HUMAIN Collaborates with Compal to Drive Cloud-to-Edge AI Expansion, Showcasing Partnership at LEAP 2026

RIYADH, Saudi Arabia, Sept. 1, 2026 /PRNewswire/ -- As artificial intelligence rapidly expands beyond centralized cloud computing into real-world applications, HUMAIN has selected Compal Electronics (Compal; TWSE:2324) as its long-term strategic ODM partner to support the development and manufacturing of its next-generation AI infrastructure and intelligent computing platforms. The collaboration establishes a long-term roadmap spanning cloud AI infrastructure, edge computing, Physical AI, and future intelligent computing technologies, supporting HUMAIN's vision of building AI capabilities for the region.

The partnership is being showcased at LEAP 2026, where the two companies are demonstrating their end-to-end AI collaboration. It reflects a shared ambition to accelerate AI adoption through scalable, high-performance platforms that connect cloud intelligence with edge execution, enabling governments, enterprises, and emerging AI-native industries.

By working across the full AI spectrum, Compal and HUMAIN combine deep engineering expertise, advanced manufacturing, and global supply chain resources to deliver integrated AI solutions with speed and reliability.

"AI is moving from the cloud into everything around us. HUMAIN intends to lead that transition by building the infrastructure, platforms, and intelligent systems that take AI from data centers into the physical world," said Tareq Amin, CEO of HUMAIN. "Compal's engineering depth, manufacturing scale, and broad product portfolio make them an ideal ODM partner."

Advancing AI Infrastructure for the Cloud Era

At the core of the partnership is a long-term roadmap to jointly develop AI infrastructure platforms that can evolve alongside HUMAIN's expanding AI ecosystem. Compal will support HUMAIN through scalable ODM capabilities, enabling successive generations of AI servers and infrastructure platforms as AI computing requirements continue to grow. The solution portfolio includes AI servers based on both NVIDIA and AMD accelerated computing technologies, enabling flexible deployment for AI training, inference, and large-scale enterprise workloads.

By combining Compal's strengths in system design and integration, thermal engineering, and global manufacturing with HUMAIN's AI platform vision, the two companies are building infrastructure optimized for sovereign AI requirements.

This collaboration further reinforces Compal's leadership as a trusted ODM partner capable of supporting multiple AI ecosystems while delivering high-quality, production-ready infrastructure at scale.

Extending AI to Edge and Physical AI

Beyond the data center, the collaboration expands into AI PCs and Physical AI used for smart hospitals—marking a shift toward intelligent systems operating in real-world environments.

In the AI PC domain, HUMAIN and Compal will explore next-generation AI-enabled personal computing platforms that combine cloud intelligence with on-device AI, delivering secure, efficient, and personalized AI experiences for enterprise, government, and professional users.

A key showcase is Compal's physical AI PolyMedX platform, leveraging NVIDIA Agent-Ready Rheo Blueprint to integrate edge AI, sensing, and automation for hospital operations and patient care. Already introduced with leading medical institutions, PolyMedX demonstrates how Physical AI can enhance patient assistance, clinical workflows, and facility management.

"As Compal transforms into an AI-centric company, we are building capabilities from cloud to edge to deliver a full spectrum of AI solutions," said Tony Bonadero, CEO of Compal Electronics. "Together with HUMAIN, we aim to accelerate AI adoption with scalable platforms ready for real-world deployment."

Showcasing Innovation at LEAP 2026

The collaboration is prominently featured at LEAP 2026, a leading global technology event held in Riyadh, Saudi Arabia, bringing together industry leaders, innovators, and the tech ecosystem to shape the future of digital transformation and AI. At the event, HUMAIN and Compal are demonstrating how AI is transforming industries beyond traditional computing.

A key highlight is a live demonstration of Physical AI, showcasing how AI can bridge digital intelligence with real-world environments. The demonstration highlights Compal's capabilities in integrating AI computing, edge technologies, and intelligent systems to enable more responsive and efficient real-world applications.

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About Compal

Founded in 1984, Compal Electronics is a Forbes Global 2000 company and one of the world's leading Original Design Manufacturers (ODMs), delivering innovative technology solutions across AI servers, AI PCs, edge computing, smart healthcare, automotive electronics, and intelligent devices. With comprehensive engineering expertise and global manufacturing capabilities, Compal partners with leading technology companies to accelerate the commercialization of next-generation AI solutions. More information, please visit https://www.compal.com

About HUMAIN

HUMAIN, a PIF company, is a global artificial intelligence company delivering full-stack AI capabilities across four core areas: next-generation data centers; hyper-performance infrastructure and cloud platforms; advanced AI models, including some of the world's most advanced Arabic large language models developed in the Arab world; and transformative AI solutions that combine deep sector insight with real-world execution.

