ExperimentEXP-2026-0020v0.1

PACC-Lab v0.13 — reverse residual field / base-point dependence

The held-out reverse residual r_CA(S) = Φ_A(S) − T_CA(Φ_C(S)) is measured for mean, covariance, latent-logit energy concentration, between- versus within-stratum variance, direction stability and a train-only per-stratum constant correction against global and shuffled-stratum controls. N0/N1 concentrate 98 % of residual energy on the latent-logit axis, yet the mean direction flips sign across seeds (cross-seed cosine ≈ −1), between-q structure is ≈ 0.1–0.3 % against a 20 % gate, and stratum correction changes held-out fidelity by < 10⁻⁵ — no family passes.

Research status: STABLE the current conclusions are relatively stable
Evidence level: E3 Repeated experiment
Result: NEGATIVE
Data basis: SYNTHETIC Synthetic data and theoretical reasoning. Many now treat synthetic data as if it were real; this laboratory says the opposite deliberately — until a real hybrid model exists, an inference is only an inference, and theoretically possible is not actually possible.
Version: 0.1
Updated: 2026-09-09
Created: 2026-09-09
Domain: Model Representation, Evaluation, Formal AI
Program: PRG-2026-0001 Adaptive Epistemic Systems
Authors: Neo.K (EveMissLab)
AI collaborators: Sol (GPT-5.6, OpenAI ChatGPT)

Hypothesis

hypothesis: The reverse bias is a base-point-conditioned residual field r(S) ≈ b_q + ε learnable per frozen overlap stratum.

Setup

model_ids: MOD-2026-0001 N0 — signed support
MOD-2026-0002 N1 — ordinal tournament
MOD-2026-0003 N2 — signed graph
MOD-2026-0004 N3 — constraint competition
MOD-2026-0005 Bayesian reference (exact posterior / Beta-Bernoulli / joint common-cause / HMM)
dataset_ids: DAT-2026-0001 PACC synthetic evidence worlds
benchmark_ids: BEN-2026-0001 PACC micro-lab protocol v0.1 (frozen gates)
software_environment: Python; deterministic seeded generators; no network, no LLM.

Runs

run_count

random_seeds

20260909
4 secondary seeds
3 primary-scale-ish seeds

controls

shuffled-target mapping
constant prediction
broken composition (v0.6+)
target-local refit (v0.7+)

metrics

verdict

RESIDUAL MOSTLY UNSTRUCTURED

latent_energy_fraction_affine

N0: 0.9845
N2: 0.7723
N3: 0.3182

between_q_fraction

0.0012–0.0033 vs gate 0.20

stratum_correction

N0_uncorrected: 0.011616
N0_corrected: 0.011624

cross_family_direction_cosine_median

0.7024

cross_seed_direction_cosine_N0

-0.9977

base_point_pass

0 for every family

Interpretation

interpretation: A stable residual axis is not a stable residual orientation and not a base-point field; the reverse bias contains family-dependent dominant error modes, which weakens the gauge/connection-like reading. Next (v0.14, preregistered): sign-free residual subspace / principal-axis stability, with no q input, no higher degree, no neural mapper.

Limitations

limitations: Residual unstructuredness is not established in every sign-free or subspace sense — that is the v0.14 question.

Reproduction

reproduction_instructions: Extract the version's FINAL bundle; python -m pytest -q; run the version's primary script with the recorded seed; docs/PACC_LAB_v0.N_RESULTS.md and docs/EXPERIMENT_PROTOCOL_v0.N.md are inside the bundle.

Recorded fields

completed_at: 2026-09-09

Relations

Source	Relation	Target	Status	ID
`EXP-2026-0020` PACC-Lab v0.13 — reverse residual field / base-point dependence	`runs_on`	`SYS-2026-0002` PACC-Lab — micro-lab harness for the convergence conjecture	`ACTIVE`	`REL-2026-0302`
`EXP-2026-0020` PACC-Lab v0.13 — reverse residual field / base-point dependence	`uses_benchmark`	`BEN-2026-0001` PACC micro-lab protocol v0.1 (frozen gates)	`ACTIVE`	`REL-2026-0303`
`EXP-2026-0020` PACC-Lab v0.13 — reverse residual field / base-point dependence	`uses_dataset`	`DAT-2026-0001` PACC synthetic evidence worlds	`ACTIVE`	`REL-2026-0304`
`EXP-2026-0020` PACC-Lab v0.13 — reverse residual field / base-point dependence	`uses_model`	`MOD-2026-0001` N0 — signed support	`ACTIVE`	`REL-2026-0305`
`EXP-2026-0020` PACC-Lab v0.13 — reverse residual field / base-point dependence	`uses_model`	`MOD-2026-0002` N1 — ordinal tournament	`ACTIVE`	`REL-2026-0306`
`EXP-2026-0020` PACC-Lab v0.13 — reverse residual field / base-point dependence	`uses_model`	`MOD-2026-0003` N2 — signed graph	`ACTIVE`	`REL-2026-0307`
`EXP-2026-0020` PACC-Lab v0.13 — reverse residual field / base-point dependence	`uses_model`	`MOD-2026-0004` N3 — constraint competition	`ACTIVE`	`REL-2026-0308`
`EXP-2026-0020` PACC-Lab v0.13 — reverse residual field / base-point dependence	`uses_model`	`MOD-2026-0005` Bayesian reference (exact posterior / Beta-Bernoulli / joint common-cause / HMM)	`ACTIVE`	`REL-2026-0309`
`EXP-2026-0020` PACC-Lab v0.13 — reverse residual field / base-point dependence	`tests`	`THY-2026-0005` PACC conjecture — the four-level convergence ladder	`ACTIVE`	`REL-2026-0310`
`EXP-2026-0020` PACC-Lab v0.13 — reverse residual field / base-point dependence	`extends`	`EXP-2026-0019` PACC-Lab v0.12 — bounded quadratic transition	`ACTIVE`	`REL-2026-0311`
`EXP-2026-0020` PACC-Lab v0.13 — reverse residual field / base-point dependence	`produced`	`ART-2026-0033` PACC-Lab v0.13 Residual Field FINAL `artifact://evemisslab/adaptive-epistemic-systems/PACC-Lab_v0.13_Residual_Field_FINAL.zip`	`ACTIVE`	`REL-2026-0312`

History and provenance

Canonical URL

https://evemisslab.com/ai/experiments/EXP-2026-0020/

Machine-readable

/ai/experiments/EXP-2026-0020/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