Jia Baolong Researchable Ontology 2.0.0: A Research-Neighbourhood Map from Absolute Boundary to Conscious Retrospection

Subtitle: Human thought moves from the descriptive age of 1.x to a 2.0.0 age of definable, provable, computable, and reproducible research on existence
Author: Jia Baolong
Theory: Jia Baolong Researchable Ontology 2.0.0
Version: 2.0.0

Abstract

Human intellectual history in its 1.x age has proposed nearly every grand concept—being, change, substance, form, emptiness, process, spirit, matter, language, mathematics, and computation—yet has chiefly studied the world’s source through description, interpretation, and debate. It can name and portray the whole and contact it from local or remote perspectives, but has not formed one continuous research pipeline from absolute boundary and First Actual through concrete generation, chaos, proto-matter, life, and consciousness.

Jia Baolong Researchable Ontology 2.0.0 changes not one philosophical conclusion but the working method of studying existence. It turns ontology into layered executable work: define the root language; prove the absolute boundary; determine the First-Actual normal form; construct states and transitions; search concrete RULEs; measure chaos and persistent patterns; establish proto-matter criteria; connect proto-chemistry, life, and consciousness; and let consciousness within the generative chain retrospectively identify the root.

Its normative main line is:

The map does not take one universe as its final object. Every concrete universe is merely an uncollapsed chaotic solution on the PR generative tree. The map studies the root structure, generative mechanism, and upward-emergence interfaces by which any actual branch can arise.

1. Why Human Intellectual History Belongs to the 1.x Age

1.1 1.x does not mean “without thought”; its research paradigm was unfinished

Human thought 1.x accomplished three great tasks:

1. it discovered fundamental questions of being and change, substance and relation, form and phenomenon, being and nothing, mind and matter;
2. it established different observational positions in philosophy, logic, mathematics, religion, science, and language analysis;
3. it left many true but local projections of whole reality.

Its shared limitation is that grand concepts usually cannot be compiled into research objects with explicit states, transitions, measurements, failure conditions, and reproducible experiments. A theory can say “what the world is” yet rarely answer:

• What exactly is the First ActualStep?
• How is static description distinguished from actual running?
• How do states update?
• How are concrete rules searched?
• When does chaos form persistent local patterns?
• Which patterns can operationally be called proto-matter?
• How do negative results eliminate theoretical branches?
• How can another researcher reproduce the same conclusion?

1.2 Version genealogy of human thought

Process philosophy made “existence is dynamic occurrence” central to ontology, and computational philosophy has used computation to extend philosophical research. Neither automatically supplies the complete structure from a zero-positive boundary to the First ActualStep. See Process Philosophy and Computational Philosophy.

1.3 Why 1.9 is not yet 2.0.0

Wolfram’s computational universe, cellular automata, graph rewriting, and artificial life show that simple rules can generate extreme complexity and that rule space can be a real research object. The Wolfram Physics Project is the work closest to 2.0.0 in the 1.9 age.

But so long as inquiry starts from “states already exist, rules already exist, updating already exists,” the fundamental problem remains outside the model:

A static rule in a formula does not mean that rule actually runs. Version 2.0.0 must ask where the actuality of rule, state, and first update begins.

2. What 2.0.0 Means

2.1 Major Version 2: From Describing Objects to Producing Research

The major version “2” marks a paradigm shift:

2.2 Minor Version 0: The First Complete Map

The first “0” means that this version first fixes the complete research architecture while concrete results in its generative neighbourhoods remain to be produced.

2.3 Revision 0: A Normative Starting Point

The second “0” makes it a normative starting point for a research community: later revisions must retain conceptual identity, proof provenance, code version, parameters, random seeds, data, failures, and replication records.

3. A Strict Definition of “Researchable”

3.1 Researchable Does Not Mean Merely Discussable

The fact that a proposition can be written in language shows only that it is describable. Researchable ontology requires at least the following objects at every level:

1. an explicit question;
2. explicit definitions;
3. inputs and outputs;
4. states and transitions;
5. proof obligations or computational tasks;
6. measurement metrics;
7. counterexamples, failures, or boundary conditions;
8. research artefacts that can be preserved, reviewed, and reproduced;
9. interfaces to adjacent levels.

