Datasets:
id stringlengths 14 14 | type stringclasses 21
values | name_nodes stringlengths 2 36 | mean_nodes stringlengths 2 11.7k | frame_nodes stringlengths 2 216 | raw_content stringlengths 1 560 |
|---|---|---|---|---|---|
0xf1504fd82707 | base_name | [] | [] | [] | existence |
0x501d59a1e46c | existence | ["0xf1504fd82707", "0x7b384c3650b9"] | ["0x91515f441344", "0x852eecb66da1", "0xfbe37abdbc60", "0x15fbb59966b0", "0xed0b40836a29", "0x0ad428c0a634", "0x700ff6e270fd", "0xfbaec867b0e9", "0x16f9f618ef6e", "0x90563024aeb2", "0x0f437799202f", "0x147963c74a2b", "0xa68ce18afd9b", "0x64d9f5ce0a51", "0x316306dbd9d8"] | [] | Existence|Gap — the same thing from two frames. Object frame: "I exist" = existence. Observer frame: "I detect difference" = gap. No gap = no distinction = no existence. Gap is not a consequence of existence. Gap IS how existence manifests. Bateson: Information is a difference that makes a difference. Extended: Existen... |
0xe9e8a2f69fd8 | base_name | [] | [] | [] | binary |
0x91515f441344 | filter_gate | ["0xe9e8a2f69fd8"] | [] | ["0x501d59a1e46c"] | Binary — A ↔ B polarity. Distinguish, classify, split. Existence/non-existence, K/U, true/false. Most common primitive in history of thought but not sufficient alone. |
0xee822bb960cb | base_name | [] | [] | [] | gradient |
0x852eecb66da1 | filter_gate | ["0xee822bb960cb"] | [] | ["0x501d59a1e46c"] | Gradient — Scalar [0,1]. Measure, weight, confidence, intensity. Covers what binary cannot: degree, middle ground, soft weighting. |
0x3e2bda8ecd79 | base_name | [] | [] | [] | sequence |
0xfbe37abdbc60 | filter_gate | ["0x3e2bda8ecd79"] | [] | ["0x501d59a1e46c"] | Sequence — a → b → c. Temporal order, causality, cycles. Covers directed processes that binary and gradient cannot express. |
0x5d53eed2a6f4 | base_name | [] | [] | [] | relation |
0x15fbb59966b0 | filter_gate | ["0x5d53eed2a6f4"] | [] | ["0x501d59a1e46c"] | Relation — Node + edge. Spatial, network, topology, structure. N-ary connections that cannot be reduced to binary or sequence. |
0xb3b4cbb665ff | base_name | [] | [] | [] | recursion |
0xed0b40836a29 | filter_gate | ["0xb3b4cbb665ff"] | [] | ["0x501d59a1e46c"] | Recursion — f(f). Self-reference, fractal, meta-cognition. The only primitive that can generate the others. Creates meta-levels. Makes the system self-aware. |
0xd34bd93a65e1 | base_name | [] | [] | [] | nmf |
0x0ad428c0a634 | framework_layer | ["0xd34bd93a65e1"] | [] | ["0x501d59a1e46c", "0x91515f441344", "0x15fbb59966b0"] | NMF — Name = f(Meaning, Frame). Every entity has 3 dimensions: what it is called here (Name), what it contains (Meaning), where it lives (Frame). Same Meaning + different Frame = different Name. |
0xff86a0929abe | base_name | [] | [] | [] | ku |
0x700ff6e270fd | framework_layer | ["0xff86a0929abe"] | [] | ["0x501d59a1e46c", "0x91515f441344", "0x852eecb66da1"] | K/U Split — Invariant vs Variant. Everything separates into K (what stays) and U (what changes). Fix at K level, never patch U. |
0x8b81370b9f54 | base_name | [] | [] | [] | collapse |
