{ "id": "EXP-2026-0020", "kind": "experiment", "label": "PACC-Lab v0.13 — reverse residual field / base-point dependence", "created_at": "2026-09-09", "updated_at": "2026-09-09", "values": { "eml_status": "STABLE", "eml_evidence_level": "E3", "eml_object_version": "0.1", "eml_canonical_url": "https://evemisslab.com/ai/experiments/EXP-2026-0020/", "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": "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.", "eml_summary_zh": "量測 held-out 反向殘差 r_CA(S) = Φ_A(S) − T_CA(Φ_C(S)) 的均值、共變異、潛在 logit 能量集中度、層間 vs 層內變異、方向穩定性，以及只用訓練集的逐層常數校正（對照全域與 shuffled 層控制）。N0/N1 把 98 % 的殘差能量集中在潛在 logit 軸上，但均值方向跨 seed 翻號（跨 seed 餘弦 ≈ −1），層間結構只有 ≈ 0.1–0.3 %（門檻 20 %），逐層校正對 held-out 保真度的改變 < 10⁻⁵——沒有家族通過。", "eml_label_zh": "PACC-Lab v0.13——反向殘差場／基點依賴", "eml_primary_domain": "Model Representation", "eml_domains": [ "Evaluation", "Formal AI" ], "eml_program_id": "PRG-2026-0001", "eml_hypothesis": "The reverse bias is a base-point-conditioned residual field r(S) ≈ b_q + ε learnable per frozen overlap stratum.", "eml_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" }, "eml_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.", "eml_limitations": [ "Residual unstructuredness is not established in every sign-free or subspace sense — that is the v0.14 question." ], "eml_random_seeds": [ "20260909", "4 secondary seeds", "3 primary-scale-ish seeds" ], "eml_run_count": 3, "eml_result_type": "NEGATIVE", "eml_controls": [ "shuffled-target mapping", "constant prediction", "broken composition (v0.6+)", "target-local refit (v0.7+)" ], "eml_software_environment": "Python; deterministic seeded generators; no network, no LLM.", "eml_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.", "eml_completed_at": "2026-09-09", "eml_data_basis": "SYNTHETIC", "eml_model_ids": [ "MOD-2026-0001", "MOD-2026-0002", "MOD-2026-0003", "MOD-2026-0004", "MOD-2026-0005" ], "eml_dataset_ids": [ "DAT-2026-0001" ], "eml_benchmark_ids": [ "BEN-2026-0001" ], "eml_authors": [ "Neo.K (EveMissLab)" ], "eml_ai_collaborators": [ "Sol (GPT-5.6, OpenAI ChatGPT)" ] }, "canonical_url": "https://evemisslab.com/ai/experiments/EXP-2026-0020/", "json": "/ai/experiments/EXP-2026-0020/index.json", "relations": [ { "id": "REL-2026-0302", "predicate": "runs_on", "source": "EXP-2026-0020", "target": "SYS-2026-0002", "status": "ACTIVE" }, { "id": "REL-2026-0303", "predicate": "uses_benchmark", "source": "EXP-2026-0020", "target": "BEN-2026-0001", "status": "ACTIVE" }, { "id": "REL-2026-0304", "predicate": "uses_dataset", "source": "EXP-2026-0020", "target": "DAT-2026-0001", "status": "ACTIVE" }, { "id": "REL-2026-0305", "predicate": "uses_model", "source": "EXP-2026-0020", "target": "MOD-2026-0001", "status": "ACTIVE" }, { "id": "REL-2026-0306", "predicate": "uses_model", "source": "EXP-2026-0020", "target": "MOD-2026-0002", "status": "ACTIVE" }, { "id": "REL-2026-0307", "predicate": "uses_model", "source": "EXP-2026-0020", "target": "MOD-2026-0003", "status": "ACTIVE" }, { "id": "REL-2026-0308", "predicate": "uses_model", "source": "EXP-2026-0020", "target": "MOD-2026-0004", "status": "ACTIVE" }, { "id": "REL-2026-0309", "predicate": "uses_model", "source": "EXP-2026-0020", "target": "MOD-2026-0005", "status": "ACTIVE" }, { "id": "REL-2026-0310", "predicate": "tests", "source": "EXP-2026-0020", "target": "THY-2026-0005", "status": "ACTIVE" }, { "id": "REL-2026-0311", "predicate": "extends", "source": "EXP-2026-0020", "target": "EXP-2026-0019", "status": "ACTIVE" }, { "id": "REL-2026-0312", "predicate": "produced", "source": "EXP-2026-0020", "target": "ART-2026-0033", "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 } }