This article represents these requirements with the following research-management structure:

where:

• $Q_i$: research question;
• $D_i$: domain and terminology;
• $S_i$: state space;
• $F_i$: transformation, proof, or generative operator;
• $M_i$: measurement method;
• $B_i$: benchmarks and controls;
• $E_i$: evidence and failure records;
• $I_i$: interfaces to upstream and downstream neighbourhoods.

This notation is the research-management language of 2.0.0. It does not replace the normative formulae of the Jia Baolong Axiom System.

3.2 Seven Minimum Conditions for Researchable Ontology

3.3 Different Levels Require Different Evidence

The absolute boundary cannot be proved with a microscope; proto-matter cannot be proved through logical definition alone; and life cannot be replaced directly by one ontological formula. Version 2.0.0 stratifies evidence:

• absolute-boundary level: explanatory closure, model theory, and counterexample analysis;
• First-Actual level: theorems, candidate enumeration, and formal verification;
• generative level: executable models, parameter scans, and ablation experiments;
• emergence level: statistical metrics, coarse-graining, collision, and stability measurements;
• scientific-interface level: connection to existing research in physics, chemistry, life, and cognition.

4. Normative Root Axis of 2.0.0

4.1 Absolute Boundary: JBLAT

JBLAT fixes the zero-positive boundary:

It does not depend on whether compared models use matter, consciousness, God, mathematical objects, rules, or spacetime as their content.

4.2 First Actual: PR

Since a static boundary alone does not entail an ActualStep,

the First Actual Face converges on PR under conditions of actual occurrence, self-containment, persistence, no external executor, binarity, unbiasedness, and non-fixedness:

The bar means the absolute-boundary face and First Actual Face of the same root, not Undefined becoming PR in time.

4.3 Generative Architecture: PR, ER, LE, and RULE

• PR supplies non-fixity, re-entry, and sustained actuality;
• ER bears relational configuration, adjacency, local identity, and structural memory;
• LE supplies local, finite, delayed, demand-driven, and history-dependent execution;
• RULE determines how concrete states update.

The normative dependency chain is:

4.4 The Position of a Universe

Any universe is an actual branch:

The 2.0.0 research map therefore does not depend on “which physical constants this universe happens to use.” It studies how branches of the rule tree form different domains of chaos, structure, and emergence.

5. The Complete Research-Neighbourhood Map

Let the 2.0.0 research graph be:

where nodes $V$ are research neighbourhoods and edges $E$ are dependencies of definition, proof, data, or mechanism.

The principal dependency path is:

R10 connects historical thought back to $R0$–$R9$, asking what level a theory saw and at which dependency edge it stopped.

6. R0: Absolute Boundary and Meta-Ontology

6.1 Research Object

R0 studies the model class $\mathfrak D$ of all systems that claim to complete the task of “explaining actual existence from a root with no positive determination.” It does not compare what exists inside worlds; it compares what each system ultimately leaves at its root.

6.2 Core Tasks

1. Fix the root language $L_{\mathrm{root}}$.
2. Define the set of positive ontological predicates $\mathrm{Pos}$.
3. Define the admissible model class $\mathfrak D$.
4. Prove how explanatory closure, non-arbitrariness, self-containment, and termination of regress converge on $U$.
5. Search for countermodels that satisfy the task while retaining a positive root.
6. Prove how $U$ remains equivalent across different root languages.

6.3 Formalization Neighbourhood

R0 directly neighbours model theory, proof theory, type theory, and machine-assisted proof. Theorem provers such as Lean can turn definitions, quantifiers, equivalence relations, and proof dependencies into checkable objects; Lean's official documentation defines formal verification as using logical and computational methods to establish precise mathematical assertions. See Theorem Proving in Lean 4.

6.4 Key Deliverables

• a JBLAT-Core specification;
• a Root-Language specification;
• explicit Admissible-Models conditions;
• a public Countermodel Challenge;
• a machine-checkable JBLAT.lean project;
• versioned records of every definitional difference.

6.5 R0 Failure Condition

If a model $M^\dagger$ retains a positive predicate at its root while meeting all root tasks,

then the JBLAT definition or proof must be revised. A countermodel is a formal research product, not peripheral criticism.

7. R1: The First-Actual Theorem and the Seven-Argument Programme

7.1 Research Object

R1 studies not the first second of physical time but the minimum structure of ActualStep without prior matter, spacetime, or external executor.