0xfbaec867b0e9 | framework_layer | ["0x8b81370b9f54"] | [] | ["0x501d59a1e46c", "0x91515f441344", "0x852eecb66da1", "0xfbe37abdbc60"] | Collapse — Decision emerges when context is sufficient. Not chosen — emerges. Insufficient context = no decision. Contradictory context = failed collapse. |
0x34a31d7ffc86 | base_name | [] | [] | [] | ipod |
0x16f9f618ef6e | framework_layer | ["0x34a31d7ffc86"] | [] | ["0x501d59a1e46c", "0xfbe37abdbc60", "0x15fbb59966b0", "0xed0b40836a29"] | IPOD — Input/Process/Output/Data. Universal decomposition. Everything has I(receive), P(transform), O(emit), D(remember). Not 4 steps — 4 simultaneous aspects. |
0x55626b5ba5b3 | base_name | [] | [] | [] | gap |
0x90563024aeb2 | framework_layer | ["0x55626b5ba5b3"] | [] | ["0x501d59a1e46c", "0x91515f441344", "0x852eecb66da1"] | GAP — Difference when comparing two things. Gap between imagination and observation = learning signal. Gap is not error — gap is signal. |
0x30e9d2d30a5a | base_name | [] | [] | [] | superpose |
0x0f437799202f | framework_layer | ["0x30e9d2d30a5a"] | [] | ["0x501d59a1e46c", "0x91515f441344", "0x852eecb66da1", "0xed0b40836a29"] | Superpose — E = Superpose(K, U). Existence = grounded known + structured unknown. U is NOT absence — U is unresolved presence. |
0xd9449fc10242 | base_name | [] | [] | [] | 1. Tại sao LLM fail ARC-AGI-3 |
0x1fc3983f0843 | discovery | ["0xd9449fc10242"] | [] | ["0x501d59a1e46c", "0x15fbb59966b0", "0x16f9f618ef6e"] | 1. Tại sao LLM fail ARC-AGI-3
LLM là hệ **open-loop**: input → output, không có vòng phản hồi.
ARC-AGI-3 đòi hỏi hệ **closed-loop**: state → action → feedback → update belief → action tiếp theo.
Không phải LLM "không đủ thông minh". LLM đang bị đặt sai loại bài toán. LLM giỏi mapping function. ARC-AGI-3 cần system in... |
0x59231e244bea | base_name | [] | [] | [] | 2. NMF — Name, Meaning, Frame |
0x4b30fddd4206 | discovery | ["0x59231e244bea"] | [] | ["0x501d59a1e46c", "0x15fbb59966b0", "0x0ad428c0a634"] | 2. NMF — Name, Meaning, Frame
Mọi thông tin đều được tổ chức dưới dạng node liên kết. Để navigate bể node khổng lồ này, cần cổng lọc.
NMF là cổng lọc hoạt động qua 3 chiều đồng thời:
- **Name** — tên gọi trong ngữ cảnh hiện tại
- **Meaning** — nội dung khái niệm không đổi qua các ngữ cảnh
- **Frame** — hệ quy chiếu đ... |
0xd0c02ee24a9c | base_name | [] | [] | [] | 3. NMF khác các filter khác ở chỗ nào |
0xa5cde7c41b44 | discovery | ["0xd0c02ee24a9c"] | [] | ["0x501d59a1e46c", "0x15fbb59966b0", "0x0ad428c0a634"] | 3. NMF khác các filter khác ở chỗ nào
Các filter trong lịch sử tư tưởng đều **cố định** ít nhất một trong ba:
| Filter | Cố định |
|---|---|
| Tôn giáo/Giáo điều | Name cố định |
| Khoa học cổ điển | Meaning cố định trong frame mặc định |
| Các hệ triết học | Frame cố định |
| Attention (Transformer) | Frame implicit ... |
0xcfc8e50743b7 | base_name | [] | [] | [] | 4. NMF tự áp lên chính nó |
0x94030577ed22 | discovery | ["0xcfc8e50743b7"] | [] | ["0x501d59a1e46c", "0x15fbb59966b0", "0x0ad428c0a634"] | 4. NMF tự áp lên chính nó
`NMF(NMF)` không phải vòng lặp vô hạn. Nó là **2-level awareness**:
- Level 1: nhìn data
- Level 2: nhìn cách mình nhìn data
Khi áp NMF lên chính NMF:
- Biết frame hiện tại là gì
