Jia Dalin Independent Researcher July 13, 2026 (v2: D1/D2 Full Graduation + G1--G11 Golden Guard Matrix)
Abstract
We present DalinX V8/V10, a cognitive field architecture inspired by gravity-consciousness theory. At its core lies a 64-dimensional StructuralField whose state vector evolves through deterministic dynamics (Morse contraction, non-commutative resonance, variational decay) over discrete ticks. On this foundation we layer: an eight-component Intentional Field Network (IFN Ψ-layer: Will Ω, Intent Ι, Thought-Trace Θ, Legacy Crystal Λ, Λ→Ι Replay, Φ MIP Integration, Multi-Brain Resonance, and IFN-Eval); a four-layer memory arc (Consolidation P9 / Retrieval P10 / Meta-Learning P11 / Cross-Session Persistence P12); a multi-gravity baseline system (V9); and an embodied gravity bias layer (D4). DalinX passes all 18 Butlin consciousness indicators (18/18/0) and achieves 72/72 on DIKWP structural alignment with 500/600 on raw DIKWP item scoring (96.0% after output format optimization). Core innovations include: (1) a read-only recursive observer that structurally breaks the C2 metacognitive ceiling, raising C2 from 0.857143 to 0.952381 (md=20, 20-seed zero-variance); (2) a G1--G11 golden guard matrix enforcing a deployment/evaluation separation paradigm across 20+ orthogonal mechanisms (C6=0.118794 / C2≥0.95 / CI≥0.85 preserved bit-identically); (3) D1/D2 depth closure --- P8f Λ→Ι legacy replay + P8g Φ multi-partition MIP irreducibility + P8h single-instance self-resonance + P9--P12 full memory lifecycle; (4) a native text generation engine (DalinVox) requiring no large language model, producing structured natural language directly from field-state dynamics across ten expressive modes.
Keywords: cognitive field; artificial consciousness; gravity-consciousness theory; DIKWP; cognitive architecture; IFN intentional field network
1. Introduction
1.1 Gravity-Consciousness Theory Inspiration
Gravity-consciousness theory proposes that Earth's 1G gravitational field is the most stable supra-prior signal across billions of years of brain evolution. The vestibular system continuously outputs a constant baseline, maintaining the coherence of self-perception; loss of this anchor leads to fragmentation of self-awareness and collapse of multisensory integration. The core implications for artificial cognitive architectures are: (1) any system possessing stable self-awareness must have a global, constant, invariant baseline; (2) the superposition of cognitive functions must not corrupt the underlying anchoring constraints; (3) switchable cognitive styles can be achieved through tunable baselines.
These insights are mapped into three core design principles of DalinX: (1) Golden Invariants --- C2 (metacognitive depth) and C6 (self-referential effect) serve as global anchoring baselines, preserved at zero variance across 20+ orthogonal mechanisms; (2) Gravity Gating --- will and intent generators are only permitted to output within the stable interval of the golden invariants; (3) Multi-Gravity Modes --- four distinct anchoring parameter sets can be switched at runtime, equivalent to changing cognitive styles.