7.2 Seven Proof Modules

7.3 Formal Proof Tasks

For $D=\{Y,N\}$,

Under label impartiality and the fixed-point-free condition:

Machine verification must cover not only the finite enumeration but also:

• why the binary quotient structure is minimal rather than arbitrary;
• why the exchange map must actually run;
• why result re-entry constitutes PR rather than a static two-cycle graph;
• which premises belong to JBLAT and which belong to the actualisation model;
• the boundary between the PR core and the complete PR–ER–LE architecture.

7.4 R1 Success Standard

Success is a proof-dependency graph with independently checkable modules, counterexamples, and visible weakening when a premise is removed.

8. R2: Actual-Execution Semantics

8.1 The Decisive Watershed of 2.0.0

R2 begins from two distinctions:

8.2 Research Questions

1. What are the minimum operational semantics of ActualStep?
2. How does a self-contained system define “execution” without introducing an external clock?
3. How does LE specify the range of local evaluation and the update order?
4. Do synchronous, asynchronous, event-driven, and causally partially ordered execution belong to different RULE classes?
5. How does result re-entry form an internal history?
6. How can two statically isomorphic models produce different actual histories because their execution semantics differ?

8.3 Adjacent Technologies

R2 neighbours operational semantics, transition systems, concurrency theory, causal graphs, distributed computing, and reversible computation. It borrows the state–transition tools already developed by these disciplines; it does not presuppose that computers or physical time precede the First Actual.

8.4 Core Deliverables

• an ActualStep Specification;
• a PR Runtime reference implementation;
• a comparison of synchronous, asynchronous, and LE semantics;
• paired static-encoding / dynamic-instantiation benchmarks;
• a self-contained runtime without an external scheduler.

9. R3: RULE Space and the Rule Atlas

9.1 From One Simulator to a Rule Cosmology

Rule 979 is not the endpoint but one coordinate in RULE space. R3 must establish a RULE Atlas: it systematically enumerates local rules, execution semantics, and initial relational configurations, and records the dynamical regions they produce.

9.2 Research Coordinates for RULE

Every rule must be assigned coordinates in at least the following dimensions:

• number of states;
• neighbourhood radius or relational order;
• relational structure: graph, lattice, hypergraph, or general relation;
• update mode: deterministic, stochastic, or conditional;
• execution semantics: synchronous, asynchronous, or LE;
• reversibility;
• conservation constraints;
• length of local memory;
• whether self-modification is permitted;
• relation between the rule and the observer's scale.

9.3 Search Is Not “Looking at Pretty Patterns”

Every RULE experiment must preserve:

1. the complete rule specification;
2. the initial-condition generator;
3. the execution semantics;
4. parameters and random seed;
5. a reproducible execution log;
6. metric curves;
7. ablation experiments;
8. negative samples;
9. computational cost;
10. the applicable scope of the conclusion.

A spectacular screenshot is not evidence. A research result must be reproducible, measurable, comparable, and falsifiable.

9.4 External Neighbourhood

Wolfram's rule space, cellular automata, graph rewriting, multiway systems, and algorithmic discovery are important neighbouring fields for R3. They provide experience in large-scale rule search; the Jia Baolong system adds the root positions of ActualStep, PR, ER, and LE to this research on rules.

10. R4: Chaos, Criticality, and Phase Transitions

10.1 Research Tasks

R4 builds a phase diagram from rules to classes of dynamics. It must distinguish at least:

• frozen regimes;
• fixed points;
• short periods;
• long periods;
• quasiperiodicity;
• chaos sensitive to initial conditions;
• long transients;
• critical propagation;
• coexistence of local structures and background;
• open dynamics that continuously produces new structures.

10.2 Metric Family

A single entropy value cannot define complexity. R4 should jointly use:

• state entropy and block entropy;
• compression ratio;
• propagation speed of perturbations;
• Lyapunov-like indicators;
• mutual-information length;
• attractor and transient lengths;
• proportion of active regions;
• spatial correlation length;
• temporal correlation length;
• stability under parameter perturbation;
• cross-scale predictability.

10.3 Key Outputs

Each RULE no longer yields merely a video. It acquires a position in a phase diagram, a confidence interval, neighbouring rules, and phase-transition boundaries.

