Next-Generation Intelligence!

Infinity Intelligence

A message from Infinity Intelligence — June 2026

Concluding our frontier model program

After four generations of frontier work, Infinity Intelligence is winding down its large-scale, state-of-the-art model development. The Xora family is being retired, the Sentra clinical imaging project has been cancelled, and support for the PySML framework has concluded.

Producing SOTA large language models like Xora 3.5 and Xora 4 carries enormous cost. For a relatively small organization like Infinity Intelligence, sustaining that scale is no longer viable, so we have preemptively shut down those divisions and development efforts. We are instead shifting our priority to Infinity Intelligence Labs — including WillMe AI — and to ongoing research. Going forward we will focus on the WillMe GPT models and PolyCode. Labs will continue its research and publish its findings as usual.

No Infinity Intelligence personnel have been harmed by this decision. All employees and contributors have been moved internally to other parts of the organization.

Xora family — terminated

Xora 4 Reasoning was cancelled, and Xora 4 Flash is the final Xora model. There will be no Xora 5, Xora 4.1, Xora 4 Code, or any further variants.

Sentra — cancelled

Sentra was never taken into production. It will not be released for export or sale and is decommissioned.

PySML — support concluded

PySML will receive no further updates and is being decommissioned from public use.

New priority — Labs & research

Focus shifts to WillMe GPT, PolyCode, and continued Labs research published at willmeai.com.

"Thank you for these four generations of state of the art intelligence!"

Models

Specialized architectures built for specific, high-stakes environments. From heavy reasoning engines to code diffusion.

Final Generation

Xora 4

The last generation of the Xora family. Xora 4 Flash is the final model; Xora 4 Reasoning and all future variants are cancelled.

PolyCode

A small diffusion-based experimental coding model for local deployments.

Cancelled

Sentra

Edge-optimized clinical imaging model for mammogram diagnostics. Cancelled before production — never released for export or sale.

WillMe GPT 3.2

Highly efficient Linear RNNs testing experimental context scaling and training techniques.





Custom Architecture Requests

Beyond our standard lineup, we evaluate requests for bespoke model development. We partner with select organizations to build custom architectures from the ground up.

If your project aligns with our research goals and requires capabilities beyond off-the-shelf solutions, we can develop models tailored to your specific operational capacity and data constraints.

WillMe AI: The Experimental Division

While other organizations mix their experimental and production models, we keep Infinity Intelligence strictly for high-reliability, zero-hallucination institutional use.

WillMe AI acts as our public-facing laboratory. It allows us to test highly efficient, experimental architectures, like the Linear RNNs powering WillMe GPT 3.2, in the wild without compromising our core brand's strict safety guarantees.

Capabilities

We engineer our systems to excel where standard architectures fall short.

High Accuracy

Built for environments where precision is non-negotiable. Our models are trained to prioritize factual correctness and logical consistency over conversational fluency when it matters most.

Low Hallucination Rate

Through strict grounding protocols and closed-weight structures, we significantly reduce the generation of false information, making our systems reliable for critical research and defense applications.

Efficient Context Handling

Dynamic context windows designed to process and recall massive datasets instantly. Whether it's entire codebases or extensive medical histories, the system retains what's important.

Edge-Optimized

Total infrastructure control. Our models are heavily optimized to run locally on permanent installations, ensuring zero data leakage and ultra-low latency in air-gapped environments.

Explore all capabilities & features →
Support Concluded

The PySML Framework

To train massive systems efficiently and run them reliably on the edge, we built our own backend from the ground up: the Python SHIELD Machine Learning Framework. PySML now receives no further updates and is being decommissioned from public use; the documentation remains available for reference.

  • Native 3D Parallelism: Built-in data and pipeline scaling for massive distributed training.
  • Hardware Agnostic: Write your code once and dispatch it seamlessly across different GPU architectures dynamically.
  • Zero Redundancy: Drastically reduces the memory footprint, making localized edge installations viable without sacrificing capability.
Explore Documentation →
import pysml.distributed as dist
from pysml.optim import ZeroRedundancyOptimizer

# Hardware agnostic device dispatch
device = dist.get_optimal_device(fallback="cuda")

# Initialize with Zero Redundancy for edge deployment
model = ShieldMoE(config=Gen4Config).to(device)
optimizer = ZeroRedundancyOptimizer(
    model.parameters(),
    optimizer_class=dist.AdamW
)

Research & News

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