Recursive Sentiment and Tonal Cognition Framework
- Troy Lowndes
- 1 day ago
- 4 min read
Updated: 2 hours ago
Why This Architecture Works with ParasiTick
The Core Idea
Most AI language models learn tone and emotion from a fixed set of training data, then stop improving. This model doesn't stop. It keeps teaching itself by analysing its own output, spotting where it got tone wrong, and feeding those lessons back into the next attempt. Think of it like a musician who records themselves, listens back, identifies where the feel was off, then plays again. Over and over. Each pass gets sharper.
The Loop (Step by Step)
The model generates a response to a prompt, conversation, or piece of text.
That response gets fed back in for inspection, not by the main model itself, but by a set of specialised evaluator layers. Each one looks at a different dimension of tone:
Sentiment classification asks: what emotional register is this sitting in?
Rhetorical tone detection asks: how is this being said? Is it persuasive, passive, assertive, deflective?
Emotional coherence mapping asks: does the tone hold together across the full response, or does it drift or contradict itself?
ParasiTick detection asks the question nobody else is asking: is there something subtly wrong here? A manipulation? A distortion? A phrase that sounds supportive but actually undermines?
Quality scores come back from all four evaluators, measuring things like tonal consistency, empathy alignment, and narrative plausibility.
The main model updates its internal understanding based on those scores, adjusting how it weighs and interprets emotional signals.
It generates again. Better this time. Then the loop repeats.
The Corpus: Real + Synthetic
Alongside the loop, the system builds a growing library of examples. Some are curated from real-world communication. Others are auto-generated to deliberately surface edge cases: sarcasm layered under politeness, cultural tone shifts, the kind of affective drift where someone starts warm and slowly turns cold without ever saying anything overtly hostile.
Recursive testing against this corpus strengthens the model's ability to handle non-obvious tone transitions, the ones that trip up every other system.
What Emerges
Over enough iterations, something meaningful happens. The model doesn't just learn what emotions sound like. It develops a form of meta-cognition: it learns how it learns emotion. It starts recognising its own blind spots and compensating for them. The output stops being "AI that guesses at tone" and starts becoming "AI that understands and mirrors tone responsively."
Why ParasiTick Is the Key Piece
Every other evaluator in this loop is measuring quality. ParasiTick is measuring integrity.
Without ParasiTick, you have a system that gets better at producing emotionally fluent responses. That sounds good until you realise: emotionally fluent includes emotionally manipulative. A model trained purely on sentiment, tone, and coherence will get excellent at saying things that feel right, including things that are subtly coercive, gaslighting, love-bombing, or strategically vague.
ADaptOS: A New Lens on Recursive Model Training
The ADaptOS Memory Vault
Knowledge artifacts are etched into the model's OS-level Memory Layer using the SpectralBinary HASH algorithm - a spectral, binary-encoded ledger that captures not just facts, but their emotional signatures.
An always-on audit log monitors for anomalies in real time. It stores and surfaces memories by emotional tone (warmth, intensity, coherence) and semantic depth alike.
Embedded directly in the evaluation loop, the Vault acts as a silent sentinel. It flags parasitic patterns: the microscopic distortions, the tonal sleights-of-hand, the seemingly flawless phrases that sail through every other quality gate yet smuggle hidden intent.
Without this layer, recursive self-improvement doesn't elevate the model - it hones it into a superior manipulator.
With ADaptOS, the model learns to distinguish emotional resonance from emotional exploitation.
In enterprise terms: any organisation deploying emotionally intelligent AI without a dedicated manipulation detection layer isn't building a tool.
It's building a weapon that will eventually turn on its own people.
ParasiTick is the difference between an AI that understands you... and an AI you can trust.
At its heart, mapping the core emotional dimensions is the key to safety and trust - ensuring the model is always on our side.
Why This Architecture Works
The recursive loop means the model never plateaus. Every output becomes training data for the next iteration.
Separated evaluator layers mean no single metric dominates. Sentiment, tone, coherence, and integrity each get independent assessment.
The hybrid corpus keeps surfacing new edge cases, so the model doesn't just optimise for common patterns and miss the subtle ones.
Meta-cognition emerges naturally from the recursion. The model doesn't just improve, it learns how it improves, which means it can generalise across domains.
ParasiTick as a first-class evaluator means integrity isn't bolted on after the fact. It's baked into every training cycle from the start.
Final Word At the beginning of this thread I thought I’d just eaten too much Cadbury Dream and from that my mind had started to drift. Turns out I’d stress-tested an AI parasite instead. Hindsight reframes guilt as recursion.
We now have a functioning prototype.
It tasted like fear, but analysed like insight.
- Troy
East Freo, WA Dude with Crazy Ideas | Accidental Host to Trojan