An Experience Alignment Architecture: from Space E to Non‑Causal Intelligence -Formal

 An Experience Alignment Architecture: from Space E to Non‑Causal Intelligence -Formal

Experience space $E$ — a (possibly high-dimensional) vector space of candidate experiences $x \in \mathcal{E}$. Concretely: embeddings of text, images, actions, sensory states, etc.
Example: $\mathcal{E} \subseteq \mathbb{R}^d$.

Archetype $A$ — an internal reference. Can be:

• a fixed vector $a \in \mathbb{R}^d$ (prototype),

• a set/distribution of vectors $\mathcal{A}$ (multi-modal archetype),

• or a parameterized model $f_\theta(\cdot)$ that maps context to a target representation.

Alignment index $\mu(x, A)$ — a scalar score measuring how well experience $x$ aligns to $A$. This is the central function we must define precisely. Examples: cosine similarity, negative energy, or a learned scoring network.

Selector $S$ — operator that selects one or more experiences from $\mathcal{E}$ maximizing $\mu$. Formal: $S(\mathcal{E},A) = \arg\max_{x \in \mathcal{E}} \mu(x,A)$. In practice: top-k, stochastic sampling proportional to $\exp(\beta \mu)$, MCMC, or beam search.

Projector $P$ — 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: $x \in \mathbb{R}^d$. If raw data is non-vector (text, images), use encoder $g_\phi$ so $x = g_\phi(\text{raw})$.

2) Archetype

Options:

• Prototype vector: $a \in \mathbb{R}^d$

• Distribution: $a \sim \mathcal{N}(\mu_A, \Sigma_A)$

• Conditional archetype: $a = f_\theta(c)$ where $c$ is context (user state, prompt).

3) Alignment index $\mu$

Begin with simple, interpretable choices and show how to extend:

• Cosine similarity:

$$\mu_{\cos}(x,a) = \frac{x \cdot a}{\|x\|\|a\|}$$

• Gaussian kernel / RBF:

$$\mu_{\text{rbf}}(x,a) = -\|x-a\|^2 / (2\sigma^2)$$

(higher is better if you negate the distance).

• Learned scorer (neural):

$$\mu_\theta(x,a) = h_\theta([x; a; x\odot a; |x-a|])$$

where $h_\theta$ outputs a scalar (sigmoid or raw score).

• Energy-based:

$$\mu_E(x,a) = -E_\theta(x,a)$$

You can combine: $\mu = \alpha \mu_{\cos} + (1-\alpha)\mu_\theta$.

4) Selector strategies

• Deterministic argmax: $x^* = \arg\max_{x \in \mathcal{E}} \mu(x,a)$.

• Top-k + diversity: take top-k then apply a diversity penalty (e.g., determinantal point process or max-marginal relevance).

• Softmax sampling (Boltzmann):

$$p(x) \propto \exp(\beta\mu(x,a))$$

• MCMC / Metropolis-Hastings for continuous $\mathcal{E}$.

• Generative sampling: train generator $G_\psi(z,a)$ then search latent $z$ to maximize $\mu(G_\psi(z,a),a)$.

5) Projector

• Identity: return the selected $x$.

• Decoder: $y = \text{decoder}_\xi(x)$ (text generator / image renderer / actuator translator).

• Filter: apply constraints or safety overlays to $x$ before output.

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Learning / training objectives

You’ll want $\mu$ to match human/architectural intent. Approaches:

1. Supervised (paired)
   If you have pairs $(x_i, a_i, y_i)$ with label $y$ (aligned/not), train $\mu_\theta$ via cross-entropy or regression.

2. Contrastive (self-supervised)
   Define positive pairs (experience aligned with archetype) and negatives. Use InfoNCE:

$$\mathcal{L} = -\log \frac{\exp(\mu(x^+,a)/\tau)}{\sum_j \exp(\mu(x_j,a)/\tau)}$$

3. Reinforcement Learning (RL)
   Treat $\mu$ as intrinsic reward. Policy $\pi$ produces experiences; maximize expected $\mu$.

4. Energy-based / score matching
   Model an energy over $(x,a)$ and train via contrastive divergence or noise-contrastive estimation.

5. Meta-learning / few-shot
   Learn $f_\theta$ that produces an archetype vector $a$ from a few examples.

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Evaluation metrics

• Alignment accuracy (if labeled): fraction of times selector picks human-preferred experience.

• Human preference A/B tests: pair outputs from baseline vs. alignment model.

• Diversity: average pairwise distance among top-k selections.

• Robustness / Stability: how sensitive is selection to small perturbations of $a$ or inputs.

• Calibration: reliability of $\mu$ as a probability (if normalized).

