PACC 猜想——非概率 primitive 會不會收斂到概率表象？

研究問題

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)?

主張

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).

限制

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.

關係

歷史與來源歷程

Canonical URL

https://evemisslab.com/ai/research/RES-2026-0002/

機器可讀

/ai/research/RES-2026-0002/index.json

快照

AI-SNAPSHOT-v0.1-fe85b9694a45

來源歷程

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