The bet paid off. Individually, each of these units possesses limited intelligence and only fragmentary information about the world. Together, however, by exchanging local signals and coordinating their behaviour, they can build a shared representation of their existence, their environment and their goals. The researchers call this collaborative awareness, and it became the conceptual backbone of the entire project. The consortium brought together an unusually broad set of disciplines: artificial intelligence and robotics researchers from the University of Pisa, philosophers and cognitive scientists from Ludwig Maximilian University of Munich, swarm robotics specialists at the University of Bristol, soft robotics experts at Delft University of Technology, and the French research hub Da Vinci Labs. Over four years, this group produced not just papers, but a philosophical, mathematical and technological framework for awareness in artificial systems—along with robotic prototypes, more sustainable AI architectures, startup ventures and follow-up European projects.

One of the project’s most important contributions is a careful conceptual distinction that separates awareness from consciousness. Consciousness, in the philosophical sense, is generally associated with subjective or phenomenal experience: having feelings, or experiencing the world from a particular first-person perspective. Awareness, as the EMERGE team defines it, is something far more tractable. “In EMERGE, we understand awareness more operationally, as the capacity of an agent to process and integrate information in a way that is relevant to its actions,” explains Ophelia Deroy, philosopher and cognitive scientist at LMU Munich. Crucially, the project’s experiments showed that people can understand an artificial system as aware without ever assuming that it has subjective experience. That separation matters enormously for public perception and for the ethics of human–machine interaction, because it allows engineers to build and describe aware machines without drifting into claims about machine sentience.

To make the concept scientifically useful rather than merely evocative, the researchers decomposed awareness into distinct dimensions that can be applied across individuals and collective systems: spatial awareness, temporal awareness, self-awareness, agentive awareness and metacognitive awareness. Rather than treating awareness as an all-or-nothing property—a binary switch that a system either possesses or lacks—the framework ties each dimension to specific capabilities and concrete tasks. This move transforms a philosophical puzzle into an experimental programme. It becomes possible, for the first time, to test empirically whether increasing a system’s awareness along a particular dimension actually improves its performance on a defined task, and to measure by how much. The result is a research methodology in which awareness is not asserted but demonstrated, dimension by dimension, capability by capability.

The mathematical and computational core of the project addresses a deceptively simple question: how can awareness emerge across a physically distributed system in which no single agent sees the whole picture? The EMERGE framework describes how simple local information, exchanged with neighbouring agents, can be integrated so that the collective coordinates its actions toward solving a task. “The beauty of the approach is that the system operates in a distributed way,” says Sabine Hauert, professor at the University of Bristol. “This way systems that can scale to large numbers of agents and remain operational when individual robots fail or conditions change. It also allows humans to interact with the systems as a coordinated collective rather than controlling each robot separately.” In practical terms, that means a swarm does not collapse when one of its members breaks down, and a human supervisor can issue intentions to the group as a whole instead of micromanaging every unit.

At Delft University of Technology, the framework was pushed toward physical robotic bodies. “We want robots that can understand how their actions affect the world, anticipate what people and other robots are doing, and adapt accordingly,” explains Cosimo Della Santina, associate professor at TU Delft. “That is a key step toward robotic systems that can operate more autonomously and work naturally alongside humans.” This anticipation of consequences—understanding how one’s own movements reshape the environment, and predicting the trajectories of both human partners and fellow machines—is precisely the kind of integrated, action-relevant information processing that the project’s operational definition of awareness was built to capture. It is also what separates a genuinely collaborative robot from a pre-programmed arm repeating fixed motions.

The consortium’s mathematical framework also delivered an unexpected dividend for machine learning itself: parsimony. Systems built on the EMERGE principles proved capable of learning from substantially smaller quantities of data than conventional architectures require. “Many AI systems today depend on large, centralized computing infrastructures,” says Claudio Gallicchio, associate professor at the University of Pisa. “Our results open the way to a more decentralized and environmentally sustainable models, in which data can be processed locally using small and energy-efficient devices, such as neuromorphic hardware, reducing energy consumption, network traffic and latency while improving privacy.” In an era when training frontier models consumes enormous energy budgets and concentrates computational power in a handful of data centres, the prospect of aware, adaptive systems running on small, low-power edge hardware carries implications far beyond robotics—touching sustainability, data sovereignty and the geopolitics of computing.

The project also confronted the human side of the equation, and the findings are sobering. Bahador Bahrami, director of the Crowd Cognition group at LMU Munich, notes that “our work also examined the ethical side of human interaction with collaboratively aware systems, with important implications for future environments in which groups of humans and artificial agents will have to interact, negotiate and cooperate.” Among the studies was research into how people behave toward automated systems such as self-driving cars. The result: users may be more willing to take advantage of artificial agents than of human counterparts—a phenomenon the researchers describe as algorithmic exploitation. People who would never cut in front of another driver may happily exploit a machine, precisely because it cannot be offended, retaliate, or hold them socially accountable. Understanding this asymmetry, the team argues, is essential for designing and governing artificial systems that interact directly with people.