- Biết frame đó có bias gì
- Có thể hỏi "frame này có đúng không?" từ một frame khác
Recursion có điểm dừng tự n... |
0x5e06667954f8 | base_name | [] | [] | [] | 5. Mọi hệ triết học là một cổng lọc từ bể node chung |
0xa6d65b8bf064 | discovery | ["0x5e06667954f8"] | [] | ["0x501d59a1e46c", "0x15fbb59966b0"] | 5. Mọi hệ triết học là một cổng lọc từ bể node chung
Toàn bộ lịch sử tư tưởng là các cách khác nhau để chọn axis nào, đặt lên layer nào, và fix K anchor ở đâu trong bể node vô hạn của thực tại:
- Buddhism → attend: suffering layer
- Platonism → attend: form/essence layer
- Heraclitus → attend: process/flux layer
- Nie... |
0x889c975e4753 | base_name | [] | [] | [] | 6. Mọi filter đều có cấu trúc binary ở lõi |
0x7eeaae2eeb1f | discovery | ["0x889c975e4753"] | [] | ["0x501d59a1e46c", "0x852eecb66da1", "0x15fbb59966b0", "0xed0b40836a29"] | 6. Mọi filter đều có cấu trúc binary ở lõi
Tất cả 31 cổng lọc khảo sát đều là cách **đặt binary axis (A ↔ B)** lên một layer của thực tại với một K anchor khác nhau.
Tuy nhiên, binary **không đủ** để cover toàn bộ thực tại. Có 4 thứ không reduce về binary:
- **Gradient** — độ lớn, intensity, mức độ (mất middle nếu bi... |
0x0f6c22a612f4 | base_name | [] | [] | [] | 7. Minimal grounding set — 5 primitives |
0x03fa21305ec5 | discovery | ["0x0f6c22a612f4"] | [] | ["0x501d59a1e46c", "0x91515f441344", "0x852eecb66da1", "0x15fbb59966b0", "0xed0b40836a29"] | 7. Minimal grounding set — 5 primitives
Không thể reduce về nhau:
| Primitive | Cơ chế | Covers |
|---|---|---|
| **G1 Binary** | A ↔ B | phân biệt, existence/non, K/U split |
| **G2 Gradient** | scalar [0,1] | đo lường, weight, confidence |
| **G3 Sequence** | a → b → c | thời gian, nhân quả, chu kỳ |
| **G4 Relation... |
0x81ba944fb12f | base_name | [] | [] | [] | 8. E = Superpose(K, U) — tổng quát hóa constant/variable |
0x756558cbb727 | discovery | ["0x81ba944fb12f"] | [] | ["0x501d59a1e46c", "0x15fbb59966b0", "0x700ff6e270fd", "0x0f437799202f"] | 8. E = Superpose(K, U) — tổng quát hóa constant/variable
Dạng cổ điển: `A = constant + variable`
Dạng tổng quát: `E = Superpose(K, U)` trong đó:
- **K** = grounded known anchor (không phải chỉ "constant" — là cái đã được stabilize)
- **U** = structured unknown — **không phải noise**, mà là unresolved structured prese... |
0x96027872374c | base_name | [] | [] | [] | 9. K/U split là frame-dependent |
0xbc670c3a477f | discovery | ["0x96027872374c"] | [] | ["0x501d59a1e46c", "0x91515f441344"] | 9. K/U split là frame-dependent
Không có Frame, không thể biết cái gì là K và cái gì là U.
Cùng một game state, khác Frame → K và U khác nhau hoàn toàn:
- **Navigation frame**: K = obstacle positions, goal pos / U = player movement
- **Puzzle frame**: K = rotator rule, door condition / U = key_state, player pos, heal... |
0xf5847ea629d4 | base_name | [] | [] | [] | 10. 2D world layer — không phải grid |
0xfa2200df1af3 | discovery | ["0xf5847ea629d4"] | [] | ["0x501d59a1e46c", "0x15fbb59966b0", "0x0ad428c0a634"] | 10. 2D world layer — không phải grid
Grid là **encoding** (cách hiển thị). 2D plane là **ontology** (không gian tồn tại).
