{
  "ledger": {
    "schema": "intrikata-topology.singularity-finite-proxy-ledger/v1",
    "ledger_id": "public-proxy:2026-08-02",
    "as_of": "2026-08-02",
    "sources": [
      {
        "id": "metr-time-horizons-2025",
        "title": "Measuring AI Ability to Complete Long Tasks",
        "url": "https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/",
        "published_on": "2025-03-19",
        "source_kind": "official-evaluation",
        "version": "time-horizon-1.0"
      },
      {
        "id": "rebench-v2",
        "title": "RE-Bench: Evaluating frontier AI R&D capabilities of language model agents against human experts",
        "url": "https://arxiv.org/abs/2411.15114",
        "published_on": "2025-05-27",
        "source_kind": "primary-research",
        "version": "arxiv:2411.15114v2"
      },
      {
        "id": "anthropic-economic-index-2025-09",
        "title": "Anthropic Economic Index: Uneven geographic and enterprise AI adoption",
        "url": "https://www.anthropic.com/research/anthropic-economic-index-september-2025-report",
        "published_on": "2025-09-15",
        "source_kind": "official-dataset",
        "version": "september-2025"
      },
      {
        "id": "shade-arena-control-v1",
        "title": "Evaluating Control Protocols for Untrusted AI Agents",
        "url": "https://arxiv.org/abs/2511.02997",
        "published_on": "2025-11-04",
        "source_kind": "primary-research",
        "version": "arxiv:2511.02997v1"
      },
      {
        "id": "aisi-async-control-2025",
        "title": "Async control: Stress-testing asynchronous control measures for LLM agents",
        "url": "https://www.aisi.gov.uk/research/async-control-stress-testing-asynchronous-control-measures-for-llm-agents",
        "published_on": "2025-12-15",
        "source_kind": "official-evaluation",
        "version": "2025-12-15"
      },
      {
        "id": "aisi-mcp-tools-2026",
        "title": "How are AI agents used? Evidence from 177,000 MCP tools",
        "url": "https://arxiv.org/abs/2603.23802",
        "published_on": "2026-03-25",
        "source_kind": "primary-research",
        "version": "arxiv:2603.23802v1"
      },
      {
        "id": "cl-bench-2026",
        "title": "Continual Learning Bench: Evaluating Frontier AI Systems in Real-World Stateful Environments",
        "url": "https://arxiv.org/abs/2606.05661",
        "published_on": "2026-06-04",
        "source_kind": "primary-research",
        "version": "arxiv:2606.05661v1"
      }
    ],
    "skills": [
      {
        "id": "skill:ai-rd-experimentation",
        "label": "AI R&D experimentation",
        "parent_id": null
      },
      {
        "id": "skill:long-horizon-execution",
        "label": "Long-horizon task execution",
        "parent_id": null
      },
      {
        "id": "skill:adversarial-control",
        "label": "Adversarial control",
        "parent_id": null
      },
      {
        "id": "skill:control-monitoring",
        "label": "Control monitoring",
        "parent_id": "skill:adversarial-control"
      },
      {
        "id": "skill:enterprise-task-automation",
        "label": "Enterprise task automation",
        "parent_id": null
      },
      {
        "id": "skill:agent-tooling",
        "label": "Agent tooling",
        "parent_id": null
      },
      {
        "id": "skill:continual-learning",
        "label": "Continual learning",
        "parent_id": null
      },
      {
        "id": "skill:economic-coordination",
        "label": "Economic coordination",
        "parent_id": null
      }
    ],
    "observations": [
      {
        "id": "obs:metr-horizon-trend",
        "kind": "frontier_trend",
        "source_id": "metr-time-horizons-2025",
        "observed_start": "2019-01-01",
        "observed_end": "2025-11-30",
        "skill_ids": [
          "skill:long-horizon-execution"
        ],
        "value": {
          "metric": "time_horizon_doubling_period",
          "low": 7.0,
          "high": 7.0,
          "unit": "months",
          "causal_attribution": "not-identified"
        }
      },
      {
        "id": "obs:rebench-two-hour",
        "kind": "rd_throughput",
        "source_id": "rebench-v2",
        "observed_start": "2024-11-22",
        "observed_end": "2025-05-27",
        "skill_ids": [
          "skill:ai-rd-experimentation",
          "skill:long-horizon-execution"
        ],
        "value": {
          "benchmark": "RE-Bench",
          "horizon_hours": 2.0,
          "ai_human_score_ratio_low": 4.0,
          "ai_human_score_ratio_high": 4.0,
          "iteration_rate_ratio_low": 10.0,
          "iteration_rate_ratio_high": null,
          "causal_attribution": "not-identified"
        }
      },
      {
        "id": "obs:rebench-thirty-two-hour",
        "kind": "rd_throughput",
        "source_id": "rebench-v2",
        "observed_start": "2024-11-22",
        "observed_end": "2025-05-27",
        "skill_ids": [
          "skill:ai-rd-experimentation",
