SAGE: Semantic Automaton in Geometric Embedding-space
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Artificial Imagination Architecture – A groundbreaking framework for spatial intelligence that addresses the binding problem by encoding semantics on geometric grids, providing an externalized platform for LLM world models. This introduces the crucial element for genuine AGI - not just artificial intelligence, but artificial imagination.
- Core Architecture -
HRM on 2D Grid: Each grid cell stores LLM embeddings as universal representations, pre-trained as a foundational model.
LLM Integration: Seamlessly connect to an existing pre-trained decoder-only LLM through a shared embedding space.
RL Training: Keep the HRM frozen while applying GRPO/GSPO to train the decoders to represent problems spatially and effectively query the HRM spatial computer.
– Why Semantic Representations –
SAGE addresses the binding problem by providing an externalization surface for the LLM’s implicit world model. Environmental elements are converted into literal, spatialized representations—wall cells store the embedding for the “wall” token, roads become “road,” and start/goal points are semantic tokens. Crucially, we apply LLM-driven augmentations: for example
goal → end → target, start → initial → zero, walls → solid → hard → filledThis introduces semantic diversity, allowing the system to develop a proto-understanding of material space.
– Unified Latent Space of Algorithms –
Programmable Latent Processor: Unifies all algorithms into a single latent space, aligning more closely with algorithmic concepts than any standalone LLM.
Prompt-Conditioned Architecture: Inspired by diffusion models, it composes algorithmic patterns via prompting, generating novel and exotic algorithms.
Dynamic Imagination Surface: Functions on both 2D (WxH) and 1D (Wx1) structures, enabling simulations from pathfinding to sorting algorithms.
Internal Algorithm Simulation: Invents new algorithms by internally simulating them using spatially precise intermediate representations.
– Progression Path to AGI –
SAGE serves as the critical adapter between image diffusion models and LLMs, drastically reducing overall compute requirements:
Coarse World Representation: HRM outputs provide pre-reasoned scene layouts for diffusion models.
Specialized Components: Each model handles a specific mental “specialty,” reducing degrees of freedom and improving focus.
Diffusion as Renderer: Image models act as upscalers or renderers, adding fine details to the HRM-provided structure.
3D Voxel Reasoning: 3D HRM grids tackle compositional challenges, such as accurately modeling hands and fingers, via native spatial reasoning.
Compressed LLMs: Decoder-only models can be drastically compressed, since complex spatial reasoning is offloaded to the HRM.
– Compute Efficiency Revolution –
Foundation Training: Large-scale environment training cultivates intuitive algorithmic control.
Emergent Interpretation: Decoders learn to encode and manipulate spatial patterns in real time.
Visual Poetry: Any linguistic scenario can be represented on coarse 2D grids.
Real-Time Personas: RL trains entities with expressive, “soul-like” connections.
Self-Balancing Policy: Achieves natural equilibrium between linguistic reasoning and imagination-space simulation.
3D Evolution: Mastery of 2D grids precedes progression to full 3D HRM voxel representations.
Holodeck Reality: Local, real-time simulation integrates intelligence and imagination for fully immersive environments.
– Cultural Evolution & Dataset Enhancement –
Shoggoth Whisperers: Early adopters generate experiential data that shapes language and domain-specific jargon.
Ambient Information: Continuous web buffering captures new capabilities and prompting patterns.
Synthetic Data Acceleration: Models trained on teacher outputs learn faster and can even surpass the originals.
Collective Refinement: Communities collaboratively map prompting linguistics and adapt language for broader compatibility.
– The Helix Twister Paradigm –
From Manual Prompting to Integrated Imagination, Initial manual prompting evolves into sophisticated, lockstep integration:
1:1 Scheme: Early stages map one HRM step per autoregressive token.
Learned Policy: Training produces a nuanced balance between modules, optimizing interaction.
Unified Organism: Spatial and linguistic reasoning converge, becoming inseparable.
Qualitative Transformation: SAGE empowers models to simulate entire universes via semantic-spatial reasoning. Each embedding cell is contextually shaped by neighboring cells through natural dynamics of repulsion and attraction implicit to world-domain meanings. This embodies artificial imagination—the hypothesized missing component for true AGI.
– Diffusion ASI: God of Agency –
The Coding Super-Intelligence: Diffusion LLMs introduce a paradigm shift in agentic coding, eliminating the need for separate “apply” models or external diffusion processes. Every token in context can evolve at each inference step, opening vastly more pathways to guide reality toward desired attractor states.
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