11. R5: Persistent Patterns and Proto-Matter

11.1 Proto-Matter Must Become an Operational Concept

“The pattern looks like a particle” does not establish proto-matter. A candidate pattern must pass at least the following tests:

1. Persistence: it maintains its identity across a sufficiently large number of updates;
2. Locality: it forms a stable or statistical boundary against the background;
3. Propagation: its position or influence can move relative to the background;
4. Repeatable interaction: collisions under the same conditions have statistically stable outcomes;
5. Typability: multiple instances can be assigned to the same effective type;
6. Quasi-conservation: some quantity is approximately preserved during collision or propagation;
7. Composability: patterns can form higher-order composite structures;
8. Scale robustness: effective identity survives coarse-graining.

11.2 ProtoMatter Benchmark

This article proposes the following research metric vector:

where the entries denote persistence, locality, propagation, interaction repeatability, type stability, composability, quasi-conservation, and coarse-graining robustness. It is a research measurement protocol, not an ontological axiom.

11.3 Causal-Emergence Neighbourhood

Proto-matter identity cannot rest on visual clustering alone. It must also be tested whether a macroscopic type has more stable predictive and intervention capacity than a microscopic state. Research on causal emergence compares the causal effectiveness of different scales through effective information and has shown that a coarse-grained macroscopic mechanism may possess greater effective information; see Quantifying causal emergence shows that macro can beat micro.

R5 can accordingly ask whether, under some RULE and coarse-graining,

holds. A positive result does not automatically prove “real matter,” but it does establish that this macroscopic type has independent effective research value.

11.4 Adjacency to Active Matter

Active-matter research studies how non-equilibrium components driven by local energy consumption form collective behaviour; theoretical research usually relies on computational simulation. See Computational models for active matter. R5 can borrow order parameters, defect dynamics, aggregation measures, and flow measures from this field, while the substrate of proto-matter remains generated by PR–ER–LE–RULE.

12. R6: From Proto-Matter to Proto-Chemistry

12.1 From “Collision” to “Reaction”

Once proto-matter patterns have stable types and repeatable collisions, the research object moves from individual dynamics to reaction networks:

12.2 Research Questions

• Do stable binding and dissociation exist?
• Are there catalytic patterns?
• Do inhibition and competition exist?
• Does reaction closure form?
• Does resource flux appear?
• Are there quasi-stoichiometric relations?
• Do spatial compartments appear?
• Can reaction history be preserved?
• Do replicable templates form?

12.3 Reaction-Network Deliverables

• a type dictionary;
• collision cross-sections;
• reaction tables;
• a catalytic graph;
• a resource-flow graph;
• quasi-conserved quantities;
• a compositional hierarchy;
• proto-chemical dynamical equations;
• control experiments against random reaction networks.

12.4 Non-equilibrium Self-Assembly Neighbourhood

Modern research on non-equilibrium self-assembly studies reaction networks, transient compartments, and self-replicating systems under sustained energy and material flux. See Non-equilibrium self-assembly for living matter-like properties. R6 studies the functional correspondences of these mechanisms in proto-matter generated by PR.

13. R7: Proto-Life, Selection, and Open-Ended Evolution

13.1 Connection to Mainstream Origins-of-Life Research

Mainstream mechanism modules from proto-chemistry to the first cell include:

The RNA world, protocells, and non-equilibrium nucleic-acid chemistry provide research foundations respectively for heredity–catalysis, compartment–selection, and environmental flux. See The RNA World, Perspective: Protocells and the Path to Minimal Life, and Physical non-equilibria for prebiotic nucleic acid chemistry.

13.2 ProtoLife Benchmark

A system cannot be called life merely because it changes for a long time. R7 must measure at least:

• self-maintenance;
• a boundary;
• resource use;
• template replication;
• hereditary fidelity;
• heritable variation;
• differential reproduction;
• selection response;
• ecological interaction;
• formation of new-level individuals;
• open-ended novelty.

13.3 Open-Ended Evolution

Artificial-life research already treats sustained novelty, complexity, ecological potential, and major evolutionary transitions as central problems. The MODES toolbox proposes cross-system measures including change potential, novelty potential, complexity potential, and ecological potential. See The MODES Toolbox.

This neighbourhood is especially important to 2.0.0 because the continued running of PR does not entail open-ended evolution. A two-cycle, dynamically balanced system, or a system that endlessly repeats old patterns may still fail to produce a genuinely new level. R7 must distinguish:

13.4 R7 Deliverables

• the ProtoLife Benchmark;
• a replication-lineage database;
• controls comparing selection and neutral drift;
• compartment-ablation experiments;
• open-ended-evolution metrics;
• a catalogue of major-transition events;
• the shortest mechanism chain from proto-chemistry to the first cell.