Khi xử lý bài toán 2D tương tác:
- Grid-based: `grid[x][y] = value` → không có object, không có meaning
- World-based: object chiếm không gian, có tọa độ là anchor (K), có movement là biến (U)
Tọ... |
0x069a4d41959d | base_name | [] | [] | [] | 11. Sandbox là bắt buộc cho closed-loop cognition |
0x5bd1ace3e49d | discovery | ["0x069a4d41959d"] | [] | ["0x501d59a1e46c", "0x15fbb59966b0"] | 11. Sandbox là bắt buộc cho closed-loop cognition
Agent không thể phụ thuộc hoàn toàn vào environment thật để học. Cần **bản sao nội bộ** để thử trước.
Human làm điều này liên tục trong đầu — mental simulation trước khi hành động.
Không có sandbox: agent chỉ có thể observe → guess → output.
Có sandbox: observe → bui... |
0x9aa6ad792586 | base_name | [] | [] | [] | 12. Hành động epistemic vs hành động exploit |
0x5267e37d4226 | discovery | ["0x9aa6ad792586"] | [] | ["0x501d59a1e46c", "0x852eecb66da1"] | 12. Hành động epistemic vs hành động exploit
Khi chưa biết rule, hành động tốt nhất không phải hành động maximize reward — mà là hành động **maximize information gain**.
Câu hỏi đúng không phải "action này có đúng không?" mà là "action này nói gì về rule của hệ?"
Hai mode:
- **Epistemic**: hành động để phân biệt các ... |
0x4e45e462ae41 | base_name | [] | [] | [] | 13. Frame Adjudicator — 3 nhánh |
0x8c1e3a3c942f | discovery | ["0x4e45e462ae41"] | [] | ["0x501d59a1e46c", "0x15fbb59966b0"] | 13. Frame Adjudicator — 3 nhánh
Trước khi solve, agent cần đánh giá frame/goal nhận được:
- **ACCEPT**: frame consistent với observation → solve theo frame đó
- **REJECT**: frame mâu thuẫn với observation → reframe từ world model nội bộ
- **PARALLEL**: chưa đủ evidence → giữ nhiều frame song song, chọn action maximize... |
0x57850278d793 | base_name | [] | [] | [] | 14. Meaning ổn định qua Name thay đổi — cơ chế generalization |
0xbaeed44be9e7 | discovery | ["0x57850278d793"] | [] | ["0x501d59a1e46c", "0x15fbb59966b0", "0x0ad428c0a634", "0x16f9f618ef6e"] | 14. Meaning ổn định qua Name thay đổi — cơ chế generalization
Trong các game khác nhau:
- ls20: `rotator` (value 3) = transformer
- ft09: `switch` (value 7) = transformer
- vc33: `lever` (value 2) = transformer
Các filter dựa vào Name (attention, feature extraction) thấy 3, 7, 2 — khác nhau hoàn toàn, không transfer đ... |
0xaf8dde8571f7 | base_name | [] | [] | [] | 15. Grounding precedes relation |
0x7ba1654df5b0 | discovery | ["0xaf8dde8571f7"] | [] | ["0x501d59a1e46c", "0x15fbb59966b0"] | 15. Grounding precedes relation
Node phải được grounded trước khi có thể drift hoặc tương tác một cách có nghĩa.
Không có grounding → node tồn tại như symbol không có ontological presence → semantic drift → reasoning errors.
Grounding cho ontological legitimacy. Interaction cho field reality. Cả hai cần thiết, nhưng ... |
0xd2d1ac475ce1 | base_name | [] | [] | [] | 16. State là derived, không phải primitive |
0x97957550962f | discovery | ["0xd2d1ac475ce1"] | [] | ["0x501d59a1e46c", "0x15fbb59966b0"] | 16. State là derived, không phải primitive
State không giống grounding. Grounding là anchor. State là biểu hiện hiện tại.
```
S(t) = F(G, L(t), T, W)
```
State là kết quả của grounding + interaction + time + weight — không phải identity của node.
