Project Dropstone: Neuro-Symbolic Runtime for Long-Horizon Engineering

Documents found on an open directory describe Project Dropstone, a neuro-symbolic runtime designed to solve context saturation for engineering tasks lasting 24 hours or more. The system utilizes a Recursive Swarm architecture to manage complex, long-horizon workflows.

Two major technical claims are highlighted within the documentation. First, the D3 Engine separates the 'Active Workspace' from 'Latent History,' claiming to reduce compute costs by 99% through the use of 'Trajectory Vectors' rather than standard token caching. Second, Horizon Mode reportedly uses a swarm of 10,000 agents to explore solution paths, diverging from standard linear prediction models. The documents also reference a 'Flash-Gated Consensus' protocol and a separate paper on Horizon Mode available in the same directory.

The documentation for Project Dropstone introduces the D3 Engine as a core component for managing computational resources. This engine is designed to handle the immense data loads associated with long-duration engineering tasks by fundamentally changing how context is stored and accessed.

According to the documents, the D3 Engine achieves significant efficiency gains by separating the Active Workspace from the Latent History. This architectural choice allows the system to maintain focus on immediate tasks while archiving previous data without the heavy overhead of traditional methods. The key innovation appears to be the replacement of token caching with Trajectory Vectors, a method purported to reduce compute costs by 99%.

While the D3 Engine focuses on resource management, Horizon Mode addresses the logic and problem-solving aspect of the runtime. This mode represents a departure from standard AI prediction models, which often struggle with the complexity of multi-day engineering projects.

Instead of relying on linear prediction, Horizon Mode utilizes a swarm of 10,000 agents to explore various solution paths simultaneously. This massive parallel processing capability allows the system to evaluate a wider range of possibilities and potentially identify optimal solutions more effectively than sequential processing. The documentation notes that a separate paper detailing Horizon Mode is available in the same directory.

The underlying structure of Project Dropstone is described as a Recursive Swarm architecture. This framework supports the coordination of the thousands of agents used in Horizon Mode and manages the data flow between the Active Workspace and Latent History.

Among the technical protocols mentioned is the Flash-Gated Consensus protocol. While the documents do not provide a detailed breakdown of this protocol's mechanics, its inclusion suggests a mechanism for ensuring agreement among the swarm agents during the problem-solving process. The existence of these documents on the blankline.org open directory suggests these technologies are currently in a research or development phase.

If the claims in the documentation hold true, Project Dropstone could represent a significant shift in how automated engineering tasks are handled. The ability to maintain context over a 24-hour period without saturation is a major hurdle in current AI capabilities. By reducing compute costs so drastically, the D3 Engine makes such long-running tasks more feasible from an economic standpoint.

The shift from linear prediction to swarm intelligence in Horizon Mode offers a robust alternative to current models. This approach could lead to more creative and comprehensive solutions in complex engineering scenarios. The documents imply that these technologies are being explored to push the boundaries of what is possible in automated, long-horizon task execution.