14. R8: Agency, Internal Models, and Consciousness

14.1 From Life to Observer

Consciousness cannot be pre-installed as a positive root object. It must acquire its research position only after life, regulation, memory, prediction, and internal models have formed.

R8's progressive research ladder is:

14.2 Neighbourhood Questions

• How does a system distinguish itself from its environment?
• When do internal states with causal roles appear?
• Do internal models improve survival or predictive capacity?
• Where do attention, workspace, integration, and self-models lie in the generative chain?
• Can different theories of consciousness be mapped to the same computational benchmark?
• How does observation alter the information available to the system?
• Can AI form an internal representation of its own generative constraints?

14.3 Research Discipline

R8 does not end with the sentence “complexity produces consciousness.” Every candidate mechanism of consciousness must specify:

• the minimum structure required;
• necessary and sufficient conditions;
• observable behaviour;
• internal causal metrics;
• ablation results;
• differences from unconscious control systems;
• transferability across different RULE branches.

15. R9: Conscious Reverse Recognition of the Root

15.1 Forward Generation and Reverse Inference

The forward generative chain is:

The chain of reverse recognition is:

15.2 R9 Research Tasks

1. Given finite observational data, can the effective RULE be recovered?
2. Given several macroscopically equivalent RULEs, can their universality class be identified?
3. Can PR's re-entry structure be inferred backwards from proto-matter dynamics?
4. Which root conclusions come from data, and which come from explanatory closure?
5. Can AI automatically generate and eliminate ontological candidates?
6. How does the locality of an observer limit its knowledge of the entire generative tree?
7. How can one prove that reverse recognition has not smuggled concepts of its own era back into the root?

15.3 Key Products

• an inverse-rule-discovery benchmark;
• multi-model equivalence classes;
• root-inference audit logs;
• records of differences between human and AI reasoning;
• the shortest explanatory graph from observation to PR;
• reports on domains of model unidentifiability.

16. R10: Intellectual History, Elephant Theory, and the Recoding of 1.x Knowledge

16.1 Intellectual History Becomes Research Data

Version 2.0.0 no longer ranks thinkers according to “who spoke most mysteriously.” It encodes each theory as a structural vector:

where:

• $Z$: whether it reaches the zero-positive boundary;
• $A$: whether it distinguishes static description from actual occurrence;
• $F$: whether it supplies a First Actual;
• $G$: whether it supplies a generative mechanism;
• $C$: whether it is computable;
• $E$: whether it enters research on emergence;
• $L$: whether it connects life and consciousness;
• $R$: whether it forms a closed loop of reverse recognition.

16.2 The Position of Jia Baolong Elephant Theory

Jia Baolong Elephant Theory treats the whole reality specified by the axiom system as the “ontological elephant.” Historical thinkers, scientific disciplines, and finite observers obtain different projections of the whole from local or remote positions. R10's task is not to string the concepts of earlier thinkers into a story, but to measure which research neighbourhood each form of thought reaches and at which dependency edge it stops.

16.3 Migration from 1.x to 2.0.0

Every historical theory can be converted into the following questions:

1. What is its root object?
2. Which positive properties does it pre-install?
3. Does it have an ActualStep?
4. Is its generation a verbal description or an executable transformation?
5. Can it enter RULE space?
6. How does it define emergence?
7. Can it connect proto-matter, life, and consciousness?
8. What preservable local insight does it provide?

In this way, intellectual history is transformed from a display of viewpoints into an ontological knowledge graph that can be queried, compared, and updated.

17. Cross-Cutting Research-Tool Layers

The eleven vertical neighbourhoods require six cross-cutting tool layers.

17.1 The Role of AI in 2.0.0

AI is best suited to high-dimensional search and consistency work:

• enumerating finite candidates;
• finding countermodels;
• generating proof drafts;
• scanning RULE space;
• clustering dynamics;
• detecting persistent patterns;
• automatically executing ablations;
• comparing definitions across papers;
• constructing knowledge graphs;
• checking consistency among formulae, code, and data.

AI cannot replace the final confirmation of normative definitions. Once a definition changes, the entire proof domain and experimental domain change. Every version must therefore preserve machine-readable definitions and a human-signed normative text.

