The Company Unveils the First Brain-Inspired Self-Evolving Foundation System at the Shanghai Pujiang Innovation Forum
SINGAPORE, Sept. 23, 2026 /PRNewswire/ — Xisiid Intelligence, an AI neo-lab exploring new paradigms for next-generation artificial intelligence, announced the completion of a RMB 25 million seed round. The funding will primarily support continued research and development of its Brain-Inspired Self-Evolving Foundation System and the joint development of AI applications with domain experts.
As AI agents move beyond conversational applications toward increasingly complex professional workflows, Xisiid Intelligence is developing a new approach designed to help AI systems learn from real-world task execution and continuously build reusable capabilities.
The company also made its public debut at the recent Shanghai Pujiang Innovation Forum, a high-level international science and technology forum jointly hosted by the Ministry of Science and Technology of the People’s Republic of China. At the forum, it introduced its technical vision and multidisciplinary team and unveiled its proprietary foundation system.
The Next Phase of AI: From Content Generation to Reliably Completing Complex Tasks
The way large language models (LLMs) are evaluated is reaching a pivotal transition. Industry attention is shifting beyond generation and reasoning performance on static benchmarks toward a more demanding question: Can AI reliably complete complex, real-world tasks?
In high-stakes professional fields such as investment banking, management consulting, and cross-border legal practice, a single assignment may require processing large volumes of heterogeneous documents, coordinating workflows across multiple software applications, and reasoning through complex, multi-step processes with iterative verification.
Current state-of-the-art LLMs, however, still struggle with complex, long-horizon tasks. As task length and complexity increase, they remain vulnerable to reasoning drift, compounding errors, and inconsistent execution. Frontier agent benchmarks highlight this gap. Across both APEX-Agents, which evaluates complex tasks spanning multiple applications, and Harvey LAB, which assesses professional legal deliverables, even state-of-the-art models continue to face challenges in reliably completing end-to-end work. Recent public results from Harvey LAB, for example, show that under its rigorous All-Pass standard, leading models still achieve end-to-end task completion rates of below 20%.
“Current AI systems have already achieved remarkable capabilities in generation and reasoning, but in mission-critical environments, the real test is whether AI can reliably complete complex, multi-step work over extended task horizons, like a human expert,” said Dr. Chua Yam Song, Founder of Xisiid Intelligence. “Increasing model parameters alone cannot solve the challenges of reliable execution and cumulative learning. Our approach, Brain-inspired Intelligence for Artificial Intelligence (BI4AI), goes beyond replicating the physical structure of the brain. Instead, we draw on the principles by which biological intelligence organizes cognition, memory, learning, and adaptive feedback—moving AI from a computation-centric paradigm toward a memory-centric one.”
A Dual-System Architecture for Reliable Execution and Continual Learning
To translate this memory-centric philosophy into an engineering architecture, Xisiid Intelligence has developed a dual-system architecture comprising System 1 and System 2, integrating a proprietary language model layer with structured memory layer.
System 1: Fast Execution Through Latent-Space Interaction
System 1 serves as the high-speed execution layer for frequent tasks. Powered by their proprietary latent exchange protocol, it enables vector-based interaction at the internal representation layer, allowing functional modules to exchange states directly rather than relying on decoding and encoding of text.
Conventional agent frameworks typically coordinate modules through prompts and natural-language context. As task horizons expand, larger context windows increase token overhead while important information can become diluted or lost. By using latent exchange protocol, Xisiid Intelligence aims to reduce communication overhead and information loss across complex workflows.
System 2: Task-Centric Hierarchical Memory
System 2 functions as a task-centric hierarchical memory system. Beyond factual knowledge, it captures reusable workflows, decision heuristics, and experience derived from previous task execution.
When the system encounters non-standard or unfamiliar situations, it can retrieve relevant prior experience to provide task-specific constraints, execution guidance, and decision support. This allows accumulated experience to become reusable rather than requiring each task to effectively start from scratch.
