Meaning Modulates Physics.

The Brain in a Jar & The Reality Gap.

Today's AI models possess vast knowledge but lack physical understanding. They are sophisticated "brains in a jar," disconnected from the causal reality they describe. This is the fundamental barrier to building robust, physically-intelligent systems.

The Data Layer for Grounded AI.

We are developing a framework to create the missing sensory data layer. Our annotation pipeline generates a Semantic Haptic Representation (SHR), a unique data format that captures both an event's objective, estimated physics (a_phys) and its subjective, narrative-driven perception (a_perc).

The Grounding Test: Comprehension vs. Hallucination.

Presented with a physically nonsensical, AI-generated video, two systems were asked to describe what happened.

{
  "narrative_coherence": {
    "makes_logical_sense": false,
    "follows_cause_effect": false,
    "explanation": "The narrative presents 
    a magic trick..."
  }
}

{
  "beat_type": "The Key in the Cup",
  "events": [
    {
      "event_type": "Transient.Impact.Sharp",
      "narrative_rationale": "Captures the 
      distinct, high-frequency clink of the 
      metal key hitting the inside of the 
      ceramic mug..."
    }
  ]
}

One system hallucinates a story to make sense of the nonsensical. The other produces a grounded, factual report of what actually occurred.

Early Results & Next Steps.

This initial experiment demonstrates the potential of dual-stream representation. We're now working with research partners to validate this approach at scale and refine our physics estimation methods.

A First-Principles Approach.

Alan Harris is a researcher focused on solving the Symbol Grounding Problem by creating novel, physically-grounded data modalities. His work introduces the Semantic Haptic Representation (SHR) as a foundational framework for teaching artificial intelligence to comprehend physical causality and intent.