An Experience Alignment Architecture: from Space E to Non‑Causal Intelligence -Formal
Experience space — a (possibly high-dimensional) vector space of candidate experiences . Concretely: embeddings of text, images, actions, sensory states, etc.
Example: .
Archetype — an internal reference. Can be:
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a fixed vector (prototype),
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a set/distribution of vectors (multi-modal archetype),
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or a parameterized model that maps context to a target representation.
Alignment index — a scalar score measuring how well experience aligns to . This is the central function we must define precisely. Examples: cosine similarity, negative energy, or a learned scoring network.
Selector — operator that selects one or more experiences from maximizing . Formal: . In practice: top-k, stochastic sampling proportional to , MCMC, or beam search.
Projector — maps selected internal experience(s) to an output for a downstream subsystem or user (rendering, language, actuator command). Could be identity, decoder, or a transformation network.
Formal definitions / candidate choices
1) Experience space
Let experiences be vectors: . If raw data is non-vector (text, images), use encoder so .
2) Archetype
Options:
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Prototype vector:
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Distribution:
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Conditional archetype: where is context (user state, prompt).
3) Alignment index
Begin with simple, interpretable choices and show how to extend:
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Cosine similarity:
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Gaussian kernel / RBF:
(higher is better if you negate the distance).
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Learned scorer (neural):
where outputs a scalar (sigmoid or raw score).
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Energy-based:
You can combine: .
4) Selector strategies
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Deterministic argmax: .
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Top-k + diversity: take top-k then apply a diversity penalty (e.g., determinantal point process or max-marginal relevance).
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Softmax sampling (Boltzmann):
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MCMC / Metropolis-Hastings for continuous .
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Generative sampling: train generator then search latent to maximize .
5) Projector
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Identity: return the selected .
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Decoder: (text generator / image renderer / actuator translator).
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Filter: apply constraints or safety overlays to before output.
Learning / training objectives
You’ll want to match human/architectural intent. Approaches:
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Supervised (paired)
If you have pairs with label (aligned/not), train via cross-entropy or regression. -
Contrastive (self-supervised)
Define positive pairs (experience aligned with archetype) and negatives. Use InfoNCE:
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Reinforcement Learning (RL)
Treat as intrinsic reward. Policy produces experiences; maximize expected . -
Energy-based / score matching
Model an energy over and train via contrastive divergence or noise-contrastive estimation. -
Meta-learning / few-shot
Learn that produces an archetype vector from a few examples.
Evaluation metrics
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Alignment accuracy (if labeled): fraction of times selector picks human-preferred experience.
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Human preference A/B tests: pair outputs from baseline vs. alignment model.
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Diversity: average pairwise distance among top-k selections.
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Robustness / Stability: how sensitive is selection to small perturbations of or inputs.
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Calibration: reliability of as a probability (if normalized).
Toy implementation (Python-style pseudocode)
Extending to learned scorer
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Replace
cosine
with a small MLP:mu_theta(x,a) = MLP([x, a, x*a, |x-a|])
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Train with contrastive pairs or human labels.
Example experiment plan (practical)
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Dataset & encoder
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Pick domain (dialogue snippets, images, short music clips).
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Use pre-trained encoder (CLIP for images/text, Sentence-Transformers for text).
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Define archetypes
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Start with prototype vectors: e.g., “Harmony” = average embedding of 200 curated positive examples.
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Or learn a small mapping from textual archetype label to vector using few examples.
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Baseline scorer
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Cosine similarity to prototype. Evaluate with small human study.
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Upgrade scorer
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Train lightweight MLP with contrastive loss.
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Selector
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Start deterministic (argmax) then evaluate sampling vs. argmax for diversity.
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Projector
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Use LM or image decoder to render selected internal experience.
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Human evaluation
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Rate alignment, novelty, coherence.
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