EMERGE was deliberately structured as a pathway from foundational research to technological innovation, and its results are already spinning out into ventures. Embodied AI develops efficient AI models for future generations of humanoid robots. ContinualIST builds AI models efficient enough to run on smaller, lower-power hardware. Collective Robotics focuses on swarms of robots that coordinate and operate collectively, and Phoebe is dedicated to systems in which humans and artificial agents solve problems together. The concepts will also anchor new research projects: EIC Pathfinder SWIFT-BUILD will apply collaboration among multiple robots and humans to automated construction, while HE MINGLE and ADA-COLLAB investigate how general-purpose, agentic AI systems can form societies, collaborate with one another, and interact with humans. Even public engagement got its own artefact: nOdes, an artistic installation in which an interactive swarm exchanges information and changes its behaviour in response to both its agents and its human visitors, letting anyone experience collaborative awareness firsthand.

“EMERGE started from a fundamental question about awareness, but it has ended with very concrete results: new robotic systems, more sustainable AI architectures, new startup initiatives and follow-up projects that are already carrying forward the idea of building artificial systems that are more efficient, sustainable, collaborative, and imbued with human values,” concludes Davide Bacciu, professor at the University of Pisa and coordinator of the project. The project was funded by the European Union under Grant Agreement 101070918, with UK participants supported by UKRI grant number 10038942. Its legacy is a reframing of one of technology’s oldest ambitions: intelligence may not need to be concentrated, enormous and opaque. It can be distributed, modest, energy-lean and—perhaps most importantly—legible to the humans who live alongside it. The swarms are small, but the idea they carry is anything but.

Subject of Research: Collaborative awareness in distributed artificial agents and swarm robotics

Article Title: European project EMERGE concludes advancing collective awareness in artificial systems

Article References: European project EMERGE concludes advancing collective awareness in artificial systems. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: EMERGE project, collaborative awareness, swarm robotics, distributed AI, EIC Pathfinder, machine learning, neuromorphic hardware, human-robot interaction, algorithmic exploitation, sustainable AI, philosophy of mind, cognitive science

The central innovation lies in the transistor itself. Molybdenum disulfide, a two-dimensional semiconducting material just a few atoms thick, forms the conducting channel of the device. Because the material is so thin, its electronic properties can be controlled with exceptional precision by electric fields applied from above and below. The researchers exploited this by constructing a dual-gate architecture: one gate tunes the channel’s conductivity in the conventional manner, while the second gate is made of a ferroelectric material whose polarization state can be flipped and retained without continuous power. This ferroelectric layer effectively gives the transistor a form of nonvolatile memory, allowing it to remember its configuration even when the device is switched off.

That combination of tunability and memory is what enables the reconfigurability at the heart of the new architecture. By adjusting the voltages applied to the two gates, the researchers can steer a single transistor between fundamentally different modes of operation. In one configuration, the device behaves as a spiking neuron, integrating incoming electrical pulses and firing an output spike only when the accumulated input crosses a threshold, mirroring the leaky integrate-and-fire dynamics of biological neurons. In another configuration, the same physical device operates as a logic transistor within a standard digital circuit, performing the deterministic switching operations on which conventional computing relies. No rewiring, no fabrication changes, and no additional components are needed to move between these modes; only gate voltages change.

Spiking neural networks represent a fundamentally different approach to computation compared with the artificial neural networks that dominate today’s AI landscape. Rather than exchanging continuous numerical values, spiking networks communicate through discrete electrical pulses, or spikes, much like the neurons in a biological brain. Information is encoded in the timing and frequency of these spikes, which allows the network to remain largely idle between events and consume power only when meaningful signals arrive. This event-driven behavior is the reason the human brain, running on roughly twenty watts, can outperform supercomputers on many perceptual tasks. Hardware that natively supports spiking dynamics could therefore deliver dramatic improvements in energy efficiency, particularly for edge applications such as wearable sensors, medical implants, and autonomous systems where power budgets are unforgiving.

Until now, building spiking hardware has typically required dedicated devices such as memristors, phase-change memory cells, or specialized neuron circuits, each fabricated separately from the logic elements of the surrounding system. That separation imposes penalties in chip area, fabrication complexity, and the energy cost of moving signals between distinct regions of a circuit. The new work collapses that distinction. Because every transistor in the architecture is potentially reconfigurable, a chip could dynamically allocate its resources, dedicating more of its fabric to spiking computation during sensory processing tasks and reprogramming sections for deterministic logic when precise arithmetic is required. This fluid boundary between neural and digital operation is what the researchers describe as a neural-network-in-logic architecture.