Nhầm state với identity là nguồn gốc của nhiều lỗi reasoning.
--- |
0x795a6233b9ff | base_name | [] | [] | [] | 17. 5 grounding primitives không reduce về nhau |
0x7cab352e979a | discovery | ["0x795a6233b9ff"] | [] | ["0x501d59a1e46c", "0xed0b40836a29"] | 17. 5 grounding primitives không reduce về nhau
Bất kỳ hệ nào muốn represent thực tại đầy đủ cần ít nhất:
1. **Binary** — để phân biệt
2. **Gradient** — để đo lường
3. **Sequence** — để sắp xếp theo thời gian/nhân quả
4. **Relation** — để kết nối
5. **Recursion** — để tự nhìn lại
Binary là primitive phổ biến nhất tro... |
0x7c34332c7828 | base_name | [] | [] | [] | 18. Attention không phải cổng lọc — là dynamic spotlight |
0x1415dda37cae | discovery | ["0x7c34332c7828"] | [] | ["0x501d59a1e46c", "0x15fbb59966b0", "0x0ad428c0a634", "0x16f9f618ef6e"] | 18. Attention không phải cổng lọc — là dynamic spotlight
Các cổng lọc thông thường là **spotlight cố định**:
- Buddhist nhìn → suffering layer luôn sáng
- Physics nhìn → mechanism layer luôn sáng
Attention là **spotlight động** — di chuyển theo query. Không có gì bị loại vĩnh viễn, chỉ mức độ activate thay đổi.
Cơ ch... |
0xea8ce6ff5273 | base_name | [] | [] | [] | 19. NMF vs Attention — điểm giao và điểm khác |
0x72897aede7f3 | discovery | ["0xea8ce6ff5273"] | [] | ["0x501d59a1e46c", "0x91515f441344", "0x852eecb66da1", "0xed0b40836a29", "0x0ad428c0a634"] | 19. NMF vs Attention — điểm giao và điểm khác
| | Attention | NMF |
|---|---|---|
| Dynamic | ✓ query-dependent | ✓ frame-switchable |
| Frame | implicit trong embedding | explicit, có thể thay đổi |
| Tại sao relevant | không biết | biết (Meaning grounded) |
| K/U split | không có | có |
| Self-apply | không thể | có... |
0xeab38c030f76 | base_name | [] | [] | [] | 1. Purpose |
0x3b15eb763f3f | design_spec | ["0xeab38c030f76"] | [] | ["0x501d59a1e46c", "0x91515f441344", "0x15fbb59966b0"] | 1. Purpose
This document defines a full design for a grounded fieldmap architecture.
The goal is to avoid a floating semantic map with no ontological base. Instead, the system begins from **primordial binary grounding**, then allows later nodes to emerge, connect, polarize, balance, and form semantic structures.
The ... |
0xb36964b0dcbb | base_name | [] | [] | [] | 2. Existence Core |
0x71b9ea7093f1 | design_spec | ["0xb36964b0dcbb"] | [] | ["0x501d59a1e46c", "0x15fbb59966b0"] | 2. Existence Core
Existence is not treated as an ordinary node. It is treated as the **substrate** or **base field** in which all other structures appear.
We denote this substrate by:
\[
\mathcal{E}
\]
This means:
- primordial axes exist *within* the existence core,
- grounded nodes emerge *within* the existence co... |
0x032e87a8acd2 | base_name | [] | [] | [] | 3. Primordial Binary Grounding |
0x4c918053d94c | design_spec | ["0x032e87a8acd2"] | [] | ["0x501d59a1e46c", "0x91515f441344", "0x15fbb59966b0", "0x700ff6e270fd"] | 3. Primordial Binary Grounding
### 3.1 Primordial Binary Axis
A primordial binary axis is a foundational polarity pair:
\[
\mathcal{P}_k = (A_k \leftrightarrow B_k)
\]
Examples include:
- Matter \(\leftrightarrow\) Energy
- Space \(\leftrightarrow\) Time
- Constant \(\leftrightarrow\) Variable
Each primordial pair... |
0x0147736c53e0 | base_name | [] | [] | [] | 4. General Form of Existence |
0x90cc503b004a | design_spec | ["0x0147736c53e0"] | [] | ["0x501d59a1e46c", "0x15fbb59966b0", "0x700ff6e270fd", "0x0f437799202f"] | 4. General Form of Existence
We define existence in its most general form as:
\[
E = \operatorname{Superpose}(K, U)
\]
Where:
- \(K\) — **Grounded Known (Anchor)**
The stabilized, anchored component of the system.
