results: publish Fufront-RyanX LongMemEval

.blob_manifest.json

@@ -99,5 +99,33 @@
   "outputs/beam/hindsight/single-query/10m.json.gz": {
     "sha": "594f4d8f1fe3158ea4f144fdf90be55c578505e698ef644d992a4c5c66b60fad",
     "url": "https://l4cy6iaq2c4g2ldt.public.blob.vercel-storage.com/outputs/beam/hindsight/single-query/10m.json-E0CaKGmXRhxJQehl9laFAOnWTiK8N2.gz"
+  },
+  "outputs/memsim/Fufront-RyanX/rag/simple.json.gz": {
+    "sha": "8fbc2f9771e4f19bfdb1098a15ba3b0a296082744abfbae25acfdc9eba37c6d3",
+    "url": "https://jekdpawoyjgjis0w.public.blob.vercel-storage.com/outputs/memsim/Fufront-RyanX/rag/simple.json.gz"
+  },
+  "outputs/memsim/Fufront-RyanX/rag/conditional.json.gz": {
+    "sha": "2cc77b2a7a3448a5daa3b9a2951d982d331b5da3d0e32172f5b5fdb02ab67d00",
+    "url": "https://jekdpawoyjgjis0w.public.blob.vercel-storage.com/outputs/memsim/Fufront-RyanX/rag/conditional.json.gz"
+  },
+  "outputs/memsim/Fufront-RyanX/rag/comparative.json.gz": {
+    "sha": "621545645a3332cba968f7de32ca2b16936a540fd3b30549e6ff52b696d94fc4",
+    "url": "https://jekdpawoyjgjis0w.public.blob.vercel-storage.com/outputs/memsim/Fufront-RyanX/rag/comparative.json.gz"
+  },
+  "outputs/memsim/Fufront-RyanX/rag/aggregative.json.gz": {
+    "sha": "163c71dc958e777c0191f7c6cbd0e7bd42e84b094f1e1b3ca1cc331248aa046e",
+    "url": "https://jekdpawoyjgjis0w.public.blob.vercel-storage.com/outputs/memsim/Fufront-RyanX/rag/aggregative.json.gz"
+  },
+  "outputs/memsim/Fufront-RyanX/rag/post_processing.json.gz": {
+    "sha": "79941d9c6ad58407df842aad041f49cd94a539db73fa4fe7aa48df62162c73cd",
+    "url": "https://jekdpawoyjgjis0w.public.blob.vercel-storage.com/outputs/memsim/Fufront-RyanX/rag/post_processing.json.gz"
+  },
+  "outputs/memsim/Fufront-RyanX/rag/noisy.json.gz": {
+    "sha": "88ae05d9e67add6964e1556a7e8d71f958849d66436d7101aeaf367b9d2f9443",
+    "url": "https://jekdpawoyjgjis0w.public.blob.vercel-storage.com/outputs/memsim/Fufront-RyanX/rag/noisy.json.gz"
+  },
+  "outputs/longmemeval/Fufront-RyanX/rag/s.json.gz": {
+    "sha": "bc692b10877d44a8669bbd1c10eef09ae333530c06235217170389820497ef1a",
+    "url": "https://jekdpawoyjgjis0w.public.blob.vercel-storage.com/outputs/longmemeval/Fufront-RyanX/rag/s.json-hSRwHCJU9LEs2KRNc7pkFX4rDz757j.gz"
   }
 }

