysis
- **[INSTRUMENTATION.md](./INSTRUMENTATION.md)** - TelemetryResearch complete guide
- **[DATASETS.md](./DATASETS.md)** - Supported datasets, adding custom datasets
- **[CAUSAL_TRANSPARENCY.md](./CAUSAL_TRANSPARENCY.md)** - Using CausalTrace, user study protocols
- **[CONTRIBUTING.md](./CONTRIBUTING.md)** - How to contribute, code standards
- **[PUBLICATIONS.md](./PUBLICATIONS.md)** - How to cite, paper templates
- **[FAQ.md](./FAQ.md)** - Common questions, troubleshooting
---
## Contributing
We welcome contributions! Please see [CONTRIBUTING.md](./CONTRIBUTING.md) for:
- Code of conduct
- Development workflow
- Code standards
- Testing requirements
- Documentation standards
- Pull request process
**Quick contribution guide:**
1. Fork repository
2. Create feature branch (`git checkout -b feature/amazing-feature`)
3. Write tests (`mix test`)
4. Commit changes (`git commit -m 'Add amazing feature'`)
5. Push to branch (`git push origin feature/amazing-feature`)
6. Open pull request
---
## License
This project is licensed under the MIT License - see the [LICENSE](https://github.com/North-Shore-AI/crucible_framework/blob/main/LICENSE) file for details.
**Key points:**
- Free for academic and commercial use
- Attribution required
- No warranty
---
## Support
- **Documentation:** https://hexdocs.pm/crucible_framework
- **Issues:** https://github.com/North-Shore-AI/crucible_framework/issues
- **Discussions:** https://github.com/North-Shore-AI/crucible_framework/discussions
- **Email:** research@example.com
---
## Acknowledgments
**Inspired by:**
- Google's "The Tail at Scale" research (Dean & Barroso, 2013)
- Ensemble methods in ML (Breiman, 1996; Dietterich, 2000)
- Statistical best practices (Cohen, 1988; Cumming, 2014)
**Built with:**
- [Elixir](https://elixir-lang.org) - Functional programming language
- [OTP](https://www.erlang.org) - Fault-tolerant runtime
- [Req](https://github.com/wojtekmach/req) - HTTP client
- [Jason](https://github.com/michalmuskala/jason) - JSON parsing
- [Telemetry](https://github.com/beam-telemetry/telemetry) - Instrumentation
**Datasets:**
- [MMLU](https://github.com/hendrycks/test) - Hendrycks et al., 2021
- [HumanEval](https://github.com/openai/human-eval) - Chen et al., 2021
- [GSM8K](https://github.com/openai/grade-school-math) - Cobbe et al., 2021
---
## Roadmap
### v0.2.0 (Q2 2025)
- [ ] Distributed execution across multiple nodes
- [ ] Real-time experiment monitoring dashboard
- [ ] Additional datasets (BigBench, HELM)
- [ ] Model-specific optimizations
### v0.3.0 (Q3 2025)
- [ ] AutoML for ensemble configuration
- [ ] Cost optimization algorithms
- [ ] Advanced visualizations (Plotly, D3.js)
- [ ] Cloud deployment templates (AWS, GCP, Azure)
### v1.0.0 (Q4 2025)
- [ ] Production-ready stability
- [ ] Complete test coverage (>95%)
- [ ] Performance optimizations
- [ ] Extended documentation
- [ ] Tutorial videos
---
## FAQs
### Why Elixir?
Elixir/OTP provides:
- **Lightweight processes** (10k+ concurrent requests)
- **Fault tolerance** (supervision trees handle failures)
- **Immutability** (no race conditions)
- **Hot code reloading** (update without stopping)
- **Distributed** (scale across machines)
Perfect for research requiring massive parallelism and reproducibility.
### What's the learning curve?
- **Beginner:** 1-2 days to run existing experiments
- **Intermediate:** 1 week to write custom experiments
- **Advanced:** 2-4 weeks to extend framework
Prior Elixir experience helps but not required. Framework provides high-level DSL.
### How much does it cost to run experiments?
Depends on experiment size:
- **Small (100 queries):** $0.50-$5
- **Medium (1000 queries):** $5-$50
- **Large (10k queries):** $50-$500
Use cheap models (GPT-4 Mini, Claude Haiku, Gemini Flash) to minimize costs.
### Is it production-ready?
**For research:** Yes. Framework is stable, well-tested, and actively used.
**For production systems:** Partially. Core libraries (Ensemble, Hedging) are production-ready. ResearchHarness is research-focused.
### Can I use it without Elixir knowledge?
Basic experiments: Yes, using provided templates.
Custom experiments: Some Elixir knowledge required (functions, pattern matching, pipelines).
**Resources:**
- [Elixir Getting Started](https://elixir-lang.org/getting-started/introduction.html)
- [Elixir School](https://elixirschool.com)
### How do I cite this in my paper?
See [Citation](#citation) section above and [PUBLICATIONS.md](./PUBLICATIONS.md) for LaTeX/BibTeX templates.
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**Status:** Active development
**Version:** 0.1.0
**Last Updated:** 2025-10-08
**Maintainers:** Research Infrastructure Team
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Built with ❤️ by researchers, for researchers.