- \(U\) — **Structured Unknown**
The unresolved, variable, and potentially recursive component... |
0xaf9f70ed5d0d | base_name | [] | [] | [] | 5. Relation to Constant–Variable Polarity |
0x528a290f5790 | design_spec | ["0xaf9f70ed5d0d"] | [] | ["0x501d59a1e46c", "0x91515f441344", "0x15fbb59966b0", "0x700ff6e270fd", "0xfbaec867b0e9"] | 5. Relation to Constant–Variable Polarity
Given a primordial binary axis:
\[
\text{Constant} \leftrightarrow \text{Variable}
\]
we reinterpret:
- **Constant** \(\to\) grounded known core \(K\)
- **Variable** \(\to\) structured unknown \(U\)
Thus:
\[
A = K + U
\]
is a more scalable version of:
\[
A = \text{consta... |
0x426f83bd533c | base_name | [] | [] | [] | 6. Node Existence: Grounding and Manifestation |
0xa58c63fbaacb | design_spec | ["0x426f83bd533c"] | [] | ["0x501d59a1e46c", "0x15fbb59966b0"] | 6. Node Existence: Grounding and Manifestation
A node does not exist in only one sense. It has at least two distinct layers of existence.
### 6.1 Ontological Existence
A node exists if it is grounded:
\[
\text{Existence}(N_i) \Leftrightarrow \|G_i\| > 0
\]
This defines the node’s ontological presence.
### 6.2 Mani... |
0xb80dba4e57ae | base_name | [] | [] | [] | 7. Minimal Grounded Node Form |
0x973fe9edce6b | design_spec | ["0xb80dba4e57ae"] | [] | ["0x501d59a1e46c", "0x852eecb66da1", "0x15fbb59966b0"] | 7. Minimal Grounded Node Form
A minimal grounded node is:
\[
N_i = [G_i, T_i, S_i, W_i, X_i]
\]
Where:
- \(G_i\) — **Grounding**
Primordial axes and polarity coordinates.
- \(T_i\) — **Temporal Trace**
The time signature of the node.
- \(S_i\) — **Manifested State**
The node’s current expression in the... |
0xba7c3ccfa1ef | base_name | [] | [] | [] | 8. State as Manifestation |
0x95e4269408e8 | design_spec | ["0xba7c3ccfa1ef"] | [] | ["0x501d59a1e46c", "0x15fbb59966b0"] | 8. State as Manifestation
State is not the same as grounding. Grounding is the anchor. State is the current expression.
We define:
\[
S_i(t) = \mathcal{F}(G_i, \mathcal{L}_i(t), T_i, W_i)
\]
Thus:
- grounding defines what the node is anchored to,
- links define how the node is pulled or reinforced,
- time defines w... |
0x08004bcd9b51 | base_name | [] | [] | [] | 9. Polarization and Field Dynamics |
0x8a209b27dba2 | design_spec | ["0x08004bcd9b51"] | [] | ["0x501d59a1e46c", "0x15fbb59966b0"] | 9. Polarization and Field Dynamics
A grounded node may shift under field interaction.
Its grounding vector may evolve as:
\[
\mathbf{g}_i(t+1) = \mathbf{g}_i(t) + \Delta \mathbf{g}_i(\mathcal{L}_i, W_i, T_i)
\]
Its state updates accordingly:
\[
S_i(t+1) = \mathcal{F}(G_i(t+1), \mathcal{L}_i(t), T_i, W_i(t))
\]
Its... |
0xd6d8cc93686a | base_name | [] | [] | [] | 10. Semantic Plane Formation |
0xecbcbaf6fcb5 | design_spec | ["0xd6d8cc93686a"] | [] | ["0x501d59a1e46c", "0x15fbb59966b0", "0x0ad428c0a634"] | 10. Semantic Plane Formation
Primordial pairs do not create semantic planes. They remain axis-based.