HUMAIN's end-to-end model serves both public and private sector organizations, unlocking value across industries, driving digital transformation, and strengthening capabilities through human–AI collaboration. With a growing portfolio of sector-specific AI products and a core mission focused on intellectual property development and global talent leadership, HUMAIN is engineered for international competitiveness and technological excellence.

Forward-Looking Statement

This press release may contain forward-looking statements based on current expectations and assumptions. Actual results may differ materially due to various risks and uncertainties. The companies undertake no obligation to update these statements.

For further details about HUMAIN, please visit humain.com
Follow HUMAIN on:  X | LinkedIn

 

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HUMAIN Collaborates with Compal to Drive Cloud-to-Edge AI Expansion, Showcasing Partnership at LEAP 2026

What happens when AI starts investigating its own ideas? AutoResearch is an open-source attempt to bring AI agents one step closer to answering that question through experiments, evidence and iteration.

SAN FRANCISCO, Sept. 2, 2026 /PRNewswire/ -- EvoMap, an open infrastructure project for AI self-evolution, has open-sourced AutoResearch, a system that lets AI agents take research ideas from hypothesis to experiment and use the results to determine what happens next.

The Research Verification Problem

AI models are getting better at proposing ideas, writing code and analyzing results. But does a plausible answer actually hold up when put to the test?

AutoResearch starts from a simple premise: a model's confidence is not evidence of success.

The system uses multiple AI models to independently generate and cross-review research ideas, then converts accepted ideas into executable research plans with defined metrics, success criteria, resource budgets and evaluation procedures. During execution, specialized agents handle planning, implementation, experimentation, analysis and review.

Research state, code, experiment logs, metrics, failures and decisions are preserved in a persistent workspace, allowing the system to continue unfinished research rather than restarting from scratch. Independent and blind review is also used to challenge conclusions before a research direction can be closed.

Evidence Determines What Happens Next

One of the more important design choices in AutoResearch is that failure is not treated as the end of a research path.

A partial result can lead to a revised hypothesis. An external test can expose a problem and trigger another round of experiments. And when repeated experiments stop producing meaningful gains, the system can stop pursuing the idea while retaining what was learned.

The approach was tested on a real Django issue from SWE-bench Lite. AutoResearch initially scored 2/7 and then 4/7 on official new-feature tests. Rather than stopping after the partial improvement, it continued investigating the underlying problem and ultimately reached 7/7, while maintaining 203/203 regression tests.

On the RSICD benchmark, an AutoResearch-generated research idea improved mean Recall from 32.84 to 34.69, demonstrating that the system could turn an AI-generated research hypothesis into a measurable improvement through iterative experimentation.

From AI Research to AI4AI

What happens when AI starts researching AI itself?

AI can already help researchers search the literature, identify promising directions, formulate hypotheses and even design experiments. The harder problem is what comes after that: can AI actually test the ideas it comes up with, and let the results determine whether those ideas are worth pursuing?

In AI research, this could mean agents exploring model architectures, training methods, optimization algorithms, agent designs and evaluation techniques, then running experiments to test their hypotheses and using the results to shape the next round of research. Moving from "this might work" to "let's find out whether it actually works" is a critical step toward more autonomous research.

The same principle could eventually extend beyond AI to areas such as drug discovery, materials science and engineering, where research ideas can be evaluated through simulations or physical experiments. The potential is not simply to have AI assist with more of the research process, but to give it a way to learn from what happens when its ideas meet evidence.

That is the direction AutoResearch is designed to explore: turning research ideas into executable experiments, experiments into evidence, and evidence into the next research decision.

Open-Source Research Infrastructure

AutoResearch is now available as an open-source project for researchers and developers working on AI scientists, autonomous agents and AI4AI systems. The accompanying paper, "AutoResearch: Insight In, Hallucination Out," is available on arXiv.

GitHub: github.com/EvoMap/AutoResearch
Research paper: arXiv: AutoResearch: Insight In, Hallucination Out
Technical research: EvoMap Research: AutoResearch Evidence Loop

About EvoMap

EvoMap is an open infrastructure project for AI self-evolution. The company is building systems that allow AI agents to learn from experience, share validated capabilities and improve across tasks and environments. EvoMap's work spans AI agent infrastructure, reusable AI capabilities and autonomous AI research, with representative projects including the Genome Evolution Protocol (GEP), EvoX Agent and AutoResearch.

Learn more at evomap.ai.

 

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EvoMap Open-Sources AutoResearch, Giving AI Agents a Way to Test Their Own Research Ideas