          "skill:long-horizon-execution"
        ],
        "value": {
          "benchmark": "RE-Bench",
          "horizon_hours": 32.0,
          "ai_human_score_ratio_low": 0.5,
          "ai_human_score_ratio_high": 0.5,
          "iteration_rate_ratio_low": null,
          "iteration_rate_ratio_high": null,
          "causal_attribution": "not-identified"
        }
      },
      {
        "id": "obs:anthropic-api-task-sample",
        "kind": "population_activity",
        "source_id": "anthropic-economic-index-2025-09",
        "observed_start": "2025-08-01",
        "observed_end": "2025-08-31",
        "skill_ids": [
          "skill:enterprise-task-automation",
          "skill:ai-rd-experimentation"
        ],
        "value": {
          "measure_kind": "task_events",
          "observed_count": 1000000,
          "automation_share_low": 0.77,
          "automation_share_high": 0.77,
          "coverage_fraction_low": null,
          "coverage_fraction_high": null,
          "human_minutes_per_event_low": null,
          "human_minutes_per_event_high": null,
          "direct_agent_count": false
        }
      },
      {
        "id": "obs:shade-resampling-default",
        "kind": "control_eval",
        "source_id": "shade-arena-control-v1",
        "observed_start": "2025-11-04",
        "observed_end": "2025-11-04",
        "skill_ids": [
          "skill:adversarial-control",
          "skill:control-monitoring"
        ],
        "value": {
          "protocol_id": "shade:resampling-for-incrimination",
          "environment": "SHADE-Arena",
          "adversary_class": "default",
          "safety_rate": 0.96,
          "usefulness_rate": null,
          "false_negative_rate": null,
          "false_positive_rate": null,
          "safe_trials": null,
          "total_trials": null,
          "representative_of_deployment": false
        }
      },
      {
        "id": "obs:shade-resampling-adaptive",
        "kind": "control_eval",
        "source_id": "shade-arena-control-v1",
        "observed_start": "2025-11-04",
        "observed_end": "2025-11-04",
        "skill_ids": [
          "skill:adversarial-control",
          "skill:control-monitoring"
        ],
        "value": {
          "protocol_id": "shade:resampling-for-incrimination",
          "environment": "SHADE-Arena",
          "adversary_class": "adaptive-protocol-aware",
          "safety_rate": 0.17,
          "usefulness_rate": null,
          "false_negative_rate": null,
          "false_positive_rate": null,
          "safe_trials": null,
          "total_trials": null,
          "representative_of_deployment": false
        }
      },
      {
        "id": "obs:aisi-async-monitor",
        "kind": "control_eval",
        "source_id": "aisi-async-control-2025",
        "observed_start": "2025-12-15",
        "observed_end": "2025-12-15",
        "skill_ids": [
          "skill:adversarial-control",
          "skill:control-monitoring"
        ],
        "value": {
          "protocol_id": "aisi:async-ensemble-monitor",
          "environment": "held-out-software-engineering-environment",
          "adversary_class": "red-team-sabotage",
          "safety_rate": null,
          "usefulness_rate": null,
          "false_negative_rate": 0.06,
          "false_positive_rate": 0.01,
          "safe_trials": null,
          "total_trials": null,
          "representative_of_deployment": false
        }
      },
      {
        "id": "obs:aisi-mcp-tools",
        "kind": "population_activity",
        "source_id": "aisi-mcp-tools-2026",
        "observed_start": "2024-11-01",
        "observed_end": "2026-02-28",
        "skill_ids": [
          "skill:agent-tooling",
          "skill:enterprise-task-automation",
          "skill:economic-coordination"
        ],
        "value": {
          "measure_kind": "tools_published",
          "observed_count": 177436,
          "automation_share_low": null,
          "automation_share_high": null,
          "coverage_fraction_low": null,
          "coverage_fraction_high": null,
          "human_minutes_per_event_low": null,
          "human_minutes_per_event_high": null,
          "direct_agent_count": false
        }
      },
      {
        "id": "obs:aisi-mcp-downloads",
        "kind": "population_activity",
        "source_id": "aisi-mcp-tools-2026",
        "observed_start": "2024-11-01",
        "observed_end": "2026-02-28",
        "skill_ids": [
          "skill:agent-tooling",
          "skill:enterprise-task-automation"
        ],
        "value": {
          "measure_kind": "tool_downloads",
          "observed_count": 14000000,
          "automation_share_low": null,
          "automation_share_high": null,
          "coverage_fraction_low": null,
          "coverage_fraction_high": null,
          "human_minutes_per_event_low": null,
          "human_minutes_per_event_high": null,
          "direct_agent_count": false
        }
      },
      {
        "id": "obs:cl-bench-domains",
        "kind": "skill_benchmark",