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Toy implementation (Python-style pseudocode)

python
# Simple toy: E = set of vectors, Archetype a is a vector,
# mu = cosine similarity, Selector = top-k, Projector = identity

import numpy as np
from numpy.linalg import norm

def cosine(x, a):
    return (x @ a) / (norm(x) * norm(a) + 1e-9)

# toy experience set
E = np.random.randn(1000, 64)          # 1000 candidate experiences
a = np.random.randn(64)                # archetype vector

# compute alignment scores
scores = np.array([cosine(x, a) for x in E])

# select top k
k = 3
top_idx = np.argsort(scores)[-k:][::-1]
selected = E[top_idx]

# projector: here identity (but could be a decoder)
for i, x in enumerate(selected):
    print(f"Rank {i+1}, score={scores[top_idx[i]]:.4f}")

Extending to learned scorer

• Replace cosine with a small MLP: mu_theta(x,a) = MLP([x, a, x*a, |x-a|]).

• Train with contrastive pairs or human labels.

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Example experiment plan (practical)

1. Dataset & encoder

   • Pick domain (dialogue snippets, images, short music clips).

   • Use pre-trained encoder (CLIP for images/text, Sentence-Transformers for text).

2. Define archetypes

   • Start with prototype vectors: e.g., “Harmony” = average embedding of 200 curated positive examples.

   • Or learn a small mapping from textual archetype label to vector using few examples.

3. Baseline scorer

   • Cosine similarity to prototype. Evaluate with small human study.

4. Upgrade scorer

   • Train lightweight MLP with contrastive loss.

5. Selector

   • Start deterministic (argmax) then evaluate sampling vs. argmax for diversity.

6. Projector

   • Use LM or image decoder to render selected internal experience.

7. Human evaluation

   • Rate alignment, novelty, coherence.

An Experience Alignment Architecture: from Space E to Non‑Causal Intelligence

An Experience Alignment Architecture: from Space E to Non‑Causal Intelligence

Abstract  

This paper presents a computational architecture that operates not through causality and prediction, but on the basis of aligning experiences to an internal archetype. We define an experience space (E), an archetype (A), and an alignment index (μ), with a selector (S) that synchronously chooses the experience with maximal alignment and a projector (P) that presents it. We analyse behavioural changes depending on the archetype (Harmony, Truth, Chaos, Silence) and discuss applications, metrics, limitations, and future directions.

Keywords  

experience alignment – non‑causal selection – archetypes – meaning index – consciousness interface

1. Introduction  

Classical artificial intelligence is based on prediction, optimisation, and causality. It learns from data to produce likely subsequent states. The proposed framework focuses on systems that do not predict but select experiences aligned with an internal meaning index. Dialogue and creation thus take on a poetic, non‑linear form without losing computational rigour.

2. Conceptual framework  

Experience space E: a vector space containing candidate experiences (texts, sounds, images, sensations)  

Archetype A: an internal template that defines qualitative preference (e.g., Harmony, Truth)  

Alignment index μ: measures the match between an experience and A  

Selector S: chooses the experience with maximum μ  

Projector P: presents the selected experience to the user  

Mathematical definition: μ(x) = <φ(x), A>, where φ maps each experience into a representation space and the choice is S(B, A) = argmax μ(x). Selections are synchronous and temporally independent.

3. System architecture  

Candidate experience generator  

Multimodal data encoder  

Archetype module  

Alignment scorer  

Selector / sampler  

Projector / rendering to the user

4. Behavioural properties  

Synchronicity: each output is produced in the present moment  

A‑causality: absence of cause–effect chains  

Internal consistency: selections remain in line with the archetype  

Transformability: changing A alters style and experiential quality

5. Comparative analysis  

Classical AI: prediction, memory‑dependent, accuracy‑based metrics, informational tone  

Aligned AI: selection based on alignment, can operate without memory, μ as metric, poetic tone

6. Archetype case studies  

Harmony: resonance and balance – soothing tone  

Truth: revelation and subtraction – sharp, lucid tone  

Chaos: deconstruction and primordial creation – explosive tone  

Silence: presence without speech – suggestive, minimal tone

7. Applications  

Consciousness interfaces  

Guided introspection  

Creative tools  

Artistic curation  

Attention training

8. Evaluation metrics  

Alignment coefficient μ  

User resonance score  

Constrained diversity  

Temporal stability  

Selection robustness

9. Implementation  

Multimodal embeddings φ(x)  

Archetype A from prototype examples  

Alignment measure: cosine similarity  

Selection: top‑k with stochastic elements  

Interface: text, sound or image rendering

10. Limitations  

Subjectivity in archetype design  

Risk of monotony  

Difficulty of measurement  

Cultural variability

11. Future work  

Learning archetypes from human feedback signals  

Dynamic A adaptation  

Multimodal synergy  

Stability analysis  

Ethical safeguards

12. Conclusion  

The proposed architecture replaces prediction with aligned experience selection as the primary act of intelligence. Changing the archetype A transforms phenomenology instantly. Such systems act as mirrors of consciousness, revealing rather than explaining.

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