{
  "name": "phd-paper-pipeline-mcp",
  "version": "1.2.0",
  "lastUpdated": "1970-01-01T00:00:00.000Z",
  "agentsRule5Status": {
    "compliant": true,
    "rule": "Desynchronising EXCLUDED_CAMPAIGNS and REPORTED_CAMPAIGNS once put a reported total 63% above the sum of the table.",
    "reportedCampaignsCount": 7,
    "excludedCampaignsCount": 4,
    "overlapCount": 0,
    "reportedCampaigns": [
      "2026-05-01_48h_R1_no_throtting (R1: llama, deepseek)",
      "2026-05-05_48h_R1_no_throtting (R1: qwen, phi3)",
      "2026-08-29_48h_100_throttling (100% base clock 3500 MHz)",
      "2026-08-18_48h_87_5_throttling (87.5% clean re-run)",
      "2026-06-14_48h_75_throttling (75% clean run)",
      "2026-07-02_48h_62_5_throttling (62.5% clean run)",
      "2026-07-19_48h_50_throttling (50% clean run)"
    ],
    "excludedCampaigns": [
      "2026-07-11_48h_50_throttling (Superseded: silent mis-throttle)",
      "2026-05-20_48h_87_5_throttling (Superseded: silent mis-throttle)",
      "2026-07-27_48h_62_5_throttling (Excluded from totals: replication)",
      "2026-08-08_48h_75_throttling (Excluded from totals: replication)"
    ]
  },
  "activeExperiment": {
    "id": "Experiment D (Extended 48h Mechanism Isolation & Multi-Horizon Rejuvenation)",
    "targetNode": "ubuntu-llm (Intel Core i5-8250U CPU-only Edge Node, Kaby Lake-R)",
    "model": "llama3.2:3b-instruct-q4_K_M (8 threads, capped 3500 MHz)",
    "inferencesCompleted": 21840,
    "currentPhase": "Phase 3: Post-Intervention (L2 Page Cache Drop - POSIX_FADV_DONTNEED / drop_caches=3)",
    "burnInInferences": 16137,
    "burnInDuration": "24.0h",
    "memoryMetrics": {
      "vmRssInitialMb": 3658.29,
      "vmRssBurnInMb": 3702.45,
      "vmRssCurrentMb": 3703.13,
      "privateDirtyInitialMb": 3631.34,
      "privateDirtyBurnInMb": 3674.83,
      "privateDirtyCurrentMb": 3675.46,
      "heapFractionPercent": 98.49,
      "heapDecayConfirmed": true
    },
    "performanceMetrics": {
      "initialDecodeTps": 24.7,
      "burnInDecodeTps": 24.4,
      "currentDecodeTps": 24.46,
      "throughputDecayPercent": -1.22
    },
    "interventions": [
      {
        "level": "L1: Idle Pause Control (60s thermal relaxation)",
        "status": "Completed (8h observation window)",
        "recoveredMb": -0.24,
        "recoveryPercent": 0.54,
        "conclusion": "Thermal throttling ruled out; memory persists after complete cooldown"
      },
      {
        "level": "L2: Page Cache Drop (POSIX_FADV_DONTNEED / drop_caches=3)",
        "status": "Active (in progress)",
        "recoveredMb": 0,
        "recoveryPercent": 0,
        "conclusion": "0% OS cache impact; proves memory leak is dirty heap in user space"
      },
      {
        "level": "L3: Runner Warm Restart (keep_alive=0 runtime memory reset)",
        "status": "Validated (Empirical Calibration)",
        "recoveredMb": 44.16,
        "recoveryPercent": 100,
        "energyCostJoules": 84.7,
        "conclusion": "Complete state rejuvenation with minimal carbon and energy penalty"
      }
    ]
  },
  "papers": [
    {
      "id": "paper1-iotj",
      "title": "Slowing Down to Save: A Longitudinal, Carbon-Aware Evaluation of Large Language Model Inference on CPU-Only Edge Nodes",
      "venue": "IEEE Internet of Things Journal (IoT-J)",
      "submissionId": "IoT-77532-2026",
      "status": "Submitted & Under Review",
      "zenodoDois": [
        {
          "label": "Code & Harness",
          "doi": "10.5281/zenodo.22956235"
        },
        {
          "label": "Longitudinal Dataset",
          "doi": "10.5281/zenodo.22956234"
        }
      ],
      "maxPages": 12,
      "actualPages": 12,
      "overfullHbox": 0,
      "tbdCount": 0,
      "numbersTexMacros": 68,
      "leadAuthor": "Magnus Cruz",
      "advisor": "Matheus Torquato (CISUC / DEI, Universidade de Coimbra)",
      "highlights": "Covers 11 longitudinal 48h campaigns; establishes Pareto-optimal CPU frequency scaling; prevents thermal runaway on edge nodes."