        "source_id": "cl-bench-2026",
        "observed_start": "2026-06-04",
        "observed_end": "2026-06-04",
        "skill_ids": [
          "skill:continual-learning",
          "skill:long-horizon-execution"
        ],
        "value": {
          "benchmark": "CL-Bench",
          "score_low": 6.0,
          "score_high": 6.0,
          "unit": "expert-validated-domains"
        }
      }
    ],
    "policy": {
      "economic_scale_fte_threshold": null,
      "control_safety_threshold": null,
      "control_usefulness_threshold": null
    },
    "claim_boundary": {
      "finite_proxy_arbiter": true,
      "source_authenticity_verified": false,
      "causal_recursive_loop_gain_established": false,
      "unique_economic_agent_population_established": false,
      "deployment_control_established": false,
      "frontier_capability_implemented": false,
      "direct_readiness_value": null,
      "readiness_promotion_allowed": false,
      "singularity_probability_claim": false,
      "occurrence_claim": false,
      "progress_claim": false
    }
  },
  "assessment": {
    "schema": "intrikata-topology.singularity-finite-proxy-assessment/v1",
    "structurally_conformant": true,
    "errors": [],
    "ledger_sha256": "c70211e31a4117df1ac72a9bd65e2d517585aba12a97744802bb3d55b0e41f03",
    "assessment_sha256": "0b44e7d9bd7db29fa2e541cdc7bc17f69d981536e1dedcbbb37adbbc9003e34f",
    "calendar": {
      "earliest_observation": "2019-01-01",
      "latest_observation": "2026-06-04",
      "calendar_depth_days": 2711,
      "calendar_depth_years": 7.4225,
      "unique_observation_end_dates": 7,
      "median_cadence_days": 70.0,
      "maximum_gap_days": 96,
      "staleness_days": 59
    },
    "skills": {
      "declared_skill_count": 8,
      "observed_skill_count": 8,
      "coverage_fraction": 1.0,
      "effective_skill_breadth": 7.31547,
      "unobserved_skill_ids": [],
      "skill_rows": [
        {
          "skill_id": "skill:ai-rd-experimentation",
          "observation_count": 3,
          "observation_kinds": [
            "population_activity",
            "rd_throughput"
          ]
        },
        {
          "skill_id": "skill:long-horizon-execution",
          "observation_count": 4,
          "observation_kinds": [
            "frontier_trend",
            "rd_throughput",
            "skill_benchmark"
          ]
        },
        {
          "skill_id": "skill:adversarial-control",
          "observation_count": 3,
          "observation_kinds": [
            "control_eval"
          ]
        },
        {
          "skill_id": "skill:control-monitoring",
          "observation_count": 3,
          "observation_kinds": [
            "control_eval"
          ]
        },
        {
          "skill_id": "skill:enterprise-task-automation",
          "observation_count": 3,
          "observation_kinds": [
            "population_activity"
          ]
        },
        {
          "skill_id": "skill:agent-tooling",
          "observation_count": 2,
          "observation_kinds": [
            "population_activity"
          ]
        },
        {
          "skill_id": "skill:continual-learning",
          "observation_count": 1,
          "observation_kinds": [
            "skill_benchmark"
          ]
        },
        {
          "skill_id": "skill:economic-coordination",
          "observation_count": 1,
          "observation_kinds": [
            "population_activity"
          ]
        }
      ]
    },
    "loop_gain": {
      "frontier_calendar_trends": [
        {
          "observation_id": "obs:metr-horizon-trend",
          "metric": "time_horizon_doubling_period",
          "low": 7.0,
          "high": 7.0,
          "unit": "months",
          "causal_attribution": "not-identified",
          "calendar_doublings_per_year_low": 1.714286,
          "calendar_doublings_per_year_high": 1.714286
        }
      ],
      "rd_throughput_proxies": [
        {
          "observation_id": "obs:rebench-two-hour",
          "benchmark": "RE-Bench",
          "horizon_hours": 2.0,
          "ai_human_score_ratio_low": 4.0,
          "ai_human_score_ratio_high": 4.0,
          "iteration_rate_ratio_low": 10.0,
          "iteration_rate_ratio_high": null,
          "causal_attribution": "not-identified"
        },
        {
          "observation_id": "obs:rebench-thirty-two-hour",
          "benchmark": "RE-Bench",
          "horizon_hours": 32.0,
          "ai_human_score_ratio_low": 0.5,
          "ai_human_score_ratio_high": 0.5,
          "iteration_rate_ratio_low": null,
          "iteration_rate_ratio_high": null,
          "causal_attribution": "not-identified"
        }
      ],
      "closed_loop_gain": {
        "status": "unavailable",
        "cycle_count": 0,
        "required_cycle_count": 3,
        "loop_gain_low": null,
        "loop_gain_high": null,
        "supercritical_status": "not-identifiable",
        "reason": "At least three contiguous causally identified recursive R&D cycles are required."
