ResearchRES-2026-0002v0.1
PACC conjecture — do non-probabilistic primitives converge to probability-like structure?
Systems whose canonical state and update rules never require probability distributions, Bayesian posteriors or sampling are placed in the same evidence-integration tasks as an exact Bayesian reference. The lab measures whether low-complexity train-only maps carry their states to the Bayesian state, whether the maps commute with updates and survive interventions, and whether independently designed families converge — a four-level ladder (PACC-B, -R, -D, -A) with preregistered falsification conditions.
Research questions
research_questions- Posed deliberately as a similar-but-not-probabilistic counter-construction to the claim that modern AI is simply a probability model: some of it is, all of it need not be. The experiments are set up so that either outcome is informative — convergence says something about attractors, divergence says something about what probability is uniquely doing.
- Is probability a necessary ontology of intelligence, an effective representation, an engineering convergence form, or an observer's compression of deeper competitive state?
- Can a non-probabilistic state be mapped by a low-complexity map to a Bayesian state on held-out tasks, with the update diagram approximately commuting?
- Do three or more independently designed non-probabilistic families converge (PACC-A)?
Claims
claims- Supported as a controlled micro-environment witness: PACC-B, PACC-R and PACC-D for N0/N1, N2 and N3 under uniform and heterogeneous evidence quality (v0.1–v0.2), with shuffled-target controls roughly two orders of magnitude worse.
- Convergence has a nontrivial basin, not a demonstrated universal attractor: N2 fails the representation threshold under adversarial geometry, and task-state convergence is architecture- and seed-sensitive once reliability must be learned (v0.2–v0.3).
- Decision-level probability coordinates can converge while a latent explanatory variable (common-cause posterior) has no direct low-complexity coordinate; the latent coordinate is hierarchical/compositional (v0.4–v0.6).
- Frozen composed coordinates transfer across dependence regimes over finite, family-specific basins; a three-chart atlas covers 8/9 of the sweep; forward cocycle coherence is robust on triple overlap, but reverse transport carries a persistent destination bias (v0.7–v0.13).
Limitations
limitations- PACC-A is not supported: N0 and N1 are exact reparameterizations, leaving two independent convergent families against a preregistered minimum of three.
- Everything is a transparent synthetic micro-lab; nothing here shows that probability is false, that Bayesian inference is unnecessary, that LLM internals are equivalent, or that probability is an observer projection.
- PACC is a conjecture. Its numbers are synthetic data and theoretical reasoning; until a real hybrid model exists, an inference is only an inference — theoretically possible is not actually possible.
Relations
| Source | Relation | Target | Status | ID |
|---|---|---|---|---|
RES-2026-0002 PACC conjecture — do non-probabilistic primitives converge to probability-like structure? | belongs_to | PRG-2026-0001 Adaptive Epistemic Systems | ACTIVE | REL-2026-0002 |
RES-2026-0002 PACC conjecture — do non-probabilistic primitives converge to probability-like structure? | develops | THY-2026-0003 Intelligent architecture attractor | ACTIVE | REL-2026-0012 |
RES-2026-0002 PACC conjecture — do non-probabilistic primitives converge to probability-like structure? | develops | THY-2026-0004 Probability is not Bayesian; Bayes cannot self-authorize its premises | ACTIVE | REL-2026-0014 |
RES-2026-0002 PACC conjecture — do non-probabilistic primitives converge to probability-like structure? | develops | THY-2026-0005 PACC conjecture — the four-level convergence ladder | ACTIVE | REL-2026-0015 |
RES-2026-0004 A PACC-style runtime on language models: reasoning, intent and creative breadth | extends | RES-2026-0002 PACC conjecture — do non-probabilistic primitives converge to probability-like structure? | ACTIVE | REL-2026-0006 |
PAP-2026-0012 The Probabilistic Appearance Convergence Conjecture (PACC) | reports | RES-2026-0002 PACC conjecture — do non-probabilistic primitives converge to probability-like structure? | ACTIVE | REL-2026-0050 |
SYS-2026-0002 PACC-Lab — micro-lab harness for the convergence conjecture | supports | RES-2026-0002 PACC conjecture — do non-probabilistic primitives converge to probability-like structure? | ACTIVE | REL-2026-0083 |
DAT-2026-0001 PACC synthetic evidence worlds | supports | RES-2026-0002 PACC conjecture — do non-probabilistic primitives converge to probability-like structure? | ACTIVE | REL-2026-0092 |
History and provenance
- Canonical URL
- https://evemisslab.com/ai/research/RES-2026-0002/
- Machine-readable
/ai/research/RES-2026-0002/index.json- Snapshot
AI-SNAPSHOT-v0.1-fe85b9694a45- Provenance
source- EveMissLab research collection: Adaptive Epistemic Systems (真本體論13)
extracted_by- Splice (Claude Code), reading the canonical UTF-8 sources and each lab's own result reports
extracted_at- 2026-09-11
generator- tools/extract_aes/extract.py
claim_boundary- status, evidence level and result type follow the source artifact's own stated claim boundary; nothing is upgraded beyond what the report supports