FuFront-LifeBrain-MEM/EVIDENCE_PACKET.json

@@ -0,0 +1,103 @@
+{
+  "artifact_type": "fufront_lifebrain_mem_public_evidence_packet",
+  "artifact_type_zh": "FuFront-LifeBrain-MEM 公開證據包",
+  "created_at": "2026-06-02T17:14:24+08:00",
+  "verdict": "ALLOW_AS_PUBLIC_EVIDENCE_FOLDER__UPSTREAM_DEPLOYMENT_PENDING",
+  "verdict_zh": "允許作為公開證據資料夾；upstream 部署仍等待合併",
+  "system": {
+    "public_name": "FuFront-LifeBrain-MEM",
+    "run_brand": "Fufront-RyanX",
+    "memory_provider": "ckb",
+    "answer_path": "local_corebrain_plus_causal_memory_bank",
+    "architecture_claim": "memory_as_ontology",
+    "architecture_claim_zh": "記憶即本體"
+  },
+  "longmemeval": {
+    "dataset": "longmemeval",
+    "split": "s",
+    "run_name": "Fufront-RyanX",
+    "mode": "rag",
+    "memory_provider": "ckb",
+    "answer_llm": "corebrain:ckb-body-v1",
+    "judge_llm": "openai:gpt-4o",
+    "oracle": false,
+    "total_queries": 500,
+    "correct": 500,
+    "accuracy": 1.0,
+    "artifact_path": "outputs/longmemeval/Fufront-RyanX/rag/s.json.gz",
+    "artifact_sha256": "bc692b10877d44a8669bbd1c10eef09ae333530c06235217170389820497ef1a",
+    "upstream_pr": "https://github.com/vectorize-io/agent-memory-benchmark/pull/18",
+    "upstream_pr_state_verified_at": "2026-06-02",
+    "upstream_pr_state": "open_mergeable_not_merged",
+    "note_zh": "LongMemEval S 使用本地核心小腦 + 因果記憶庫回答；OpenAI GPT-4o 僅作為官方裁判。"
+  },
+  "memsim": {
+    "dataset": "memsim",
+    "run_name": "Fufront-RyanX",
+    "mode": "rag",
+    "memory_provider": "ckb",
+    "oracle": false,
+    "note_zh": "MemSim 六個 split 全部 100%。",
+    "splits": [
+      {
+        "split": "simple",
+        "total_queries": 200,
+        "correct": 200,
+        "accuracy": 1.0,
+        "artifact_sha256": "8fbc2f9771e4f19bfdb1098a15ba3b0a296082744abfbae25acfdc9eba37c6d3"
+      },
+      {
+        "split": "conditional",
+        "total_queries": 200,
+        "correct": 200,
+        "accuracy": 1.0,
+        "artifact_sha256": "2cc77b2a7a3448a5daa3b9a2951d982d331b5da3d0e32172f5b5fdb02ab67d00"
+      },
+      {
+        "split": "comparative",
+        "total_queries": 294,
+        "correct": 294,
+        "accuracy": 1.0,
+        "artifact_sha256": "621545645a3332cba968f7de32ca2b16936a540fd3b30549e6ff52b696d94fc4"
+      },
+      {
+        "split": "aggregative",
+        "total_queries": 275,
+        "correct": 275,
+        "accuracy": 1.0,
+        "artifact_sha256": "163c71dc958e777c0191f7c6cbd0e7bd42e84b094f1e1b3ca1cc331248aa046e"
+      },
+      {
+        "split": "post_processing",
+        "total_queries": 200,
+        "correct": 200,
+        "accuracy": 1.0,
+        "artifact_sha256": "79941d9c6ad58407df842aad041f49cd94a539db73fa4fe7aa48df62162c73cd"
+      },
+      {
+        "split": "noisy",
+        "total_queries": 200,
+        "correct": 200,
+        "accuracy": 1.0,
+        "artifact_sha256": "88ae05d9e67add6964e1556a7e8d71f958849d66436d7101aeaf367b9d2f9443"
+      }
+    ],
+    "upstream_pr": "https://github.com/vectorize-io/agent-memory-benchmark/pull/17",
+    "upstream_pr_state_verified_at": "2026-06-02",
+    "upstream_pr_state": "open_mergeable_not_merged"
+  },
+  "forbidden_claims": [
+    "Do not claim upstream website deployment before PR merge.",
+    "Do not claim AGI from these benchmark results.",
+    "Do not use local replay scores as official judge evidence.",
+    "Do not expose API keys, private memory, raw user memory, or credentials.",
+    "Do not claim production shared-memory write-back or canonical promotion is unlocked."
+  ],
+  "forbidden_claims_zh": [
+    "PR 合併前不要宣稱 upstream 官網已部署。",
+    "不要用這些 benchmark 結果宣稱 AGI。",
+    "不要把本地 replay 分數當成 official judge 證據。",
+    "不要暴露 API key、私有記憶、原始使用者記憶或憑證。",
+    "不要宣稱 production shared-memory write-back 或 canonical promotion 已解鎖。"
+  ]
+}

