{
  "pack_id": "jepa-world-state-and-action-simulator",
  "title": "JEPA world-state and action simulator",
  "version": "0.1.0",
  "summary": "A collection of nouns and verbs for action-conditioned latent world-state prediction, representation-space epistemic energy anomaly gating and counterfactual multi-step rollout planning.",
  "dependencies": [
    "configuration-grids-and-bounds",
    "evaluation-design",
    "wiring-and-conformance-checks"
  ],
  "boundaries": [
    "ActionConditionedStatePredictor projects observations into factored latent representations (leases, resource quotas, record digests, capability taints and invariant predicates) and flags invariant breaches before irreversible execution.",
    "EpistemicEnergyAnomalyGate computes representation-space surprise E(s_hat, s_obs) invariant to surface formatting jitter and triggers rollback or review whenever surprise exceeds grid bounds.",
    "CounterfactualRolloutPlanner ranks candidate multi-step trajectories strictly inside representation space without executing external side effects.",
    "Importing the pack writes nothing, enqueues nothing and installs no code."
  ],
  "nouns": [
    {
      "slug": "pack",
      "category": "noun.skill_pack",
      "role": "pack",
      "title": "A collection of nouns and verbs for action-conditioned latent world-state prediction, representation-space epistemic energy anomaly gating and counterfactual multi-step rollout planning",
      "value": {
        "lets_workers": "Encode environment observations into compact factored latent world states (leases, resource quotas, record digests, capability taints and invariant predicates), predict post-action states s_hat_{t+1} = transition(s_t, a_t) before irreversible execution, gate unexpected execution divergence via representation-space epistemic energy E(s_hat_{t+1}, s_obs_{t+1}) and rank K multi-step counterfactual rollouts by goal distance plus cumulative risk energy.",
        "flow": [
          {
            "verb": {
              "$verb": "jepa-world-state-and-action-simulator.plan-counterfactual-rollout-trajectories"
            },
            "when": "Simulate K candidate multi-step action sequences in representation space before irreversible execution and rank feasible trajectories by goal distance plus cumulative risk energy."
          },
          {
            "verb": {
              "$verb": "jepa-world-state-and-action-simulator.gate-step-by-epistemic-surprise-energy"
            },
            "when": "Compare predicted post-action world state s_hat_{t+1} against observed world state s_obs_{t+1} in representation space and trigger review or rollback when surprise exceeds grid bounds."
          },
          {
            "verb": {
              "$verb": "jepa-world-state-and-action-simulator.verify-world-state-simulator-receipt"
            },
            "when": "Independently verify pre-execution invariant rejection, surface-jitter invariance, anomaly rollback recall and ZeroHardcodedValuesAstGate conformance."
          }
        ],
        "guardrails": [
          {
            "$pin": "guardrail.simulate-invariants-before-irreversible-effects"
          },
          {
            "$pin": "guardrail.grid-governed-surprise-bounds-and-zero-magic-literals"
          }
        ],
        "tool": {
          "$pin": "tool.world-state-jepa"
        },
        "worked_example": "Multi-step counterfactual rollout and post-action surprise evaluation bundle recomputed in test_world_state_jepa.py: rejects unfenced/tainted commit plans pre-execution, selects plan-fenced-sanitized-clean (goal_distance_energy 0.0, total_objective_energy 0.3925), assigns 0.0 surprise energy under benign whitespace/timestamp jitter and triggers rollback on 100% (3/3) of injected lease-theft, phantom-CAS and quota-drain anomalies (max energy 9.18778)."
      },
      "relations": [
        [
          "requires_noun",
          "guardrail.simulate-invariants-before-irreversible-effects"
        ],
        [
          "requires_noun",
          "guardrail.grid-governed-surprise-bounds-and-zero-magic-literals"
        ],
        [
          "requires_noun",
          "spec.jepa-world-state-and-energy-grid"
        ],
        [
          "requires_noun",
          "rules.latent-transition-and-surprise-energy"
        ],
        [
          "requires_noun",
          "tool.world-state-jepa"
        ],
        [
          "requires_noun",
          "template.world-state-jepa-receipt"
        ]
      ]
    },
    {
      "slug": "guardrail.simulate-invariants-before-irreversible-effects",
      "category": "noun.skill_pack",
      "role": "guardrail",
      "title": "Guardrail: irreversible or state-mutating actions must pass factored world-state transition simulation before execution",
      "category_reason": "Blocks unfenced writes, capability taint leaks, compare-and-set version conflicts and quota exhaustion prior to execution.",
      "value": {
        "rule": "Reject any candidate trajectory whose predicted post-action state violates lease_fenced, quota_non_negative, taint_isolated or cas_version_monotonic invariants.",
        "how_it_is_checked": "ActionConditionedStatePredictor.predict_transition and CounterfactualRolloutPlanner.rank_candidates mark invariant-breaking trajectories infeasible before recommending any plan."
