wij{2,1,0,+1,+2}w \{-2, -1, 0, +1, +2\}
Where:
+2 = strong synergistic interaction (mutual reinforcement of meaning)
+1 = weak synergy (complementary but not amplifying)
0 = neutral (coexistence without enhancement or conflict)
1 = weak inhibition (partial semantic dissonance)
2 = strong inhibition (metaphoric contradiction, irony, or sarcasm)
The weight is not absolute, but context-dependent, modulated by:
Domain knowledge (e.g., "cold fire" in poetry vs. chemistry)
Cultural framing (e.g., "white lie" may be +1 in Western pragmatics, 1 in truth-centric epistemologies)
Discourse register (literal vs. figurative)
C. Tensor Embedding in CAS-6
Interaction weight becomes the first axis (I) in the CAS-6 tensor described in Section 3.3:
Iij(1)=wijI^{(1)} = w
This value is learned or inferred via:
Cross-attention weights across semantic heads
Sentiment divergence analysis
Pre-trained embeddings' angular divergence in meaning space
Semi-supervised contrastive learning using idioms, metaphors, and satire datasets
D. Implications for AI Semiosis
1. Figurative Language Understanding
Weight gradients help distinguish between literal collocations (+2), conceptual metaphors (1 to 2), and neutral idioms (0).
2. Affective Computing
Word interactions with negative weights often correspond to emotional ambivalence, irony, or empathy gaps.
3. Creative Generation
Models that optimize for controlled weight distributions (e.g., combining +2 and 2 interactions) can simulate poetic or artistic tone, balancing resonance and disruption.
4. Disinformation Detection
High-probability but semantically contradictory combinations (e.g., oxymorons in political slogans) can be flagged via anomalous negative weight patterns.
E. Visual Intuition
Interaction weight maps can be rendered as heatmaps or edge-labeled graphs, where:
Thicker green edges denote synergistic interactions (+1/+2),
Thinner gray edges denote neutral co-presence (0),
Red dashed edges denote conflict or inhibition (1/2).
These visualizations enable interpretability in multimodal AI systems, especially those operating in explainable language generation and trust-sensitive contexts.
