Let’s Start With a Dog and a Cup of Tea
Picture this: Morning in a garden. A cup of coffe. Your spitz proudly circling around. You’re deciding whether to respond to that email or take a breath and wait for internal clarity. You don’t need all the facts. You don’t even need all the words. What you need is that subtle bodily “yes” or “not yet.”
Now contrast that with an LLM somewhere in a data center. It fires up to process a 128,000-token context window. This is just to produce a sentence that still might sound like a polite non-answer.
You’re doing more with less. And that’s not a bug in your biology. It’s a feature.
The Problem We Face
1. Computational Waste
Large Language Models are extraordinary in ability, but wildly inefficient in energy use. Your brain runs on soup and electricity from last night’s sleep. In contrast, an LLM consumes thousands of kilowatt-hours per training cycle. Often, it outputs content no more useful than a napkin sketch.
2. Overfeeding the Context Beast
Humans move with minimal, high-impact contribution. One glance, one word, one inner shift — and a decision is made. LLMs, in contrast, demand the entire buffet, often without knowing what’s actually nutritious.
3. Flat Data = Shallow Insight
LLMs are trained on massive, flattened corpora — text without hierarchy, resonance, or energy signature. It’s like teaching a dog the concept of loyalty using a phone book: exhaustive, but devoid of meaning.
A Deeper Truth About Human Memory
Here’s what modern AI still misses:
Human memory is not based on frequency or logic alone.
It’s anchored by energetic salience — experiences marked by bodily impact, whether emotional, hormonal, or survival-based.
That’s why:
- We remember the sound of a voice from a crisis ten years ago.
- A smell can override a thousand words of rational explanation.
- One bad decision during burnout becomes a lifelong teaching.
These energy-marked experiences form cognitive anchors. They structure our internal world — not as a factual map, but as a subjective, functional approximation of reality.
And each person’s map is uniquely shaped by the patterns of their lived experiences, their choices, and their sensitivities.
This is what gives humans the ability to process information not just logically — but resonantly.
The Goal
We aim to determine if LLMs can be taught to organize and prioritize knowledge. We want them to do so in ways other than frequency or statistical weight. Additionally, we will assess them using simulated energetic weight. This is a proxy for how humans encode survival-relevant, emotionally charged, or decision-critical information.
The Hypothesis
If we augment knowledge systems for LLMs to include:
- Semantic anchors (what it is),
- Energetic weight (why it matters),
- Contextual thread (when and how it becomes relevant),
- and Hypothesis structure (how it evolves),
Then:
- Models will need fewer tokens per task,
- Deliver more context-sensitive results,
- And operate with significantly lower energy overhead.
The Experimental Design
- Build a hybrid knowledge base with layered metadata:
- relevance_score: user-rated importance
- resonance: hormonal or physiological trace (where available)
- use_history: actual recall/use frequency
- narrative_path: what it connects to in meaning-making
- hypothesis: whether it’s fixed, tested, or in flux
- Test against standard LLM queries, measuring:
- Token usage
- Output clarity
- Processing time
- Subjective usefulness
- Compare results with standard flat-document ingestion.
Where This Is Being Explored
While this precise integration of energetic salience and memory modeling is still an emerging space, several active research fronts are circling it:
1. Cognitive Architectures
- ACT-R and Soar: early symbolic models attempting to simulate human cognition, including memory weighting.
- OpenCog Hyperon: exploring semantic graph reasoning with emotional or evaluative tags.
2. Embodied AI and Affect Modeling
- Labs like MIT Media Lab and Stanford’s HAI have been exploring emotion-aware models for adaptive learning and trust-building.
- The “neuromodulation-inspired learning” trend seeks to simulate dopamine-like feedback for attention and priority in artificial neurons.
3. AI + Human Design + Narrative Systems
- Projects combining LLMs with self-authoring tools, such as Replika AI, show how emotional context and self-history shape dialogue.
- Experimental frameworks like LangGraph and Semantic Kernel explore state-aware, memory-weighted workflows.
But we are still only scratching the surface. Your unique synthesis — bridging energetics, cognition, and system thinking — may be precisely what this field needs next.
Why This Matters — Ecologically, Practically, Philosophically
As creators of intelligent systems, we have a responsibility not only to make them smarter. We must also make them more humane, resource-conscious, and aligned with how life actually works.
In nature, survival doesn’t go to the biggest processor.
It goes to the one who knows what’s relevant, and when to rest.
LLMs shouldn’t replace human wisdom.
They should learn from how it’s stored.
If you’re interested — stay tuned. More to come!