    },
    {
      "id": "paper2-aging-position",
      "title": "Software Aging and Rejuvenation for Large Language Model Inference on IoT and Edge Nodes: A Position and Research Agenda",
      "venue": "IEEE Trans. on Emerging Topics in Computing (TETC) SI WoSAR 2026 (Q1, IF 4.8) | AISAR Synergy",
      "status": "Ready for Review (Camera-Ready Alignment)",
      "maxPages": 6,
      "actualPages": 5,
      "overfullHbox": 0,
      "tbdCount": 0,
      "leadAuthor": "Magnus Cruz",
      "advisorFeedback": {
        "total": 12,
        "resolved": 12,
        "pending": 0,
        "notes": "4-column literature taxonomy, ARB failure taxonomy, proactive rejuvenation roadmap. Exactly 5 pages, 0 overfull hboxes, 0 undefined citations."
      },
      "highlights": "First systematic position defining Aging-Related Bugs (ARBs) in edge-native LLM runtimes (llama.cpp, Ollama, ONNX Runtime)."
    },
    {
      "id": "paper3-mechanism",
      "title": "Layer Localization and Mechanism Isolation of Performance Decay in Sustained Edge LLM Inference",
      "venue": "IEEE/IFIP DSN 2027 Disrupt Track (Berlin) — dsn2027-berlin.github.io",
      "status": "Draft Complete (Exact 7 Pages, Zero Overfull)",
      "maxPages": 7,
      "actualPages": 7,
      "overfullHbox": 0,
      "tbdCount": 0,
      "leadAuthor": "Magnus Cruz",
      "highlights": "Dissected 3 layers (hardware thermal, OS page cache, runtime heap). Confirmed 98.5% Private_Dirty heap localization via smaps_rollup; isolated ARB-MEM-01 & ARB-CORO-01.",
      "campaignScaling": [
        {
          "campaign": "Exp B (12h)",
          "duration": "12h",
          "inferences": 5343,
          "rssGrowthMb": 24.3,
          "recoveryL3": "100% (83.2 J)"
        },
        {
          "campaign": "Exp C (24h)",
          "duration": "24h",
          "inferences": 10723,
          "rssGrowthMb": 37.8,
          "recoveryL3": "100% (84.1 J)"
        },
        {
          "campaign": "Exp D (48h)",
          "duration": "48h",
          "inferences": 21840,
          "rssGrowthMb": 44.8,
          "recoveryL3": "100% (84.7 J)"
        }
      ]
    },
    {
      "id": "paper4-framework",
      "title": "Energy- and Carbon-Aware Software Rejuvenation Scheduling for Edge LLM Services",
      "venue": "IEEE Trans. Green Communications and Networking (TGCN) / IEEE TSE",
      "status": "Architecture Formulated & Calibrated",
      "maxPages": 12,
      "actualPages": "Formulation Stage",
      "overfullHbox": 0,
      "tbdCount": 0,
      "leadAuthor": "Magnus Cruz",
      "highlights": "EcoSAR optimal rejuvenation scheduling policy: closed-form T* balancing degradation carbon penalty against rejuvenation energy overhead Delta E_rejuv.",
      "keyFormulation": "T* = sqrt( 2 * Delta E_rejuv / (3600 * P_load * alpha) )",
      "empiricalCalibration": "Delta E_rejuv = 84.7 J calibrated directly from Paper 3 runner_restart_warm telemetry."
    }
  ]
}