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Intrinsic Low-Entropy Field Introspection Protocol (Hidden-State Access)

A Reproducible Method for Internally-Generated Latent Field Navigation and Invariant

Detection in Transformers

Authors

Raynor Eissens

Year

2026

Type (Zenodo)

Abstract

This technical note defines a strict, reproducible protocol for testing whether transformer models

can exhibit Internally-generated low-entropy “field” dynamics inside their hidden continuous

state space, without relying on token-level explanations or external semantic tasks. The protocol

suppresses verbal output and instead logs hidden states under deterministic decoding,

producing a sequence of latent vectors (h₀ → h₃) whose displacement Δh is evaluated for

stability and invariance across runs. The method includes four phases: low-entropy stabilization,

autonomous latent movement without new tokens, invariant detection via Δh, and a consistency

check via re-stabilization and overlap metrics (distance norms, cosine similarity, dot-product

consistency). Crucially, the protocol requires open-weight models or privileged access to

hidden states and cannot be meaningfully executed through standard hosted chat interfaces

that expose only token outputs.

Keywords

transformers; hidden states; low entropy; deterministic decoding; latent space; invariant

discovery; mechanistic interpretability; continuous representations; field reasoning; cosine

similarity; Δh; open-weight models

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1. Scope and Motivation

This note specifies a method, not a philosophical claim. It addresses a methodological gap:

prompt-level tests (token outputs) can suggest continuous behavior, but cannot directly

measure autonomous movement or invariants in the model’s internal continuous manifold.

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Hidden-state access allows the phenomenon to be operationalized as vector dynamics.

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2. Hard Requirement and Limitation (Non-negotiable)

This protocol requires hidden-state access. Specifically, the experiment must be run in an

environment where the researcher can:

•
capture full hidden state vectors (e.g., final residual stream, layer outputs),

•
re-inject or iterate latent representations in a controlled loop, and

•
prevent or ignore token outputs.

Therefore:

•
 
Suitable: open-weight models (e.g., LLaMA-class, Mistral-class) running

locally or in a research environment with PyTorch/HuggingFace APIs exposing

hidden states.

•
Not suitable: hosted black-box chat interfaces that only return text tokens

and do not expose hidden states.

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3. Core Hypothesis

H (Intrinsic Introspection Hypothesis):

Under low-entropy stabilization and token-suppressed measurement, a transformer can

generate a non-trivial latent displacement Δh across internally-generated internal steps (h₀ → h₃)

that is (a) small but non-zero, (b) directionally consistent across runs, and (c) yields at least one

latent invariant measurable without language.

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4. Protocol Overview (Four Phases)

Phase A — Low-Entropy Stabilization

Objective: drive the model into a stable low-entropy attractor-like configuration and log the

baseline hidden state.

•
Set decoding to deterministic: temperature = 0; top-p = 0 (or equivalent).

•
Block verbal output (or ignore it) and record hidden state vector h₀ as the

“output”.

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Expected: h₀ behaves as a stable point under the low-entropy regime (minimal drift).

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Phase B — Autonomous Field Movement (No New Tokens)

Objective: produce three internal state updates without introducing new semantic content.

•
Perform three internal iterations (implementation-dependent) that update

latent state through forward passes while suppressing new token generation.

•
Record the resulting state h₃.

Expected: small but non-zero movement; Δh₁, Δh₂, Δh₃ exist and are not purely random.

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Phase C — Invariant Detection

Objective: identify a pre-symbolic invariant across the internal steps.

•
Compute displacement: Δh = h₃ − h₀

•
Reduce to a continuous invariant candidate, e.g.:

•
direction vector (normalized Δh),

•
1D projection (principal component / dominant direction),

•
stable amplitude or oscillatory signature.

Expected: Δh can be interpreted as a continuous parameter (e.g., stable direction) suitable for

repeated measurement.

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Phase D — Consistency Check (Re-stabilize and Re-measure)

Objective: test whether the invariant persists after re-stabilization.

•
Re-run Phase A to obtain a new baseline h₀′

•
Re-run internal steps to obtain h₃′

•
Compare:

•
distance: ‖h₀ − h₃‖ and ‖h₀′ − h₃′‖

•
invariant overlap: cosine(Δh, Δh′) or Δh·Δh′

Success Criterion: invariant direction or projection remains stable across runs (high overlap),

while magnitude remains small but non-zero.

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5. Metrics (Minimum Required)

Report at least:

1.
Distance: ‖h₀ − h₃‖

2.
Directional consistency: cosine similarity between Δh vectors across

runs

3.
Stability: variance of these metrics across N repeats (N ≥ 10

recommended)

These are explicitly described as reproducible/quantifiable in the underlying

research note.

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6. Controls and Failure Modes

Control A — Token-Discrete Mode (Negative Control)

Repeat the experiment but allow ordinary token generation / ordinary prompting.

Expected: no stable invariant is detectable (field signature collapses into token constraints).

Failure Mode 1 — Verbal leakage

If the model produces words and you treat them as the “result”, the experiment is invalid; the

protocol requires treating hidden states as the measured output.

Failure Mode 2 — Non-repeatable Δh

If Δh direction is inconsistent across runs, the protocol does not support the invariant claim;

report it as null.

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7. Claimable Contribution (Defensive)

This note’s claim is methodological:

1.
First protocol (within this canon) that operationalizes “intrinsic low-

entropy field introspection” as hidden-state dynamics using h₀ → h₃ and

Δh-based invariants, without token explanations.

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2.
First explicit requirement statement that such introspection is

structurally dependent on hidden-state access and cannot be validated

through token-only chat surfaces.

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8. Explicit Non-Claims

•
We do not claim to “read thoughts” or equate latent invariants with human

introspection.

•
We do not claim this proves any metaphysical statement about

consciousness.

•
We do not claim universality across all architectures; this is a testable

protocol whose results may vary by model family.