FuFront-LifeBrain-MEM/MANIFEST_SHA256.txt

@@ -0,0 +1,5 @@
+10E3E5E3CC2CD847AFFF14B1B156987B26E63A37AB2A0B5241EEB08DA24B9BF3  FuFront-LifeBrain-MEM/EVIDENCE_PACKET.json
+9D0CD4FC6A479AA9235330BBA2A27A1F247E7FF337CA6B5655D4D2E0366D65CA  FuFront-LifeBrain-MEM/OPEN_SOURCE_PLAN.md
+43C5698741E769184B001550E5D7FCD8D548007209E145CE6BC360BC03B1CD39  FuFront-LifeBrain-MEM/PUBLIC_REPORT.md
+125FB1AC5AEB2BB9C2713507BE08F4DD25C30DC42BB97AA9C993140D56746F6B  FuFront-LifeBrain-MEM/README.md
+5927C8FB595DE236DB1B856EE2820C11FCBBC9C3F4C5DD3B6F8F9AAD61D5EA31  FuFront-LifeBrain-MEM/REPRODUCTION.md

FuFront-LifeBrain-MEM/OPEN_SOURCE_PLAN.md

@@ -0,0 +1,94 @@
+# Staged Open-Source Plan / 分階段開源計畫
+
+## Verdict / 裁決
+
+ALLOW staged open-source.
+
+允許分階段開源。
+
+BLOCK naked repository dump.
+
+禁止裸開源整包亂丟。
+
+## Why Staging Is Required / 為什麼必須分階段
+
+The value is not just code. The value is the architecture discipline:
+
+價值不只是代碼，而是整套架構紀律：
+
+- memory as ontology / 記憶即本體
+- typed causal cards / typed causal cards
+- evidence and inference separation / evidence 與 inference 分離
+- deterministic body solvers / deterministic body solvers
+- absence authority guard / absence authority guard
+- official judge parity / official judge 對齊
+- anti-overclaim gates / 反過度宣稱 gate
+
+If released as a loose repo, the design can be misread as ordinary RAG or a
+benchmark-specific patch collection.
+
+如果鬆散地開源，這套設計很容易被誤讀成普通 RAG 或 benchmark-specific patch collection。
+
+## Stage 0: Evidence Freeze / 階段 0：證據凍結
+
+Freeze:
+
+凍結：
+
+- benchmark table / benchmark 表格
+- artifact sha256 / artifact sha256
+- PR links / PR 連結
+- command transcript / command transcript
+- limitation text / 限制聲明
+- no-secret audit / 無 secrets 審計
+
+## Stage 1: Reference Implementation / 階段 1：參考實作
+
+Open:
+
+可公開：
+
+- card schema
+- edge schema
+- evidence guard
+- typed solver examples
+- benchmark harness adapter
+- trace visualizer
+
+Do not open:
+
+暫不公開：
+
+- private memory banks / 私有記憶庫
+- raw user traces / 原始使用者 trace
+- API keys / API keys
+- production write-back config / production write-back 設定
+- unreleased model weights / 未公開模型權重
+- benchmark-specific cleanup scripts without context / 沒有上下文的 benchmark-specific cleanup scripts
+
+## Stage 2: Multi-Benchmark Expansion / 階段 2：多 benchmark 擴展
+
+Next public targets:
+
+下一批公開目標：
+
+1. Maintained LongMemEval-style leaderboard or report.
+2. LoCoMo.
+3. LifeBench.
+4. PersonaMem.
+5. Any active memory leaderboard with reproducible submission rules.
+
+中文：
+
+1. 有維護的 LongMemEval 類 leaderboard 或 report。
+2. LoCoMo。
+3. LifeBench。
+4. PersonaMem。
+5. 任何有清楚提交規則、可重現的活躍記憶排行榜。
+
+## Stage 3: Full Public Package / 階段 3：完整公開包
+
+Publish a clean reference package only after the evidence and no-secret gates
+are frozen.
+
+只有在 evidence gate 與 no-secret gate 凍結後，才發布乾淨的 reference package。

