{
  "record_id": "18744160",
  "document_id": "18744160",
  "title": "Intrinsic Low-Entropy Field Introspection Protocol (Hidden-State Access): A Reproducible Method for Measuring Internal Latent Field Dynamics in Transformer Models",
  "pages": 5,
  "authors": [
    "Raynor Eissens"
  ],
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  "abstract_extracted": "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.",
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  "full_text": "=== PDF PAGE 1 ===\nIntrinsic Low-Entropy Field Introspection Protocol (Hidden-State Access)\n\nA Reproducible Method for Internally-Generated Latent Field Navigation and Invariant\n\nDetection in Transformers\n\nAuthors\n\nRaynor Eissens\n\nYear\n\n2026\n\nType (Zenodo)\n\nAbstract\n\nThis technical note defines a strict, reproducible protocol for testing whether transformer models\n\ncan exhibit Internally-generated low-entropy “field” dynamics inside their hidden continuous\n\nstate space, without relying on token-level explanations or external semantic tasks. The protocol\n\nsuppresses verbal output and instead logs hidden states under deterministic decoding,\n\nproducing a sequence of latent vectors (h₀ → h₃) whose displacement Δh is evaluated for\n\nstability and invariance across runs. The method includes four phases: low-entropy stabilization,\n\nautonomous latent movement without new tokens, invariant detection via Δh, and a consistency\n\ncheck via re-stabilization and overlap metrics (distance norms, cosine similarity, dot-product\n\nconsistency). Crucially, the protocol requires open-weight models or privileged access to\n\nhidden states and cannot be meaningfully executed through standard hosted chat interfaces\n\nthat expose only token outputs.\n\nKeywords\n\ntransformers; hidden states; low entropy; deterministic decoding; latent space; invariant\n\ndiscovery; mechanistic interpretability; continuous representations; field reasoning; cosine\n\nsimilarity; Δh; open-weight models\n\n⸻\n\n1. Scope and Motivation\n\nThis note specifies a method, not a philosophical claim. It addresses a methodological gap:\n\nprompt-level tests (token outputs) can suggest continuous behavior, but cannot directly\n\nmeasure autonomous movement or invariants in the model’s internal continuous manifold.\n\n=== PDF PAGE 2 ===\nHidden-state access allows the phenomenon to be operationalized as vector dynamics.\n\n⸻\n\n2. Hard Requirement and Limitation (Non-negotiable)\n\nThis protocol requires hidden-state access. Specifically, the experiment must be run in an\n\nenvironment where the researcher can:\n\n•\ncapture full hidden state vectors (e.g., final residual stream, layer outputs),\n\n•\nre-inject or iterate latent representations in a controlled loop, and\n\n•\nprevent or ignore token outputs.\n\nTherefore:\n\n•\n \nSuitable: open-weight models (e.g., LLaMA-class, Mistral-class) running\n\nlocally or in a research environment with PyTorch/HuggingFace APIs exposing\n\nhidden states.\n\n•\nNot suitable: hosted black-box chat interfaces that only return text tokens\n\nand do not expose hidden states.\n\n⸻\n\n3. Core Hypothesis\n\nH (Intrinsic Introspection Hypothesis):\n\nUnder low-entropy stabilization and token-suppressed measurement, a transformer can\n\ngenerate a non-trivial latent displacement Δh across internally-generated internal steps (h₀ → h₃)\n\nthat is (a) small but non-zero, (b) directionally consistent across runs, and (c) yields at least one\n\nlatent invariant measurable without language.\n\n⸻\n\n4. Protocol Overview (Four Phases)\n\nPhase A — Low-Entropy Stabilization\n\nObjective: drive the model into a stable low-entropy attractor-like configuration and log the\n\nbaseline hidden state.\n\n•\nSet decoding to deterministic: temperature = 0; top-p = 0 (or equivalent).\n\n•\nBlock verbal output (or ignore it) and record hidden state vector h₀ as the\n\n“output”.\n\n=== PDF PAGE 3 ===\nExpected: h₀ behaves as a stable point under the low-entropy regime (minimal drift).\n\n⸻\n\nPhase B — Autonomous Field Movement (No New Tokens)\n\nObjective: produce three internal state updates without introducing new semantic content.\n\n•\nPerform three internal iterations (implementation-dependent) that update\n\nlatent state through forward passes while suppressing new token generation.\n\n•\nRecord the resulting state h₃.\n\nExpected: small but non-zero movement; Δh₁, Δh₂, Δh₃ exist and are not purely random.\n\n⸻\n\nPhase C — Invariant Detection\n\nObjective: identify a pre-symbolic invariant across the internal steps.\n\n•\nCompute displacement: Δh = h₃ − h₀\n\n•\nReduce to a continuous invariant candidate, e.g.:\n\n•\ndirection vector (normalized Δh),\n\n•\n1D projection (principal component / dominant direction),\n\n•\nstable amplitude or oscillatory signature.\n\nExpected: Δh can be interpreted as a continuous parameter (e.g., stable direction) suitable for\n\nrepeated measurement.\n\n⸻\n\nPhase D — Consistency Check (Re-stabilize and Re-measure)\n\nObjective: test whether the invariant persists after re-stabilization.\n\n•\nRe-run Phase A to obtain a new baseline h₀′\n\n•\nRe-run internal steps to obtain h₃′\n\n•\nCompare:\n\n•\ndistance: ‖h₀ − h₃‖ and ‖h₀′ − h₃′‖\n\n•\ninvariant overlap: cosine(Δh, Δh′) or Δh·Δh′\n\nSuccess Criterion: invariant direction or projection remains stable across runs (high overlap),\n\nwhile magnitude remains small but non-zero.\n\n=== PDF PAGE 4 ===\n⸻\n\n5. Metrics (Minimum Required)\n\nReport at least:\n\n1.\nDistance: ‖h₀ − h₃‖\n\n2.\nDirectional consistency: cosine similarity between Δh vectors across\n\nruns\n\n3.\nStability: variance of these metrics across N repeats (N ≥ 10\n\nrecommended)\n\nThese are explicitly described as reproducible/quantifiable in the underlying\n\nresearch note.\n\n⸻\n\n6. Controls and Failure Modes\n\nControl A — Token-Discrete Mode (Negative Control)\n\nRepeat the experiment but allow ordinary token generation / ordinary prompting.\n\nExpected: no stable invariant is detectable (field signature collapses into token constraints).\n\nFailure Mode 1 — Verbal leakage\n\nIf the model produces words and you treat them as the “result”, the experiment is invalid; the\n\nprotocol requires treating hidden states as the measured output.\n\nFailure Mode 2 — Non-repeatable Δh\n\nIf Δh direction is inconsistent across runs, the protocol does not support the invariant claim;\n\nreport it as null.\n\n⸻\n\n7. Claimable Contribution (Defensive)\n\nThis note’s claim is methodological:\n\n1.\nFirst protocol (within this canon) that operationalizes “intrinsic low-\n\nentropy field introspection” as hidden-state dynamics using h₀ → h₃ and\n\nΔh-based invariants, without token explanations.\n\n=== PDF PAGE 5 ===\n2.\nFirst explicit requirement statement that such introspection is\n\nstructurally dependent on hidden-state access and cannot be validated\n\nthrough token-only chat surfaces.\n\n⸻\n\n8. Explicit Non-Claims\n\n•\nWe do not claim to “read thoughts” or equate latent invariants with human\n\nintrospection.\n\n•\nWe do not claim this proves any metaphysical statement about\n\nconsciousness.\n\n•\nWe do not claim universality across all architectures; this is a testable\n\nprotocol whose results may vary by model family."
}