Together, System 1 and System 2 form a continual-learning loop driven by real-world task feedback:
Execution –> Feedback –> Generalization –> Consolidation
Within this loop, validated skills and experience can be progressively consolidated into reusable system and model capabilities. The evolution process is governed by evaluation, gating, and rollback mechanisms designed to keep updates controlled and reversible.
As experience accumulates, the system can progressively adapt to the workflows, knowledge, and operating environments of individual professionals and organizations.
Xisiid Intelligence reports that the architecture has already completed initial milestone validation. In benchmark evaluations covering complex professional tasks, its agent system—powered by models of approximately 30 billion parameters—has demonstrated performance comparable to frontier models with more than one trillion parameters, effectively supporting fully private, on-premise deployment.
A Multi-disciplinary Team Advancing Brain-Inspired Intelligence
The technical roadmap of Xisiid Intelligence is grounded on years of interdisciplinary research and engineering across computational neuroscience, brain-inspired computing, artificial intelligence, and large-scale systems.
Founder Dr. Chua Yam Song has extensive experience in computational neuroscience and neuromorphic computing. His previous roles include serving as Principal Investigator for the Neuromorphic Program at the Agency for Science, Technology and Research (A*STAR), Singapore; Chief Researcher of Neuromorphic Computing at Huawei 2012 Labs; Chief Expert and Director of a leading Chinese Neuromorphic Computing Laboratory. He currently leads and participates in several Chinese National research initiatives, including projects under the China Brain Project.
At the 2024 World Internet Conference, Dr. Chua introduced NaoQi-SuWen, the first brain-inspired medical language model developed in China. In 2025, leveraging on common core technologies Qixin, a language model designed for emotional companionship, was also launched. He also contributed to the development of a general-purpose neuromorphic cloud platform capable of supporting both large language models and brain simulations up to 10 billion neurons.
Brain-inspired technologies developed under his leadership have been deployed across healthcare, electric power, energy, transportation, and agriculture, reflecting end-to-end experience spanning algorithm research, software development, and large-scale cloud-edge computing systems.
Building on this foundation, Xisiid Intelligence has assembled a multi-disciplinary team spanning computational neuroscience, neuromorphic computing, large-model algorithms, cognitive science, and systems engineering. The team combines rigorous academic research with extensive industry experience, with research published in leading international venues including Nature Machine Intelligence and IEEE Transactions on Neural Networks and Learning Systems (IEEE TNNLS).
This enables Xisiid Intelligence to connect insights from biological intelligence with current AI approaches such as large language models, agentic systems, memory architectures—advancing a new generation of brain-inspired innovation designed for complex, real-world tasks.
From Co-Creation to Own Intelligence
Xisiid Intelligence is currently working with institutional clients across professional domains including investment decision-making, legal due diligence, and enterprise operational governance, validating its Brain-Inspired Self-Evolving Foundation System in real-world workflows.
Through continued human-AI interaction, this experience can be consolidated into persistent and reusable organizational memory, enabling the system to develop capabilities increasingly aligned with expertise and technical know-how of each organization.
The system can also be deployed on-premise, allowing mission-critical data, proprietary workflows, and accumulated organizational knowledge to remain within enterprise-controlled environments. Over time, organizations may then build an intelligence asset that is progressively accumulated and owned within the organization itself.
With the new funding, Xisiid Intelligence will continue advancing its core architecture and deepen co-creation with professional institutions across knowledge-intensive domains.
The Company sees this as critical to the transition of AI from content generation to the reliable completion of real-world tasks, and continually evolving through real-world execution.
Its long-term vision is to enable every individual and enterprise to build intelligence they truly own—intelligence that learns, evolves, and compounds over time.
This is “Own Intelligence.”
Media Contact:
Name : Yang Chen
Email: chenyang@xisiid.com
Website: www.xisiid.com
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