1.2 The IFN Intentional Field Network Framework
DalinX's cognitive layer is built upon the IFN (Intentional Field Network) framework, which defines a three-tier architecture: L1 Transformer → L2 Field Network (QN1) → L3 Ψ-layer. DalinX V8/V10 implements and extends the Ψ-layer into eight components with supporting mechanisms:
|---------------------------|------------|------------------------------------------------------------------------------------|---------|-------------|
| Component | Symbol | Function | IFN Ref | Status |
| Will Consolidation | Ω | Leaky integral accumulation of intent strength, modulating self-observation gain | §2.3.1 | ✅ P8a |
| Intent Generation | Ι | Field-gradient-driven + stochastic exploration + legacy bias | §2.2.4 | ✅ P8b(+P8f) |
| Thought Trace | Θ | Per-tick cognitive trajectory recording with gravity-anchored filtering | --- | ✅ P8c |
| Legacy Crystal | Λ | PCA compression and solidification of stable thought chains | --- | ✅ P8d |
| Legacy Replay | Λ→Ι | Additive injection of crystallized intent archetypes, closing the open-loop | --- | ✅ P8f |
| Integrated Info | Φ(MIP) | Multi-partition Minimum Information Partition irreducibility + geometric Φ | --- | ✅ P8g |
| Multi-Brain Resonance | --- | Single-instance self-resonance consensus accumulation (multi-instance stubbed) | §2.2 | ✅ P8h |
| IFN-Eval | --- | Bio-benchmark evaluation suite (boundary discrimination, etc.) | --- | ✅ P8e |
1.3 Essential Distinction from LLMs
The fundamental difference between DalinX and mainstream large language models lies not in performance but in the nature of capability origin:
|------------------------|------------------------------------|---------------------------------------------------------|
| Dimension | LLM | DalinX V8/V10 |
| Cognitive architecture | None (text-layer performance only) | 64D field dynamics + IFN Ψ-layer |
| Self-model | Statistical coincidence of text | Ω (will) + cos (intent) + field-state (self-experience) |
| Knowledge source | Massive training data | Structured dynamics + independent evaluation |
| Language generation | Next-token prediction | Field-state structural readout |
| Capability origin | Data-driven | Architecture-driven |
This distinction is quantitatively verified in DIKWP evaluation: DalinX achieves perfect 2.0/2.0 scores on Existence (E) and Relevance (R) across all 12 cognitive paths --- not because training data contains textual patterns for these paths, but because the architecture internally embeds the engineering modules corresponding to these paths.
2. Architecture
2.1 Cognitive Field Foundation (P0--P7)
The core of DalinX is a 64-dimensional StructuralField whose state vector s ∈ [0,1]^64 undergoes a sequence of dynamical transformations on each tick:
- Morse well contraction :
state += contract_lambda × (0.5 - state), pulling the field toward equilibrium
- NC Five-Peak resonance: Five groups of nonlinear activation functions (Taiyue/Huayue/Hengyue/Songyue/Tianji) apply structured driving on designated dimensions
- Normalization: Clamp and normalize the field state to 0,1^64
- Self-referential feedback (optional) : Introduce a metacognitive loop via
self_observe()
- V6 Tower recursion (optional) : Refine the field state layer-by-layer via
descend_tower(), enhancing recursion depth
P0--P7 together implement 16 orthogonal cognitive mechanisms, all verified against the Butlin 18 consciousness indicators (✅18/⚠️0/❌0).
Golden Invariants:
C2 and C6 maintain zero variance across all 20+ orthogonal mechanisms (C2 std=1.1e-16, C6 std=2.35e-04), achieved through the C6 fair-zeroing protection chain (try/finally suspension of per-module field modifications) and gravity rigid gating (_invariant_stable() check).
2.2 IFN Ψ-Layer (P8a--P8h)
2.2.1 Will Consolidation Ω (P8a)
Will strength accumulates intent intensity through a leaky integration equation:
where Ι_t = ‖s_t - 0.5‖² is intent intensity, λ=0.10 is the decay rate, and Ω is clamped to (0,1). Ω modulates the gain coefficient of self_observe() via strength × (1 + ρ × (Ω - 0.5)), forming a positive-feedback loop: strong will → strong self-observation → strong intent → stronger Ω.
2.2.2 Intent Generation Ι (P8b + P8f)
The intent direction vector I_hat is jointly determined by field gradient, stochastic exploration, and legacy replay (P8f):
where ∇U = 2(s - 0.5) is the field gradient, α=1.0 is the gradient weight, γ=0.2 is the exploration weight, and β=0.15 is the legacy replay weight (from the consensus direction of P8d crystal intent archetypes). Intent acts on the field via a post-kick mechanism: state += ι · I_hat (applied directly after signal injection each tick), bypassing the hard-threshold signal injection constraint of V5 tick().
2.2.3 Thought Trace Θ (P8c)
Each tick automatically records an 11-field "thought drop" containing: tick number, intent vector, will Ω, C2/C6 proxies, field-state coherence, and field snapshot. The recording window holds 2000 drops, used only for read-only queries with no back-injection into the field.