Semantic planes are formed only by **ordinary nodes**.
### 10.1 Rule of Semantic Plane
Three non-collinear ordinary nodes create one semantic plane.
Given three nodes:
\[
N_i, N_j, N_k
\]
if they are not collinear... |
0xc0e1f75e57f8 | base_name | [] | [] | [] | 11. Relational Structure |
0x0c2a0b471651 | design_spec | ["0xc0e1f75e57f8"] | [] | ["0x501d59a1e46c", "0x15fbb59966b0"] | 11. Relational Structure
A node may also carry explicit relational links:
\[
\mathcal{L}_i = \{N_j \mid N_j\ \text{linked to}\ N_i\}
\]
These links are not identical to grounding.
- **Grounding** anchors the node to primordial ontology.
- **Links** connect the node to field interaction and semantic formation.
This ... |
0xd877ef0b61c0 | base_name | [] | [] | [] | 12. Grounded State Space |
0xe099c81c51e6 | design_spec | ["0xd877ef0b61c0"] | [] | ["0x501d59a1e46c", "0x15fbb59966b0"] | 12. Grounded State Space
We define the grounded state space as:
\[
\mathcal{S} = \{s_i \mid s_i = (G_i, T_i, S_i, W_i, X_i)\}
\]
This is stronger than a flat state space of arbitrary symbolic or semantic configurations. A state is now a grounded field entity.
--- |
0xa15a0eceb98f | base_name | [] | [] | [] | 13. BCL Integration |
0x61bfcd77dee7 | design_spec | ["0xa15a0eceb98f"] | [] | ["0x501d59a1e46c", "0x15fbb59966b0"] | 13. BCL Integration
### 13.1 Why BCL Needs Grounding
BCL is powerful for branch-rich data because it supports:
- shared-state branching
- multi-view encoding
- fractal refinement
- entropy-governed stopping
But without grounding, its shared core remains underdefined. A core may be detected statistically or structura... |
0x0886d646c4d0 | base_name | [] | [] | [] | 14. Collapse in the Unified System |
0x8608d36ec5e5 | design_spec | ["0x0886d646c4d0"] | [] | ["0x501d59a1e46c", "0x852eecb66da1", "0x15fbb59966b0", "0xfbaec867b0e9"] | 14. Collapse in the Unified System
A path or structure collapses when grounded nodes align sufficiently under field, weight, resonance, and multi-view consistency.
Let \(\pi\) be a path of grounded nodes. Then a path score may be written as:
\[
J(\pi) = \sum_{u_i \in \pi} \Bigl(
\kappa_1 \mathrm{Res}_i
+ \kappa_2 W_i... |
0x6281c24c762f | base_name | [] | [] | [] | 15. Full Design Stack |
0x21dc7b4e736a | design_spec | ["0x6281c24c762f"] | [] | ["0x501d59a1e46c", "0x91515f441344", "0x15fbb59966b0"] | 15. Full Design Stack
The architecture can be summarized as a five-layer stack.
### Layer 0 — Existence Core
\[
\mathcal{E}
\]
The substrate of the field.
### Layer 1 — Primordial Axes
\[
\mathcal{P}_k = (A_k \leftrightarrow B_k)
\]
Binary grounding axes.
### Layer 2 — Grounded Nodes
\[
N_i = [G_i, T_i, S_i, W_... |
0x26dd07c3a004 | base_name | [] | [] | [] | 16. Core Principles |
0x2ef72cca467f | design_spec | ["0x26dd07c3a004"] | [] | ["0x501d59a1e46c", "0x15fbb59966b0", "0x0ad428c0a634"] | 16. Core Principles
The full system follows these principles:
1. **Grounding precedes relation**
A node must anchor before it can meaningfully drift.
2. **Primordial pairs define axes, not planes**
Their geometry is linear and polar.
