{ "id": "RES-2026-0002", "kind": "research", "label": "PACC conjecture — do non-probabilistic primitives converge to probability-like structure?", "created_at": "2026-09-08", "updated_at": "2026-09-09", "values": { "eml_status": "EXPERIMENTAL", "eml_evidence_level": "E3", "eml_object_version": "0.1", "eml_canonical_url": "https://evemisslab.com/ai/research/RES-2026-0002/", "eml_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" }, "eml_summary": "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.", "eml_summary_zh": "把 canonical state 與更新規則都不需要概率分布、Bayesian posterior 或抽樣的系統，放進與精確 Bayesian 參考相同的證據整合任務中，量測低複雜度、只在訓練集擬合的映射能否把它們的狀態送到 Bayesian 狀態、映射是否與更新交換並在干預下存活、以及獨立設計的家族是否收斂——四層階梯（PACC-B／R／D／A）與預先登記的否證條件。", "eml_label_zh": "PACC 猜想——非概率 primitive 會不會收斂到概率表象？", "eml_primary_domain": "Model Representation", "eml_domains": [ "Formal AI", "Reasoning", "Evaluation" ], "eml_program_id": "PRG-2026-0001", "eml_data_basis": "SYNTHETIC", "eml_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)?" ], "eml_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)." ], "eml_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." ], "eml_authors": [ "Neo.K (EveMissLab)" ], "eml_ai_collaborators": [ "Sol (GPT-5.6, OpenAI ChatGPT)" ] }, "canonical_url": "https://evemisslab.com/ai/research/RES-2026-0002/", "json": "/ai/research/RES-2026-0002/index.json", "relations": [ { "id": "REL-2026-0002", "predicate": "belongs_to", "source": "RES-2026-0002", "target": "PRG-2026-0001", "status": "ACTIVE" }, { "id": "REL-2026-0012", "predicate": "develops", "source": "RES-2026-0002", "target": "THY-2026-0003", "status": "ACTIVE" }, { "id": "REL-2026-0014", "predicate": "develops", "source": "RES-2026-0002", "target": "THY-2026-0004", "status": "ACTIVE" }, { "id": "REL-2026-0015", "predicate": "develops", "source": "RES-2026-0002", "target": "THY-2026-0005", "status": "ACTIVE" }, { "id": "REL-2026-0006", "predicate": "extends", "source": "RES-2026-0004", "target": "RES-2026-0002", "status": "ACTIVE" }, { "id": "REL-2026-0050", "predicate": "reports", "source": "PAP-2026-0012", "target": "RES-2026-0002", "status": "ACTIVE" }, { "id": "REL-2026-0083", "predicate": "supports", "source": "SYS-2026-0002", "target": "RES-2026-0002", "status": "ACTIVE" }, { "id": "REL-2026-0092", "predicate": "supports", "source": "DAT-2026-0001", "target": "RES-2026-0002", "status": "ACTIVE" } ], "snapshot": { "snapshot_id": "AI-SNAPSHOT-v0.1-fe85b9694a45", "created_at": "2026-09-11T05:00:27Z", "format_version": "0.1", "sedb_baseline": "v0.4B contract; static source content/ai/", "generator_version": "evemisslab-com ai_research 0.1", "object_count": 124, "relation_count": 499, "artifact_count": 58 } }