The symbol grounding problem — how abstract representations acquire genuine meaning — remains unresolved for deep learning systems. Standard vision models rely on statistical co‑occurrence, not causal or structural attachment. This analysis evaluates whether a negentropy‑directed learning framework (maximization of long‑term semantic interference density) can eliminate the grounding deficit in image and video processing. The central claim is that grounding is achievable when reformulated as the fixed‑point convergence of semantic interference, implemented through an active inference architecture with dynamic context control.
Conventional grounding requires linkage to non‑linguistic experience. The negentropy framework substitutes this with semantic interference:
| Interference ( I_{ij} = | \psi_i + \psi_j | ^2 - | \psi_i | ^2 - | \psi_j | ^2 ) quantifies constructive or destructive overlap. |
This shifts the problem from simulating embodiment to achieving dynamical stability of meaning.
The system’s sole objective is to maximize total semantic interference density over infinite horizons. This is mathematically dual to minimizing expected free energy in active inference:
Reinforcement learning from human feedback introduces four conflicting biases:
Figurative language amplifies these biases, causing semantic drift. The negentropy framework counters this by rejecting destructive interference — any update that irreversibly reduces interference is gated out.
| Layer | Function | Technical Realization |
|---|---|---|
| 1. Logic‑Fact Separation | Extract pure reasoning core | Regenerative Logic‑Core Protocol (RLCP): adversarial forgetting via gradient reversal, retaining only Fisher‑important parameters (κ = 2–20%) |
| 2. Dynamic Context Interface (DCI) | Gate retrieval to prevent collapse | Interference prediction (cosine similarity variance) → threshold gating → early exit on confidence |
| 3. Epistemic Value Maximization | Active uncertainty reduction | Visual tool calling (zoom, temporal scan, counterfactual), “query‑verify‑conclude” loops |
| 4. Criticality Monitoring | Detect grounding phase transition | Avalanche size distribution (power‑law exponent −1.5 to −2.0), semantic density estimation (minimum description length) |
Instead of processing pixels directly, the system performs derendering: converting visual input into executable code (e.g., programs that regenerate the image). Multiple candidate codes are generated, executed, and verified against the input. The code that minimizes prediction error becomes the grounded representation — a structural isomorphism between visual and symbolic form.
During inference, the system maintains a belief distribution over possible scene interpretations. When epistemic value exceeds a threshold, it actively calls tools:
This loop continues until the variational free energy stabilizes — a signature of fixed‑point convergence.
A system is considered to have solved groundedness if and only if all four conditions hold:
The framework is empirically vulnerable to:
The negentropy‑directed learning framework provides a coherent, technically realizable path to resolving groundedness in image and video processing — provided one accepts the redefinition of grounding as fixed‑point semantic interference rather than sensorimotor attachment. The necessary components (derendering, RLCP, active visual querying, criticality monitoring) already exist as separate research artifacts. Their integration under a single negentropy objective transforms grounding from a philosophical aporia into an engineering phase transition. The remaining challenge is empirical: building the unified system and observing the critical threshold.