WELCOME TO THE VINUNI'S CYBER-REASONING LAB
At VCyber, we are pioneering a new paradigm in artificial intelligence—developing systems that not only process data but also reason, deduce, and make informed decisions. While current trends in Large Language Models have overemphasized their statistical fluency, they often lack true reasoning capabilities—leading to unreliable outputs and hallucinated results. Our mission is to invent alternative AI models and algorithms that emulate human-like reasoning and enable them to solve complex challenges across engineering physics and system design with optimized efficiency.
Our interdisciplinary team, including backgrounds from computer science, electrical engineering, and applied mathematics, is dedicated to developing AI models that emulate human-like reasoning. By enforcing symbolic logic in a machine learning framework, we aim to create AI solutions capable of tackling complex, real-world problems with transparency and efficiency. Our research spans from foundational algorithm development to practical applications, all centered around enhancing the reasoning capabilities of AI systems.
By focusing on reasoning capabilities, we aim to push the boundaries of what AI can achieve, ensuring that our systems are not only intelligent but also trustworthy and adaptable to the ever-evolving technological landscape.
OUR RESEARCH FOCUS
We develop novel algorithms to emulate reasoning through symbolic and neural integration.
Develop hybrid models that combine neural networks with symbolic reasoning to enhance interpretability and logical inference capabilities.
Implement advanced reasoning techniques like Chain-of-Thought prompting and AoT to improve step-by-step problem-solving in AI models.
Apply reinforcement learning to train AI systems that can reason through complex tasks, improving decision-making accuracy over time.
We design platforms and tools that bring explainable reasoning into modern software ecosystems.
Design software architectures that support distributed reasoning across multiple AI agents, enabling collaborative problem-solving.
Develop tools that provide insights into AI decision-making processes, enhancing trust and accountability in reasoning systems.
Create middleware solutions that allow seamless integration of reasoning AI capabilities into current enterprise software ecosystems.
Building efficient and scalable infrastructures that enable real-time, reasoning-based AI.
Design specialized hardware accelerators that efficiently handle the computational demands of reasoning algorithms.
Develop low-power, high-performance hardware solutions enabling reasoning AI applications on edge devices for real-time decision-making.
Build scalable and energy-efficient infrastructure to support the training and deployment of large-scale reasoning AI models.
Using reasoning AI to enhance threat detection and response strategies.
Use logic-based AI models to infer unusual system behaviors that evade traditional signature-based detection.
Integrate reasoning engines with policy rules to deduce the most effective, real-time mitigation strategies.
Apply formal reasoning to verify access-control logic, configuration correctness, and policy enforcement across systems.
Applying reasoning AI to accelerate discovery and insight in scientific domains.
Enable machines to form logical hypotheses by reasoning over complex datasets, supporting scientific research and validation.
Incorporate symbolic reasoning into simulations to ensure consistency with scientific laws and known constraints.
Use reasoning AI to optimize experimental workflows, suggesting configurations that reduce trial-and-error.
Equipping autonomous agents with structured reasoning for complex environments.
Use logic and probabilistic reasoning to navigate incomplete information and dynamically adapt behavior.
Apply symbolic reasoning to break down complex tasks into executable actions in real-world environments.
Enable robots to reason about human intentions and goals for more intuitive collaboration.