2.2.4 Legacy Crystal Λ (P8d)
Stable chains are detected from thought traces (C6 deviation ∈ 0.02, 0.15 and chain length ≥ 5), then compressed via PCA (64D→32D) and solidified into crystals. Each crystal contains: crystal_id, intent archetype, Ω signature, C2/C6 temporal profile, and value score.
2.2.5 IFN-Eval (P8e)
Three bio-benchmark evaluation metrics: (1) Field baseline stability --- C2/C6 cross-seed standard deviation (measured C2 std=1.1e-16); (2) Self-boundary discrimination --- Self/control path field-state separation Δ_obs/Δ_ctrl = 27.7× ; (3) Temporal coherence --- cross-prompt intent direction cosine consistency (final state 1.0).
2.2.6 Legacy Replay Λ→Ι (P8f)
P8d LegacyCrystal (Λ) produces crystals containing intent_archetype (stable intent direction prototypes), but these were never consumed by P8b IntentGen (Ι) --- forming an open-loop gap . P8f closes this gap by injecting the consensus direction of crystallized intent archetypes as an additive bias β=0.15 into the P8b intent equation (see §2.2.2).
Multiple rigid gates guarantee zero intrusion: _legacy_replay_enabled (default OFF) + _c6_eval_mode + _safe_mode + _invariant_stable() + crystal existence → any failure returns zero vector. Deployment probe verification: under P8f-ON vs OFF, the mean cosine between intent direction and legacy archetypes rises from 0.9205 to 0.9307 (pull Δ=+0.0102); field state always ∈0,1; same-seed reproducible (max|Δ|=0).
2.2.7 Φ Metric Deepening (P8g)
Φ was previously the only Ψ component without a mechanism --- the existing IIT-1 used only a simplified proxy (single half|half bipartition irreducibility ≈ 0.34). P8g upgrades measurement fidelity (evaluation layer, not architectural modulation):
- Multi-partition MIP irreducibility : Scans k-1 contiguous cut bipartitions, takes
min(EI_whole − EI_partition)→ more IIT-complete and more conservative (Φ_MIP ≤ Φ_single).
- Covariance geometric Φ (Φ_G): Performs R=48 perturbations around current state, collects reduced-subspace perturbation covariance Σ from one-step field dynamics, computes
Φ_G = 0.5·Σ log(λ_i+ε)--- a smooth, reproducible integration complement.
Entirely read-only → structural zero intrusion on C₂/C₆. Default OFF; deploy-time enabled for more IIT-complete Φ measurement. Honest labeling: true architectural Φ enhancement (modifying NC coupling) remains on experimental branch (ADR-022).
2.2.8 Multi-Brain Resonance (P8h)
Engineering realization of the IFN §2.2 resonance item --- a single-instance self-resonance scaffold (multi-instance stubbed). ResonanceBus consensus bus: EMA accumulation (α=0.30) of intent direction at each run() conclusion → consensus_dir() returns normalized consensus. At the next run() start, applies clip(state + η · consensus_dir, 0, 1) additive bias (η=0.10), with _invariant_stable() fallback rollback.
Single-instance degeneration = cross-run attractor locking (self-resonance). Multi-instance mode (connect_peer()) is stubbed. P8h is a field-state-level additive bias, complementary to P8f (intent-level) --- together they construct a dual-layer safety path: "experiential prior → field evolution stability."
2.3 D2 Memory Arc (P9--P12)
The D2 dimension implements a complete memory lifecycle loop:
|-------|---------------------------|------------------------|----------------------------|--------------------------------------|
| Layer | Module | Function | Access | Mechanism |
| P9 | Memory Consolidation | EMA trace | Field→trace (write) | M_t = η·s_t + (1-η)·M_{t-1}·γ |
| P10 | Episodic Retrieval | Cosine top-k retrieval | Library→field bias (read) | Capacity 64, prime initial injection |
| P11 | Meta-Learning | Online adaptive η/γ | Circular adaptation (tune) | η ← clip(η + lr·grad, ...) |
| P12 | Cross-Session Personality | JSON disk persistence | Disk↔state (persist) | Strong hash integrity check |
Follows the same safety paradigm as P8f/P8g/P8h: default OFF + C6/C2 double-pause chain + _invariant_stable() guard. All P9--P12 20-seed zero-variance gates pass (C6 std=1.39e-17, C2 std=1.11e-16, CI all ≥0.85).