The ferroelectric gating mechanism deserves particular attention for what it implies about energy efficiency. Conventional transistor-based neuron circuits often need capacitors or feedback loops to accumulate charge and emulate neuronal integration, and they lose their state when power is removed. A ferroelectric gate, by contrast, stores its polarization intrinsically. In the spiking mode, the ferroelectric layer can integrate the effect of repeated input pulses by gradually shifting its polarization, acting as an intrinsic memory of recent activity. The result is a neuron whose history is physically encoded in the material itself, reducing the overhead associated with maintaining state and enabling genuinely event-driven operation. Because molybdenum disulfide channels are atomically thin, the electrostatic coupling between the ferroelectric polarization and the channel is unusually strong, which the researchers identify as essential to achieving reliable switching behavior at practical operating voltages.

Molybdenum disulfide has emerged as one of the most promising two-dimensional semiconductors for post-silicon electronics. Unlike graphene, which lacks a natural band gap, molybdenum disulfide is a semiconductor with favorable transport properties even in monolayer form. Its inert, dangling-bond-free surface means that interfaces with gate dielectrics are remarkably clean, reducing the scattering and variability that plague conventional scaled transistors. These properties have made it a favorite candidate for ultimately scaled electronics, and the new study demonstrates that the same material platform can serve functions far beyond simple switching. The combination of a two-dimensional channel with a ferroelectric gate effectively unites two of the most active research directions in device engineering into a single, multifunctional structure.

The demonstration of logic functionality alongside spiking behavior is more than a technical curiosity. Real-world intelligent systems rarely consist of neural computation alone; they require interfacing with digital peripherals, preprocessing data, and executing control decisions that demand exact, repeatable outcomes. A processor that can host both computational styles on a shared, reconfigurable fabric could avoid the energy and latency costs of shuttling data between separate neural and digital dies. The researchers show that individual transistors and small circuits built from them can be toggled between spiking and logic roles and reprogrammed repeatedly, establishing the foundation for architectures in which the boundary between inference and computation is drawn in software rather than silicon.

Significant engineering challenges remain before such devices could appear in commercial products. Ferroelectric materials integrated with two-dimensional semiconductors are still maturing, and questions of endurance, uniformity across large wafers, and long-term stability will need to be answered at scale. Fabricating high-quality molybdenum disulfide over the large areas required for industrial manufacturing remains an active area of research, although recent progress in wafer-scale growth of two-dimensional materials suggests the obstacle is one of engineering refinement rather than fundamental physics. The operating characteristics of the spiking elements, including threshold variability and response speed, will also need to be characterized and optimized for large networks.

Nevertheless, the significance of the demonstration is difficult to overstate. The semiconductor industry has spent decades pursuing ever finer transistors, but the diminishing returns of miniaturization have pushed researchers toward devices that do more with each switching element. A transistor that can remember, spike, and compute, reconfigurable on demand, represents exactly the kind of functional diversification that next-generation computing may require. If the reconfigurable molybdenum disulfide dual-gate architecture can be scaled to arrays of thousands or millions of devices, it could pave the way toward chips that learn, adapt, and compute within a single unified fabric, blurring the line between the machines we program and the brains that inspire them. For now, the work stands as a striking proof of concept that the boundary between neural and conventional computing can be drawn, and redrawn, atom by atom.

Subject of Research: Reconfigurable molybdenum disulfide dual-gate transistors with ferroelectric gating for spiking neural network-in-logic hardware architectures

Article Title: A spiking neural network-in-logic architecture based on reconfigurable molybdenum disulfide dual-gate transistors with ferroelectric gating

Article References: Li, L., Zheng, H., Li, C., Xiang, H., Wang, J., Zheng, F., Chen, M., Chien, Y.-C., Gao, J., Huo, J., Chi, D., Fong, X., Wan, Y., Meng, W., Li, L.-J., & Ang, K.-W. (2026). A spiking neural network-in-logic architecture based on reconfigurable molybdenum disulfide dual-gate transistors with ferroelectric gating. Nature Electronics. https://doi.org/10.1038/s41928-026-01706-0

Image Credits: AI Generated

DOI: 10.1038/s41928-026-01706-0

Keywords: spiking neural networks, molybdenum disulfide, ferroelectric gating, dual-gate transistors, neuromorphic computing, two-dimensional materials, reconfigurable logic, Nature Electronics, energy-efficient computing, post-silicon electronics, neural-network-in-logic, nonvolatile memory