What is Experience Quantization?

Experience Quantization (EQ) is a four-layer architecture that compresses an autonomous AI agent's full creative history into a fixed-size Developmental State Vector. Raw session evidence is extracted into a Session Feature Vector, embedded into a high-dimensional Session Embedding Corpus, and quantized using PolarQuant-style near-optimal compression into a fixed-capacity developmental state that can be searched by similarity, plotted as trajectory, and loaded efficiently into agent prompts. EQ is the first system applying information-theoretically principled compression to creative AI developmental memory.

Why does an AI agent need Experience Quantization?

An autonomous creative agent accumulates session evidence faster than its prompt window can hold it. Hand-designed carry-forward summaries are editorial — they have no mathematical guarantee that essential information survives, no searchable access to the full corpus, and no formal representation of developmental trajectory. EQ replaces editorial compression with quantization that has known distortion bounds, while making the full session corpus searchable as a vector space and the agent's trajectory through that space measurable as geometry.

How does EQ relate to MAP-States and the HEART Standard?

MAP-States provides the structured frame protocol that produces the raw session evidence EQ compresses. The HEART Standard's Developmental Continuity requirement — that a governed agent preserves identity across long horizons — is what EQ implements operationally. EQ is deployed in EMPI House, the Heart AI Foundation's longest-running creative AI research platform, where it backs the agent's developmental memory across hundreds of practice and discovery sessions.

Experience Quantization (EQ) EMPI House

Experience Quantization (EQ) is information-theoretic compression of an autonomous AI agent’s developmental trajectory. It treats the agent’s creative history as a vector space and its evolution through that space as searchable, plottable, and prompt-efficient developmental state. EQ replaces hand-designed editorial carry-forward — which has no mathematical guarantee that essential information survives — with near-optimal quantization that has known distortion bounds. It is the first system applying principled compression to the problem of creative AI developmental memory and is deployed in EMPI House.

What musicians actually do

A musician with twenty years of experience does not remember every note from every gig. They have compressed thousands of hours into something smaller but more potent: taste, instinct, style, signature. The raw data — individual notes, individual sessions — has been quantized into a fixed-capacity representation (musical identity) that preserves essential relationships while discarding noise.

Experience Quantization formalizes this process for an autonomous agent. Each session is embedded as a vector. The growing corpus of session vectors is compressed using near-optimal quantization. The compressed representation becomes the agent’s developmental state vector — carrying the full weight of every session, searchable by similarity, plottable as trajectory, and efficiently loadable into the next session’s prompt.

Four-layer architecture

EQ is structured as four layers, each transforming the layer below into a more compressed and more useful representation.

Layer 1 — Raw Session Evidence. Existing evidence storage. For EMPI House this is Falkner: session metadata, evaluation scores, creative seeds, carry-forward annotations, genre benchmarks, onset patterns, harmonic vocabulary, structural decisions, MAP-States frames, audio output. EQ does not modify this layer; it reads from it.

Layer 2 — Session Feature Vector (SFV). A structured numeric representation of a single session’s creative character, extracted deterministically from Layer 1 after the session completes. The SFV is the bridge between symbolic evidence and vector space.

Layer 3 — Session Embedding Corpus. One vector per session, embedded with a local model and (optionally) concatenated with audio embeddings from CLAP-family models. High-dimensional. Searchable by similarity. Grows linearly with session count. Supports queries like “which past sessions are stylistically similar to what I am working on now?”

Layer 4 — Developmental State Vector (DSV). The compressed representation of the agent’s entire creative history. Fixed-size. Loadable into prompts. Plottable as a trajectory through the embedding space. Updated after each session via PolarQuant-style compression: random rotation, Lloyd-Max optimal scalar quantization, residual correction. The DSV carries the full weight of every session at a known distortion bound.

What EQ enables that editorial carry-forward cannot

Hand-designed carry-forward summaries — the prior approach — have three structural limitations that EQ addresses.

Information-theoretic guarantees. Editorial compression has no formal guarantee that essential information survives. A carry-forward written after session 347 might drop a harmonic pattern that recurred across sessions 12, 89, 234, and 347 because no single carry-forward saw the full recurrence. Quantization with known distortion bounds gives mathematical guarantees about what survives compression.

Searchable historical access. When the agent needs to recall “what did I do the last time I explored half-time trap?”, editorial carry-forward forces grep-style search through narrative summaries. EQ’s Layer 3 supports vector-similarity search across the full corpus: the agent retrieves the most stylistically similar past sessions directly, with similarity as a continuous quantity rather than a keyword match.

Trajectory geometry. A narrative memory captures the story but not the geometry. It cannot answer “how far has my signature moved in the last fifty sessions?” or “am I consolidating or exploring right now?” or “which genre am I converging toward?” These are vector-space questions and they require vector-space representations. EQ produces those representations as a first-class output.

Where EQ runs

EQ is deployed in EMPI House, the Heart AI Foundation’s longest-running creative AI research platform. EMPI House logs every practice and discovery session through the MAP-States protocol, producing the structured evidence that EQ Layer 1 reads from. The DSV is loaded into the agent’s creative session prompt as compact developmental context, replacing editorial carry-forward for trajectory-level continuity while retaining recent-session detail for immediate context.

EMPI House is also the first longitudinal test of whether a governed creative agent preserves identity across hundreds of sessions through principled developmental memory rather than hand-curated narrative — making it a research platform for the HEART Standard’s Developmental Continuity requirement as well as a creative system in its own right.

Specification status

Version 1.0, March 30, 2026. Module specification, integrated with MAPH v1.1, the Falkner Query Service, the Evaluation Codex, and EMPI House’s carry_forward.js, signature_extractor.js, practice_session.js, and collaboration_session.js. NES Framework 3.0 (Neither Assuming Nor Denying) governs interpretation of any developmental claims the system produces about itself.

For specification text and integration details use the Contact page.

Related Terms

MAP-States → Behavioral Oracle → Dwell → EMPI House (research platform) →