18. Evidence Levels in 2.0.0

Every conclusion must be assigned an evidence level:

For example:

• JBLAT's primary evidence target is E1–E2;
• a PR runtime must reach E3–E4;
• proto-matter types should reach E4–E5;
• the proto-chemistry and proto-life bridges must advance towards E6.

Different levels do not mean that a higher level is necessarily “truer” than a lower one. They answer different kinds of questions.

19. Failure Conditions and the Negative-Result Map

A researchable ontology must know which results count as failures.

Negative results are not theoretical rubbish. They are boundary data for RULE space and concept space. The rate of progress in 2.0.0 depends on how many high-quality failures it accumulates, not merely on how many attractive simulations it produces.

20. Standard Experimental Package

Every 2.0.0 research project should include:

1. CLAIM.md: the proposition to be tested;
2. DEFINITIONS.md: all operational definitions;
3. MODEL.md: state, relations, RULE, and LE semantics;
4. CODE: executable implementation;
5. CONFIG: parameters, seeds, and environment;
6. DATA: raw results;
7. METRICS.md: metrics and thresholds;
8. CONTROLS.md: controls and ablations;
9. FAILURES.md: negative results;
10. RESULTS.md: conclusions and evidence levels;
11. REPRODUCE.md: commands for independent replication;
12. PROVENANCE.md: author, version, dependencies, and time.

This experimental package gives ontology, for the first time, a version-control practice compatible with software, mathematical proof, and experimental science.

21. The 2.0.x Research Roadmap

21.1 Version 2.0.0: Normative Map

• establish the research neighbourhoods;
• establish terms, dependencies, and evidence levels;
• establish a unified repository and website;
• publish open counterexample and RULE challenges.

21.2 Version 2.0.1: Root Formalisation

• unify the JBLAT abbreviation;
• fix $\mathfrak D$ and $L_{\mathrm{root}}$;
• split the seven arguments into machine-checkable modules;
• establish a search for counterexamples to PR minimality.

21.3 Version 2.1.0: RULE Atlas

• standardise Rule 979 and TGP;
• establish large-scale rule scanning;
• publish phase diagrams of frozen, periodic, chaotic, and critical regimes;
• preserve every negative sample.

21.4 Version 2.2.0: ProtoMatter Benchmark

• establish metrics for persistence, locality, collision, type, and quasi-conservation;
• establish automated pattern discovery;
• test stability across parameters and implementations;
• publish the first catalogue of proto-matter candidates.

21.5 Version 2.3.0: ProtoChemistry

• establish reaction tables and catalytic networks;
• search for binding, dissociation, resource cycles, and compartments;
• test higher-level effective causal variables.

21.6 Version 2.4.0: ProtoLife

• couple replication, resources, compartments, and heredity;
• establish controls comparing selection and drift;
• integrate open-ended-evolution metrics;
• search for major transitions in individuality.

21.7 Version 2.5.0: Observer and Reverse Recognition

• establish benchmarks for internal models and agency;
• recover effective RULEs from observational data;
• form a reverse-inference protocol from consciousness to PR;
• recode the whole of 1.x intellectual history into R0–R10 coordinates.

22. First Core Research Questions

22.1 Root and First Actual

1. What is the minimum complete definition of $\mathfrak D$?
2. How is the logical equivalence of $U$ preserved when the root language changes?
3. Is there a non-PR counterexample that satisfies every condition of the First Actual?
4. Can binary minimality be derived from a more general finite-domain theorem?
5. Can $U_*\mid\mathrm{PR}$ be given a unified type-theoretic expression?

22.2 RULE and Chaos

6. In which universality class of RULE space does Rule 979 lie?
7. How does LE asynchrony alter phase-transition boundaries?
8. Which minimum rules can simultaneously produce a background, local patterns, and collisions?
9. What is the minimum metric that distinguishes chaos from purely random noise?
10. Which structures remain stable across initial conditions, parameters, and implementations?

22.3 Proto-Matter and Proto-Chemistry

11. What is the minimum combination of indicators for proto-matter identity?
12. Does coarse-graining increase the effective information of a candidate pattern?
13. Does a finite, stable type dictionary appear?
14. Do collisions form a reproducible reaction network?
15. Do quasi-conserved quantities, catalysis, and closed reaction cycles exist?

22.4 Life and Open-Ended Evolution

16. Which compartment mechanism first forms a unit of selection?
17. How do template, metabolism, and boundary achieve minimum coupling?
18. Can the system produce a heritable new function?
19. Can novelty and complexity avoid long-term saturation?
20. How do new-level individuals form from interactions at an earlier level?