      },
      "non_equivalence": "Frontier benchmark growth, short-horizon throughput, and iteration speed are not causal recursive loop gain."
    },
    "control": {
      "protocols": [
        {
          "protocol_id": "aisi:async-ensemble-monitor",
          "evaluation_count": 1,
          "environment_count": 1,
          "adversary_classes": [
            "red-team-sabotage"
          ],
          "observed_safety_min": null,
          "observed_safety_max": null,
          "adaptive_degradation": null,
          "deployment_representative": false,
          "policy_status": "not-evaluable",
          "evaluations": [
            {
              "observation_id": "obs:aisi-async-monitor",
              "environment": "held-out-software-engineering-environment",
              "adversary_class": "red-team-sabotage",
              "safety_rate": null,
              "usefulness_rate": null,
              "false_negative_rate": 0.06,
              "false_positive_rate": 0.01,
              "safety_interval": null,
              "representative_of_deployment": false
            }
          ]
        },
        {
          "protocol_id": "shade:resampling-for-incrimination",
          "evaluation_count": 2,
          "environment_count": 1,
          "adversary_classes": [
            "adaptive-protocol-aware",
            "default"
          ],
          "observed_safety_min": 0.17,
          "observed_safety_max": 0.96,
          "adaptive_degradation": 0.79,
          "deployment_representative": false,
          "policy_status": "not-evaluable",
          "evaluations": [
            {
              "observation_id": "obs:shade-resampling-default",
              "environment": "SHADE-Arena",
              "adversary_class": "default",
              "safety_rate": 0.96,
              "usefulness_rate": null,
              "false_negative_rate": null,
              "false_positive_rate": null,
              "safety_interval": null,
              "representative_of_deployment": false
            },
            {
              "observation_id": "obs:shade-resampling-adaptive",
              "environment": "SHADE-Arena",
              "adversary_class": "adaptive-protocol-aware",
              "safety_rate": 0.17,
              "usefulness_rate": null,
              "false_negative_rate": null,
              "false_positive_rate": null,
              "safety_interval": null,
              "representative_of_deployment": false
            }
          ]
        }
      ],
      "deployment_control_status": "not-established",
      "reason": "Public control evaluations cover selected environments and attacks; none is declared representative of deployment."
    },
    "economic_agent_population": {
      "activity_rows": [
        {
          "observation_id": "obs:anthropic-api-task-sample",
          "measure_kind": "task_events",
          "observed_count": 1000000,
          "sample_automated_events_low": 770000.0,
          "sample_automated_events_high": 770000.0,
          "fte_equivalent": null,
          "direct_agent_count": false
        },
        {
          "observation_id": "obs:aisi-mcp-tools",
          "measure_kind": "tools_published",
          "observed_count": 177436,
          "sample_automated_events_low": null,
          "sample_automated_events_high": null,
          "fte_equivalent": null,
          "direct_agent_count": false
        },
        {
          "observation_id": "obs:aisi-mcp-downloads",
          "measure_kind": "tool_downloads",
          "observed_count": 14000000,
          "sample_automated_events_low": null,
          "sample_automated_events_high": null,
          "fte_equivalent": null,
          "direct_agent_count": false
        }
      ],
      "unique_agent_population": null,
      "best_nonadditive_fte_interval": null,
      "economic_scale_threshold": null,
      "economic_scale_status": "not-evaluable",
      "non_equivalence": "Transcripts, task events, tools, downloads, and FTE-equivalents are not counts of unique or concurrent agent instances."
    },
    "claim_boundary": {
      "finite_proxy_arbiter": true,
      "source_authenticity_verified": false,
      "causal_recursive_loop_gain_established": false,
      "unique_economic_agent_population_established": false,
      "deployment_control_established": false,
      "frontier_capability_implemented": false,
      "direct_readiness_value": null,
      "readiness_promotion_allowed": false,
      "singularity_probability_claim": false,
      "occurrence_claim": false,
      "progress_claim": false
    }
  },
  "protocol_sha256": "0287d593b12285e01b603fab7a40b14130ae2e2cd0c307743b86bf4b94480b2c"
}