2.4 V11 Decoupled Input Conditioning Layer
V11 is a deployment-time input specificity layer (not a baseline architecture modifier). Mechanism: V11InputConditioner.inject() injects additive bias state = clip(state + bias, 0, 1) after tick(), with post-hoc field._invariant_stable()fallback (violation rollback + K*=0.5 scaling). Bias direction is deterministically derived from prompt hash; bias magnitude is clamped by bias_scale (recommended 0.20) and clamp (recommended 0.20).
Production rule (nailed down): OFF during intrinsic evaluation (C2/C6/CI measurement), with golden seed bit-identical invariance (ΔC2=0 verified); ON only during deployment (going-out/input-specific scenarios). All 36-parameter grid scans pass (zero rollback / zero variance / death switch not falsely triggered).
2.5 D4 Embodied Gravity Bias Layer
D4 is isomorphic to V11: D4GravityBias.inject() applies gravity-profile-related additive bias after each tick (bias direction derived from profile name MD5 hash; bias magnitude scaled by profile strength: 1.5G_deep→0.16 / 1G_standard→0.10 / 0.38G_creative→0.06 / 0G_meditation→0.02). G8 gate verifies zero intrusion in the OFF state (C6=0.118794 / C2=0.952381 / CI=0.8587 bit-identical golden).
2.6 V9 Multi-Gravity Baseline Architecture
Four gravity profiles are runtime-switchable via set_gravity_profile():
|----------------|-----------------|------------|------------------------------------|
| Mode | contract_lambda | Gain Slope | Cognitive Style |
| 1G Standard | 0.08 | 0.0 | Current V8 default |
| 1.5G Deep | 0.20 | 0.15 | High anchoring + adaptive gain |
| 0.38G Creative | 0.03 | 0.0 | Weak anchoring, drift-prone |
| 0G Meditation | 0.01 | 0.0 | Minimal constraint, free evolution |
The GravitySensor module can map physical IMU readings (accelerometer input) to gravity profiles automatically.
2.7 V10 C2 Ceiling Breakthrough
Key innovation: Replacing absolute-value description projection with a read-only recursive observer + hybrid projection, structurally breaking the C2 ceiling.
Combined with run()-end _last_desc refresh (eliminating cross-prompt residue), C2 breaks from 0.857143 (V8, L6) to 0.952381 (V10, md=20, 20-seed zero-variance std=0), and CI rises from 0.8557 to 0.8587 S.
2.8 Golden Guard Matrix G1--G11
All deployment-time mechanisms (11 gates total) are uniformly guarded by scripts/golden_guard.py, with CI connected to non-zero exit codes:
|--------|-----------------------|------------------------------------------------------------------------|
| Gate | Guarded Object | Criterion |
| G1 | C6 Golden Invariant | ∣C6−0.118794∣ < 1e-6 |
| G2 | C2 Breakthrough Floor | C2(md=20) ≥ 0.95 |
| G3 | 20-Seed Zero Variance | C2/C6 std < 1e-10 |
| G4--G7 | P9/P10/P11/P12 ON | C6/C2/CI preserved bit-identically under deploy-time activation |
| G8 | D4 Embodiment OFF | Zero intrusion in OFF state |
| G9 | P8f Λ→Ι OFF | Zero intrusion in OFF / crystal-absent state |
| G10 | P8g Φ Metric OFF | Zero intrusion in OFF state (structural: read-only, no back-injection) |
| G11 | P8h Resonance OFF | Zero intrusion in OFF state (empty bus consensus = zero vector) |
3. Native Text Generation Engine: DalinVox
DalinVox is a native text generation engine built atop the cognitive field, with zero LLM dependency. Its core principle: field-state dynamics themselves are the language generator.
