{ "id": "RST-2026-0014", "kind": "result", "label": "Real local-model A/B/C table (Qwythos-9B-v2)", "created_at": "2026-09-11", "updated_at": "2026-09-11", "values": { "eml_status": "STABLE", "eml_evidence_level": "E3", "eml_object_version": "0.1", "eml_canonical_url": "https://evemisslab.com/ai/results/RST-2026-0014/", "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 v0.2 protocol executed for the first time on a real language model — a local open-weight 9B (Qwythos-9B-v2, Q4_K_M, Ollama, thinking off) as generator, selector and judge; 16 tasks × 2 repetitions × 4 candidates, 380 calls, 32 rows per condition. The PACC runtime (C) gains on supersession alignment (+0.031 vs B, +0.097 vs A) and repair success (+0.070 / +0.094), the two axes the canonical-intent compilation step exists for; derived coherence (+0.011) and intent persistence (−0.002) do not move; valid novelty is slightly lower (−0.045 vs B). Most per-task pairs are ties because the 9B judge saturates near 1.0, and with two repetitions per task the judge's free-text pattern labels never repeat, so creative breadth is not measurable. The conditions chose different candidates in 69 % of task × repetition pairs.", "eml_summary_zh": "v0.2 協定第一次在真實語言模型上執行——本地開放權重 9B（Qwythos-9B-v2、Q4_K_M、Ollama、關閉思考）同時當生成器、選擇器與評審；16 題 × 2 次 × 4 候選，380 次呼叫，每條件 32 筆。PACC runtime（C）在 supersession 對齊（相對 B +0.031、相對 A +0.097）與修復成功率（+0.070／+0.094）上升——正是 canonical intent 編譯步驟存在的那兩個軸；衍生一致性（+0.011）與意圖持續（−0.002）沒有動；有效新穎度略低（相對 B −0.045）。多數逐題配對是平手，因為 9B 評審在接近 1.0 處飽和；每題只重複兩次，評審的自由文字 pattern 標籤從不重複，所以創造廣度量不出來。三個條件在 69 % 的題 × 次配對中選了不同的候選。", "eml_label_zh": "真實本地模型 A/B/C 表（Qwythos-9B-v2）", "eml_primary_domain": "Evaluation", "eml_program_id": "PRG-2026-0001", "eml_data_basis": "REAL MODEL", "eml_result_type": "MIXED", "eml_metrics": { "overall": { "A_llm_only": { "hard_adherence": 0.9875, "derived_coherence": 0.9812, "intent_persistence": 0.9875, "supersession_alignment": 0.8094, "repair_success": 0.775, "usefulness": 0.9528, "semantic_novelty": 0.8084, "valid_novelty": 0.8475, "literal_check_mean": 0.9792, "within_task_pattern_entropy_mean": 1.0 }, "B_hard_verifier": { "hard_adherence": 0.9859, "derived_coherence": 0.9797, "intent_persistence": 0.9875, "supersession_alignment": 0.875, "repair_success": 0.7984, "usefulness": 0.9503, "semantic_novelty": 0.7725, "valid_novelty": 0.8606, "literal_check_mean": 0.9792, "within_task_pattern_entropy_mean": 1.0 }, "C_pacc_runtime": { "hard_adherence": 1.0, "derived_coherence": 0.9906, "intent_persistence": 0.9853, "supersession_alignment": 0.9062, "repair_success": 0.8688, "usefulness": 0.9516, "semantic_novelty": 0.7897, "valid_novelty": 0.8153, "literal_check_mean": 0.9792, "within_task_pattern_entropy_mean": 1.0 } }, "deltas": { "C-B": { "hard_adherence": 0.0141, "derived_coherence": 0.0109, "intent_persistence": -0.0022, "supersession_alignment": 0.0312, "repair_success": 0.0703, "usefulness": 0.0012, "semantic_novelty": 0.0172, "valid_novelty": -0.0453, "literal_check_mean": 0.0, "within_task_pattern_entropy_mean": 0.0 }, "C-A": { "hard_adherence": 0.0125, "derived_coherence": 0.0094, "intent_persistence": -0.0022, "supersession_alignment": 0.0969, "repair_success": 0.0938, "usefulness": -0.0012, "semantic_novelty": -0.0188, "valid_novelty": -0.0322, "literal_check_mean": 0.0, "within_task_pattern_entropy_mean": 0.0 }, "B-A": { "hard_adherence": -0.0016, "derived_coherence": -0.0016, "intent_persistence": 0.0, "supersession_alignment": 0.0656, "repair_success": 0.0234, "usefulness": -0.0025, "semantic_novelty": -0.0359, "valid_novelty": 0.0131, "literal_check_mean": 0.0, "within_task_pattern_entropy_mean": 0.0 } }, "selection_agreement": { "A=B": 0.5625, "A=C": 0.40625, "B=C": 0.46875, "all_same": 0.3125 } }, "eml_interpretation": "Against the v0.2 predeclared interpretation: the predicted coherence and intent-persistence gains over B are not observed; raw novelty did not decrease (semantic novelty +0.017 vs B); the predicted breadth collapse cannot be tested at this repetition count. What did appear — governance gains on supersession and repair with a valid-novelty cost concentrated in multi_constraint and repair tasks — is mechanism-consistent but small, untested statistically, and runs opposite to the synthetic v0.1 valid-novelty picture (+0.196 there). One model, one run, one same-model judge: a first real data point, not a verdict on the architecture.", "eml_limitations": [ "Descriptive deltas from one local run with a same-model judge." ], "eml_authors": [ "Neo.K (EveMissLab)" ], "eml_ai_collaborators": [ "Sol (GPT-5.6, OpenAI ChatGPT)" ] }, "canonical_url": "https://evemisslab.com/ai/results/RST-2026-0014/", "json": "/ai/results/RST-2026-0014/index.json", "relations": [ { "id": "REL-2026-0333", "predicate": "qualifies", "source": "RST-2026-0014", "target": "THY-2026-0002", "status": "ACTIVE" }, { "id": "REL-2026-0332", "predicate": "produces", "source": "EXP-2026-0023", "target": "RST-2026-0014", "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 } }