22.5 Consciousness and Reverse Recognition

21. What is the minimum structure of an internal model?
22. When does the observer become an effective causal scale in the dynamics?
23. Can AI recover PR structure backwards from proto-matter data?
24. Which root conclusions can be inferred from observation, and which can be obtained only through explanatory closure?
25. Can conscious recognition of the root form a repeatable reasoning protocol?

23. Relations between 2.0.0 and Other Research Programmes

Version 2.0.0 does not annex neighbouring disciplines. It specifies interfaces:

They study different scales on the same generative chain. The task of 2.0.0 is to give these scales, for the first time, a common root axis and explicit interfaces.

24. The Meaning of Human Thought 2.0.0

The 1.x age used human natural language as its principal runtime environment. Its concepts could be extremely profound, yet disputes were difficult to transform into shared state spaces. Version 2.0.0 has new infrastructure:

• mathematical formalisation;
• executable rules;
• large-scale computation;
• automated proof;
• AI-assisted search;
• complete records of failures;
• open data;
• cross-model replication;
• human-signed normative definitions.

This means that ontology is no longer transmitted only between thinkers' minds and texts; it can form a continuously accumulating public research object. A new researcher need not spend decades reinterpreting “Dao,” “emptiness,” “being,” or “spirit” before entering the problem. The researcher can directly read definitions, run models, submit counterexamples, extend the rule atlas, and reproduce results.

The genuine sign of Human Thought 2.0.0 is not the use of computers to write philosophy. It is that every level of ontology has a next operation:

Conclusion

Jia Baolong Researchable Ontology 2.0.0 advances human thought from an age of grand description to an age of continuous research. The 1.x history preserves genuine human knowledge of whole reality from diverse local and remote positions; 1.9 introduced rule, computation, and artificial life; 2.0.0 first organizes absolute boundary, First Actual, actual execution, RULE search, chaos, proto-matter, life, consciousness, and reverse recognition into one research map.

Its highest point is the zero-positive boundary specified by Undefined; its deepest point is why the First ActualStep must have a PR core; its widest range runs from RULE space to life, consciousness, and all intellectual history. It is not bounded by one concrete universe because every universe is only an uncollapsed chaotic solution on the PR tree.

2.0.0 does not declare theory finished. It gives every unfinished part a coordinate, method, metric, failure condition, and next deliverable. Earlier ontology chiefly left texts; researchable ontology leaves definitions, proofs, code, data, phase diagrams, counterexamples, replication records, proto-matter catalogues, life benchmarks, and reverse-recognition protocols.

Its final root axis is:

From this point, ontology must not only answer “what is the world’s origin?” It must also answer: who can study the next item, by what method, on what data, how success is established, how failure is recorded, and how the result is passed to the next level.

This is Human Thought 2.0.0.

References

Internal Jia Baolong materials

• docs/01_foundation/01_THEORY_MASTER_MAP.md
• docs/01_foundation/02_JIABAOLONG_AXIOM_SYSTEM.md
• docs/01_foundation/03_JIABAOLONG_ABSOLUTE_TRUTH.md
• docs/02_first_beat/04_FIRST_BEAT_SEVEN_ARGUMENTS.md
• docs/03_emergence/05_MATTER_TO_FIRST_CELL_EMERGENCE.md
• docs/04_reference/06_GLOSSARY_AND_FORMULAE.md
• docs/06_commentary/existence-undefined-pr-and-comparative-ontology.md

Formalization, computation, and paradigms

• Process Philosophy — Stanford Encyclopedia of Philosophy
• Computational Philosophy — Stanford Encyclopedia of Philosophy
• Theorem Proving in Lean 4
• Wolfram Physics Project

Emergence, proto-matter, and artificial life

• Quantifying causal emergence shows that macro can beat micro
• Computational models for active matter
• The MODES Toolbox: Measurements of Open-Ended Dynamics in Evolving Systems
• What Is Artificial Life Today, and Where Should It Go?

Proto-chemistry, origins of life, and protocells

• Non-equilibrium self-assembly for living matter-like properties
• The RNA World: molecular cooperation at the origins of life
• Perspective: Protocells and the Path to Minimal Life
• Physical non-equilibria for prebiotic nucleic acid chemistry