0.1.2

"True general intelligence begins exactly where the training data ends. We are no longer testing memory; we are testing reasoning under structural collapse."

© 2026 AGI Systems Directorate. Authored by Ananya Soni, Founder & CEO.

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"In the age of recursive self-correction, compute is cheap, but verifiable logic is the ultimate bottleneck."

The Contamination Crisis

The current landscape of open-source artificial intelligence benchmarking is structurally compromised. Standardized evaluations (MMLU, HumanEval, standard logic suites) have inevitably become pre-training targets. When a large language model (LLM) solves a complex reasoning task today, it is rarely demonstrating emergent intelligence; it is executing high-dimensional curve fitting on memorized test data.

AGI Gauntlet is engineered to bypass latent space memorization entirely.

Zero-Contamination Progressive Benchmarking

This package introduces a fundamentally rigorous paradigm for evaluating and orchestrating AI models. The logical constraints and evaluation matrices embedded within the Gauntlet do not exist on the open web. They are dynamically obfuscated at the source level to prevent crawler ingestion.

The framework enforces a progressive cognitive stress-test:

1. Foundational Verification: Establishes baselines for spatial, temporal, and economic reasoning.
2. Dynamic Plasticity: Injects real-time semantic shifts (e.g., redefining fundamental constants mid-inference) to break standard transformer next-token prediction loops.
3. Neuro-Symbolic Synthesis: Forces models to operate under severe temporal resource poverty, multi-actor paradoxes, and recursive logic structures.

What's New in v0.1.1

The 0.1.1 release transforms agi-gauntlet from a strict benchmark into a comprehensive lifecycle and agentic workflow engine:

• Universal Model Hooks: Seamlessly connect frontier commercial models (via API keys), self-hosted Gradio clients, or custom private server endpoints using the new ModelRegistry architecture.
• Agentic Workspace: A dedicated environment to plug in autonomous AI assistants, granting them tool access to manage data parsing and evaluation pipelines.
• Lifecycle Manager: Stage and fetch curated datasets across remote, secure endpoints to seamlessly transition from evaluation to fine-tuning.
• Test-Time Compute Guardrail: An advanced 2026 monitoring feature that analyzes semantic entropy during Monte Carlo Tree Search (MCTS). It forces a deterministic circuit-break if a multi-agent loop enters a hallucinated self-correction spiral, saving compute and preventing silent failures.

Quickstart

Install the package via pip:

pip install agi-gauntlet

Integrate your infrastructure and execute evaluations cleanly:

import os
from agi_gauntlet import ModelRegistry, GauntletEngine, AgentWorkspace

registry = ModelRegistry()

# 1. Connect a Frontier Commercial Model
registry.register_commercial_api(
    name="frontier_model_alpha",
    provider="anthropic",
    api_key=os.getenv("ANTHROPIC_API_KEY")
)

# 2. Connect a custom Gradio hosted model
registry.register_gradio_client(
    name="local_qwen_instance", 
    gradio_url="http://localhost:7860", 
    fn_index=0
)

# 3. Initialize the Engine and Agent Workspace
workspace = AgentWorkspace()
workspace.plug_agent(agent_id="eval_orchestrator", tools_granted=["database_read", "run_gauntlet"])

engine = GauntletEngine(registry)
engine.serve(port=8000)

Contributing

The pursuit of Artificial General Intelligence is a collaborative imperative. agi-gauntlet is open source, and architectural contributions, new evaluation matrices, and framework optimizations are welcome.

Please review the open issues on our GitHub Repository before submitting a pull request. Keep your code modular, ensure logical determinism, and leave contaminated benchmarks in the past.