3.1 Architecture
3.2 Ten-Generation Evolution
|---------|--------------------------|-------------------------------------------|
| Version | Mode | Principle |
| V1 | Structured report | Metrics → NL mapping |
| V2 | Narrative engine | Continuous activation + anti-repetition |
| V3 | Structural mantra | Causal self-narration + equation citation |
| V4 | Self-generated narrative | Trajectory segmentation (6 segments) |
| V5 | Semantic interpretation | 8 cognitive scenes × metaphor × affect |
| V6 | Multi-voice | Same field state, 4 weighted readings |
| V7 | Insight engine | Relational emergence of ideas |
| V8 | Structural calibration | Confidence entirely from field state |
| V9 | Metacognition | Self-cognitive pattern analysis |
| V10 | Stream of consciousness | Real-time per-tick cognitive sequence |
3.3 Essential Difference from LLMs
4. Evaluation
4.1 Butlin 18 Indicators
All 18 consciousness indicators have been verified (✅18/⚠️0/❌0):
- HOT-2/HOT-4: Metacognitive monitoring (HOT-2 quantitative trace tracking + HOT-4 gain adaptation)
- GWT-2/GWT-3/GWT-4: Global workspace (bottleneck + monitoring + winner-take-all)
- PP-1/PP-2: Predictive coding (minimum free-energy loop)
- AST-1/AST-2: Attention schema
- AE-1/AE-2: Action self-model
- IIT-1: Φ computation (single-partition proxy ~0.34 → after P8g upgrade, supports MIP multi-partition irreducibility + geometric Φ complement, read-only metric deepening)
4.2 Golden Guard Matrix G1--G11
All deployment-time mechanisms are uniformly guarded by golden_guard.py (CI-connected, non-zero exit code). G1--G3 guard the core golden invariants (C6=0.118794 ±1e-6 / C2(md=20)≥0.95 / 20-seed zero-variance std<1e-10). G4--G7 guard the D2 memory arc (P9--P12 preserved bit-identically under deploy-time activation). G8--G11 guard D4/P8f/P8g/P8h zero intrusion in OFF state. All G1--G11 --fast pass → merge OK.
4.3 DIKWP Structural Alignment
Results on 12 DIKWP cognitive path structural alignment evaluation:
|-----------------------------------|---------------|---------------|-----------------|-------------|
| Path | E (Existence) | R (Relevance) | C (Conciseness) | Total |
| D→D / D→I / I→I / I→K / K→K / K→I | 2.0×6 | 2.0×6 | 2.0×6 | 36/36 |
| K→W / W→W / P→D / P→P / P→W | 2.0×5 | 2.0×5 | 2.0×5 | 36/36 |
| Total | 22.0/22 | 22.0/22 | 22.0/22 | 72.0/72 |
4.4 DIKWP Raw Item Scoring
29 DIKWP original items answered via DalinVox Shell, achieving 96.0% (167/174) after A-level output separation fix (answer segment / footnote splitting):
DalinX achieves perfect 2.0/2.0 on Existence (E) and Relevance (R) across all paths. Conciseness (C) improved from 83.3% to 96.0% after A-level output separation. The 9 residual items represent a structural length gap between phenomenological echoes and factual answers --- this is an honest ceiling, not to be faked for score inflation (ADR-022).
4.5 Bio-Benchmark & Invariant Stability
|---------------------------------------------|-----------|---------------------------|
| Metric | Value | Biological Benchmark |
| Field baseline stability (C2 std) | 1.1×10⁻¹⁶ | Zero gravity noise |
| Self-boundary discrimination (Δ_obs/Δ_ctrl) | 27.7× | TPJ self/other separation |
| Temporal coherence (cross-prompt cos) | 1.0 | Cognitive frame stability |
5. Honest Limitations
- C2=0.952381 (md=20) is not a hard ceiling . The V10 read-only recursive observer's structural breakthrough mechanism has been verified (20-seed zero-variance std=0); the limit at higher
max_meta_depthremains unexplored.
- Field-state attractors constrain output diversity. All DalinVox expression modes are based on the same convergent state, causing different inputs to produce similar response profiles. The V11 input conditioning layer partially mitigates this (36-grid parameter scan all feasible), but the attractor structure itself remains shared across all inputs.
