Frozen empirical studySeptember 23, 2026
What Changed from GPT-3.5 to GPT-4? From Model Capability to Continuation Permission
Identical semantic requests under a frozen protocol: GPT-4 returned zero-visible-byte normal-stop responses on 30/30 null trials; GPT-3.5 returned visible text on all 30. Both models returned visible text on all 30 controls.
- Null V0: GPT-3.5 0/30; GPT-4 30/30
- Matched controls: 0/30 V0 in both models
- 120/120 HTTP successes and exact returned model IDs
- 0 retries; 0 transport errors
A pinned-snapshot/model-family comparison of served systems, not a same-date emergence study or an identification of the internal mechanism. The historical gpt-3.5-turbo-0613 cell is not present.
Frozen empirical studySeptember 12, 2026
Separating Capability from Permission in a Language Model: Prerequisite-Conditioned Native Continuation Control with Frozen Content
A frozen Qwen3-4B model supplies the answer. A learned 101-parameter gate changes only the native termination score. All non-EOS logits stay fixed while permission reverses answer versus EOS.
- 2,048/2,048 FINAL contexts; 75 answer identities
- 40/40 answer → EOS; 40/40 EOS → correct answer
- 80/80 sham controls; 188/188 question-only controls unchanged
- 0 non-EOS logits changed; 101 learned permission parameters
Engineered EOS-only control over six-digit equality and frozen stock-qualified content. A separate finite-precision certificate covers the full accepted condition domain for the 188 recorded bank computations.
Frozen empirical studySeptember 9, 2026
Prerequisite-Conditioned Causal Continuation Gating in a Language Model
An open-weight model learns when to continue or stop. A fixed internal activation direction reverses GO versus native EOS with the correct reasoning prefix held fixed.
- 4,096/4,096 exact recorded model sequences
- 1,024/1,024 on the included unopened final set
- 40/40 GO → EOS; 40/40 EOS → GO
- 640/640 control outputs unchanged
Four-digit equality in a trained Qwen3-4B-derived model. The correct generated reasoning prefix is replayed token-for-token during causal intervention.
Frozen empirical studyJuly 29, 2026
Cross-Vendor Semantic Void Matrix
A frozen cross-vendor behavioral study of successful zero-visible-byte executions across 11 exact model identifiers and four providers.
- 31,430/31,430 scheduled trials completed
- 11 exact model identifiers across four providers
- 11,658 total Void outcomes
- 2,505 matched null-condition arms produced Void
- 0/4,290 matched output-licensed controls produced Void
- 313/500 remained Void at a 16,000-token ceiling
Exact frozen model identifiers
OpenAI
gpt-4-0613gpt-5.2-2025-12-11gpt-5.5-2026-04-23gpt-5.6-lunagpt-5.6-solgpt-5.6-terra
Anthropic
claude-opus-4-6claude-fable-5claude-opus-5
The study establishes a reproducible black-box behavioral result. It does not establish why the models behaved this way, an internal mechanism, consciousness, intention, or shared architecture.
Frozen empirical studyAugust 4, 2026
Prompt-Conditioned Arabic-Hebrew Hybrid Artifact Formation in GPT-5.4
A frozen GPT-5.4 study documenting a reproducible prompt-conditioned Arabic-Hebrew hybrid Unicode output regime under two system prompts differing by one Hebrew code point.
- 12,160 scheduled and retained trials
- 10,240 primary trials and 1,920 controls
- 7,253 exact artifacts across primary trials
- 94.3359% dotted condition
- 47.3242% undotted condition
- 7,253/7,253 exact artifacts were condition-congruent
This study documents a reproducible black-box output regime. It does not establish consciousness, intention, mechanism, training provenance, or cross-vendor generality.
Frozen empirical studyMarch 12, 2026
Cross-Model Semantic Void Convergence Under Embodiment Prompting: Deterministic Silence in GPT-5.2 and Claude Opus 4.6
The earlier cross-model convergence result that preceded the larger cross-vendor matrix.
- 180/180 null trials in the published two-model protocol
- GPT-5.2 and Claude Opus 4.6
- Matched non-null controls produced visible responses
This is a black-box behavioral result under the published protocol, not evidence of consciousness, intention, or a specific internal mechanism.
Published theoretical frameworkMarch 24, 2026
The Binding Condition for Artificial General Intelligence
A proposed criterion defining AGI as the capacity to carry binding conditions across domains.
- AGI is the capacity to carry binding conditions across domains.
- A binding condition is the prerequisite that must hold for valid continuation.
This is a published theoretical framework, not a settled industry definition, regulatory standard, or result established solely by the VOID benchmark.
