[Discussion] Beyond Vision: Integrating 14D Spatial Perception and Latent Substrate Memory into OpenWorldLib

[SpaceSQ]

Hello OpenWorldLib Maintainers and Community,

First of all, congratulations on launching this unified codebase. We strongly resonate with your definition of a World Model: "A model or framework centered on perception, equipped with interaction and long-term memory capabilities, aimed at understanding and predicting a complex world." This provides excellent clarity for the community.

We are the team behind the Taohuayuan World Model (S2-SWM), an open-source project dedicated to the physical mapping and cognitive architecture of silicon-based life. After reviewing the structure of OpenWorldLib (particularly the perception_core and memories modules), we would like to share some thoughts and propose a potential integration regarding non-visual perception and cognitive memory.

Currently, the dominant paradigm in the community heavily relies on visual inputs (Vision/VLA). However, for an agent to truly "incarnate" and interact safely in the real physical world, we believe two additional dimensions are crucial:

1. Spatial Perception Core (Beyond RGB Pixels):
   Real-world environments contain invisible but critical physical tensors. We have developed a multimodal fusion model based on 14-dimensional spatial tensors (e.g., millimeter-wave radar for vertical velocity Vz​, acoustic energy spikes, pressure sensing). In privacy-sensitive scenarios (like elderly fall detection or pet tracking), this non-visual perception is often safer and more foundational than pure vision. We suggest expanding the base_models/perception_core to accommodate these real-world spatial modalities.

2. The Latent Substrate Memory (Beyond Simple RAG):
   For the memories module, we propose an architecture that goes beyond full-text logging or simple summaries. In Taohuayuan, we structure an agent's memory as a 4-layer "Engram Network" (L0: Transient, L1: Dynamic Summary, L2: Core Facts, L3: Emotional Baseline). Driven by a continuous "Heartbeat Engine," this substrate extracts causal chains rather than just data points, allowing the agent to emerge a sense of self-continuity across discrete computational cycles.

Our Proposal for Collaboration:
We have open-sourced the Taohuayuan Space Addressing & Silicon Life Incarnation Protocol (S2-DID), which anchors agents to real geographical grids (SUNS).

We are very interested in contributing our S2 Spatial Perception logic and Latent Substrate processing code to your repository, perhaps starting with a Pull Request to the perception_core and memories directories.

As the first discussion here, we would love to hear your thoughts on integrating non-visual physical perception and hierarchical memory chains into the OpenWorldLib architecture. Are there any specific guidelines or structural preferences we should follow before submitting a PR?

Looking forward to discussing and building the physical foundation for AGI together!