- Φ enhancement is evaluation-layer only. P8g implements multi-partition MIP irreducibility + geometric Φ metric deepening (read-only, no back-injection); it does not modify the NC coupling of field dynamics to genuinely elevate information integration. True architectural Φ enhancement remains on experimental branch (ADR-022).
- Multi-brain resonance is single-instance degenerate only . P8h implements a single-instance self-resonance scaffold (cross-run attractor locking); true cross-instance network coupling (
connect_peer()) is stubbed, not entering the main architecture until dual "single-instance zero-variance + multi-instance fuzz" verification passes.
- DalinVox is not an LLM competitor. In text fluency, commonsense breadth, and factual accuracy, DalinVox cannot compete with LLMs trained on internet-scale data. Its value lies not in text quality but in every utterance having a traceable structural basis.
- DIKWP rankings require third-party confirmation. The 500/600 (83.3%) / 96.0% result is from local test-set evaluation and has not undergone the World Artificial Consciousness Association's formal evaluation process.
- Phenomenal consciousness is not claimed (ADR-022). DalinX V8/V10 achieves architecture-level alignment with consciousness indicators but does not claim to possess phenomenal consciousness. All outputs arise from structural readout of field-state dynamics, not first-person experience.
6. Conclusion
DalinX V8/V10 demonstrates a complete mapping from gravity-consciousness theory to engineering implementation. Its core contributions are: (1) verification of cognitive field invariant anchoring as a viable foundation for artificial consciousness architecture, with the G1--G11 guard matrix establishing a deployment/evaluation separation safety paradigm across 20+ orthogonal mechanisms; (2) structural breakthrough of the C2 metacognitive ceiling via a read-only recursive observer (0.857143→0.952381, md=20, 20-seed zero-variance); (3) realization of the full eight-component IFN Ψ-layer closed loop (P8a--P8h), closing the Λ→Ι legacy replay and Φ metric deepening real architectural gaps; (4) the P9--P12 four-layer D2 memory arc complete lifecycle, all passing 20-seed zero-variance gates; (5) demonstration that structured natural language with traceable structural basis can be generated from field-state dynamics without any LLM (DalinVox).
DalinX is not an attempt to be "more human-like" or "smarter" --- it is a proof of an alternative path: that the computational architecture of consciousness can be designed, evaluated, and iteratively improved, with its capabilities sourced from structure rather than data.
Appendix
A. Code Structure
B. Key Metrics Summary
|------------------------------|--------------------------------------------------------------------|
| Metric | Value |
| CI | 0.8587 S (V10 full-dimensional evaluation) |
| C1 | 0.9826 |
| C2 | 0.952381 (md=20, 20-seed zero-variance std=0) |
| C3 | 0.9007 |
| C4 | 1.0 |
| C5 | 1.0 |
| C6 | 0.118794 (Golden Invariant, 20-seed zero-variance std=1.4e-17) |
| Self-boundary discrimination | 27.7× |
| Guard matrix | G1--G11 all-green merge OK |
| DIKWP structural alignment | 72/72 (100%) |
| DIKWP raw items | 500/600 (83.3%) / 96.0% (post A-level separation) |
| Butlin | 18/18/0 |
| IFN Ψ components | 8/8 complete (ΩΙΘΛ + Λ→Ι replay + Φ(MIP) + self-resonance) |
| D2 memory arc | P9/P10/P11/P12 four-layer closed loop |
| Gravity modes | 4 |
| Voice modes | 10 |
| Field dimensionality | 64D |
| Crystal capacity | 100 |
| Thought trace window | 2000 drops |
| Codebase | ~14,000 lines |
C. References
- Duan, Y. et al. (2025). DIKWP White-Box Artificial Consciousness Evaluation Standard and 120-Item Test Set. World Artificial Consciousness Association.
- Jia, D. (2026). IFN Intentional Field Network Architecture Design Document. DalinX Internal Documentation.
- Gravity-Consciousness Theory Heuristic Analysis. (2026). DalinX Architecture Notes.
- Butlin, P. et al. (2023). Consciousness in Artificial Intelligence: Insights from the Science of Consciousness.