3. **Three ordinary nodes define a semantic plane**
Semantic context... |
0x40a08b4dbbe6 | base_name | [] | [] | [] | 17. Final Summary |
0xb784b71260c7 | design_spec | ["0x40a08b4dbbe6"] | [] | ["0x501d59a1e46c", "0x91515f441344", "0x15fbb59966b0", "0x0ad428c0a634", "0x0f437799202f"] | 17. Final Summary
The architecture proposed here turns fieldmap into a grounded representational regime.
- Existence is a substrate, not an ordinary node.
- Primordial pairs are axis-based polarity anchors.
- Every ordinary node must anchor to at least one primordial axis.
- Each node has a minimal grounded form:
\[
... |
0x52f5da32a6b2 | base_name | [] | [] | [] | 18. Closing Insight |
0xff2fe45892f2 | design_spec | ["0x52f5da32a6b2"] | [] | ["0x501d59a1e46c", "0x15fbb59966b0"] | 18. Closing Insight
> A node is not merely a point in a graph.
> It is a grounded entity inside an existence field,
> positioned between primordial poles,
> manifested through interaction,
> and interpreted through semantic planes.
That is the shift from an ungrounded semantic map to a grounded field architect... |
0xe491a56502f0 | base_name | [] | [] | [] | 1. Collapse |
0x37eeb9590ec4 | principle | ["0xe491a56502f0"] | [] | ["0x501d59a1e46c", "0x15fbb59966b0", "0x0ad428c0a634", "0xfbaec867b0e9"] | 1. Collapse
A decision is not "chosen" — it *emerges* when context is sufficiently coherent.
Insufficient context → no meaningful decision.
Contradictory context → collapse fails.
Converged context → collapse happens naturally.
**Never act with insufficient context. If you don't know enough, the action is to KNOW MOR... |
End of preview. Expand in Data Studio
Three-Reality Field Dataset
33,227 grounded knowledge nodes organized into a spatial model of three reality layers.
Author: Kevin T.N.
Structure
Every node follows the NMF (Name-Meaning-Frame) schema:
- id — unique hash
- type — node category (existence, filter_gate, framework_layer, principle, discovery, topic, paper, text, code, ...)
- name_nodes — JSON array of Name node IDs (what it is called)
- mean_nodes — JSON array of Meaning node IDs (what it contains/connects to)
- frame_nodes — JSON array of Frame node IDs (where it lives/what context)
- raw_content — human-readable content
Three Realities
The dataset models knowledge in three layers:
- R1 Subjective — personal memory, unconscious patterns, layered center-to-surface
- R2 Intersubjective — shared consensus, social knowledge (structural, not yet populated)
- R3 Absolute Objective — what exists regardless of observers (the void outside)
Node Types
| Type | Count | Description |
|---|---|---|
| doc_chunk | 21,560 | Content chunks from documents |
| base_chunk_name | 9,002 | Chunk identifiers |
| base_name | 1,098 | Entity names |
| keyword | 498 | Extracted keywords |
| paper | 391 | PDF/TeX papers |
| text | 193 | Text files |
| topic | 191 | Knowledge clusters |
| document | 84 | DOCX/MD documents |
| code | 80 | Python/JS code |
| principle | 35 | General rules |
| discovery | 20 | Insights |
| design_spec | 18 | Architecture specs |
| framework_layer | 6 | NMF, K/U, Collapse, IPOD, GAP, Superpose |
| filter_gate | 5 | Binary, Gradient, Sequence, Relation, Recursion |
| pyramid_vertex | 4 | Meta-methods: LMF, SDC(D/H)V, RT/OT, R1/R2/R3 |
| existence | 1 | Root substrate (also = Gap) |
Grounding
Every node is grounded to at least one of 5 primordial filter gates via its frame_nodes. Nodes closer to the center (existence) are more general. Nodes on the surface are more specific.
Usage
Related
- ThreeRealityField — 3D visualization + build tools
- ANC — Anchor-Node Codec encoding pipeline
- QuadFusion — S/M/Q/R architecture experiment
License
AGPL-3.0
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