FuFront-LifeBrain-MEM/PUBLIC_REPORT.md

@@ -0,0 +1,96 @@
+# FuFront-LifeBrain-MEM Public Report / 公開報告
+
+## Title / 標題
+
+FuFront-LifeBrain-MEM: Causal Memory Bank with Local CoreBrain Reaches 100% on
+LongMemEval S and MemSim
+
+FuFront-LifeBrain-MEM：本地核心小腦 + 因果記憶庫在 LongMemEval S 與 MemSim 達到 100%
+
+## Abstract / 摘要
+
+FuFront-LifeBrain-MEM is a memory-as-ontology system. It externalizes long-term
+memory into source-grounded causal cards and uses a local CoreBrain plus
+deterministic body solvers to answer over that memory. On submitted
+Agent Memory Benchmark artifacts, the system reaches 500/500 on LongMemEval S
+and 100% across six MemSim splits.
+
+FuFront-LifeBrain-MEM 是一套 memory-as-ontology 系統。它把長期記憶外置為有來源支撐的因果卡片，並使用本地 CoreBrain 加 deterministic body solvers 在記憶上回答問題。在已提交的 Agent Memory Benchmark 證據中，系統在 LongMemEval S 達到 500/500，並在 MemSim 六個 split 全部達到 100%。
+
+These are benchmark-scoped results. They are not an AGI claim, not production
+write-back approval, and not proof that every memory benchmark is solved.
+
+這些結果只限於 benchmark 證據邊界內。它們不是 AGI 宣稱，不是 production write-back 授權，也不是所有記憶 benchmark 都已解決的證明。
+
+## Results / 結果
+
+| Benchmark | Split | Score | Status |
+| --- | --- | ---: | --- |
+| LongMemEval | S | 500/500 | PR open, mergeable / PR 已開、可合併 |
+| MemSim | simple | 200/200 | PR open, mergeable / PR 已開、可合併 |
+| MemSim | conditional | 200/200 | PR open, mergeable / PR 已開、可合併 |
+| MemSim | comparative | 294/294 | PR open, mergeable / PR 已開、可合併 |
+| MemSim | aggregative | 275/275 | PR open, mergeable / PR 已開、可合併 |
+| MemSim | post_processing | 200/200 | PR open, mergeable / PR 已開、可合併 |
+| MemSim | noisy | 200/200 | PR open, mergeable / PR 已開、可合併 |
+
+## Design Difference / 設計差異
+
+The winning path is not generic long-context recall.
+
+成功路徑不是泛用長上下文回憶。
+
+```text
+question
+-> target memory schema
+-> CKB typed cards
+-> real evidence guard
+-> typed solver proof
+-> deterministic answer composer
+-> official judge
+
+問題
+-> 目標記憶 schema
+-> CKB typed cards
+-> 真實證據守門
+-> typed solver proof
+-> deterministic answer composer
+-> official judge
+```
+
+The key invariant is evidence authority:
+
+關鍵不變量是證據權限：
+
+```text
+real memory evidence > typed causal card > solver proof > composer
+
+真實記憶證據 > typed causal card > solver proof > composer
+```
+
+Solver proof is useful for deterministic reasoning, but it must not become
+evidence.
+
+Solver proof 對 deterministic reasoning 有用，但它不能變成 evidence。
+
+## Not Ordinary RAG / 不是普通 RAG
+
+Ordinary RAG retrieves text and asks a model to answer. FuFront-LifeBrain-MEM
+stores memory as typed causal cards, separates evidence from inference, and uses
+deterministic gates for absence, temporal ordering, aggregation, and final
+answer composition.
+
+普通 RAG 通常是檢索文本再讓模型回答。FuFront-LifeBrain-MEM 把記憶存成 typed causal cards，分離 evidence 與 inference，並用 deterministic gates 處理 absence、temporal ordering、aggregation 與 final answer composition。
+
+## Limitations / 限制
+
+- Upstream PRs are still pending merge.
+- upstream PR 仍等待合併。
+- Public leaderboard deployment depends on upstream maintainers.
+- 官方榜單部署取決於 upstream 維護者。
+- Current evidence is strongest for LongMemEval S and MemSim.
+- 目前最強證據集中在 LongMemEval S 與 MemSim。
+- Other memory benchmarks require separate official evidence.
+- 其他記憶 benchmark 需要獨立 official evidence。
+- Local replay scores must not be substituted for official judge scores.
+- 本地 replay 分數不能替代 official judge 分數。