      }
    },
    {
      "slug": "guardrail.grid-governed-surprise-bounds-and-zero-magic-literals",
      "category": "noun.skill_pack",
      "role": "guardrail",
      "title": "Guardrail: epistemic surprise thresholds and rollout horizons resolve from NodeGrid with zero inline magic scalars",
      "category_reason": "Keeps rollback thresholds tunable and enforces mechanical AST conformance across simulator modules.",
      "value": {
        "rule": "Resolve review_energy_threshold, rollback_energy_threshold, max_rollout_horizon and risk_energy_weight from default_grid_spec.json and enforce ZeroHardcodedValuesAstGate.",
        "how_it_is_checked": "scan_directory_for_hardcoded_values and NodeGrid constraint tests run during make check."
      }
    },
    {
      "slug": "spec.jepa-world-state-and-energy-grid",
      "category": "noun.benchmark_suite",
      "role": "spec",
      "title": "Specification: ActionConditionedStatePredictor, EpistemicEnergyAnomalyGate and CounterfactualRolloutPlanner invariants",
      "category_reason": "Defines factored representation components, post-action energy decomposition and multi-step rollout objective.",
      "value": {
        "components": [
          "ActionConditionedStatePredictor",
          "EpistemicEnergyAnomalyGate",
          "CounterfactualRolloutPlanner"
        ],
        "factored_axes": [
          "leases",
          "resource_quotas",
          "record_digests",
          "capability_taints",
          "invariant_predicates"
        ],
        "default_max_rollout_horizon": 6,
        "default_review_energy_threshold": 0.2,
        "default_rollback_energy_threshold": 1.0,
        "default_risk_energy_weight": 1.0
      }
    },
    {
      "slug": "rules.latent-transition-and-surprise-energy",
      "category": "noun.reflex_procedure",
      "role": "rules",
      "title": "Deterministic factored latent transition, epistemic surprise energy and rollout ranking rules",
      "category_reason": "Zero-model representation-space transition and anomaly energy equations.",
      "value": {
        "transition_equation": "s_hat_{t+1} = transition(encode(obs_t), a_t)",
        "surprise_energy_equation": "E(s_hat, s_obs) = w_q * ||q_hat - q_obs||_2^2 + w_l * d_lease + w_d * d_digest + w_t * |taints_obs \\ taints_hat| + w_inv * broken_invariants + w_lat * ||z_hat - z_obs||_2^2",
        "rollout_objective": "J(a_{0:H-1}) = E_goal(s_hat_H, g) + lambda_risk * sum_{t=0}^{H-1} R(s_t, a_t, s_hat_{t+1})",
        "model_calls": 0
      }
    },
    {
      "slug": "tool.world-state-jepa",
      "category": "noun.code_node",
      "role": "tool_card",
      "title": "Tool card: standard-library JEPA world-state predictor, epistemic energy anomaly gate and counterfactual rollout planner",
      "category_reason": "Tested Python 3.10+ standard-library package pinned by path, file count and SHA-256.",
      "value": {
        "name": "JEPA world-state and action simulator",
        "code": {
          "$code": "jepa-world-state-and-action-simulator"
        },
        "runtime": "Python 3.10 or later, standard library only.",
        "commands": [
          "python -m world_state_jepa --bundle BUNDLE.json"
        ],
        "does": [
          "Implements ActionConditionedStatePredictor encoding raw observations into surface-noise-invariant FactoredWorldState embeddings and predicting post-action transitions.",
          "Implements EpistemicEnergyAnomalyGate computing representation-space surprise E(s_hat_{t+1}, s_obs_{t+1}) and gating execution into continue, require_review or trigger_rollback.",
          "Implements CounterfactualRolloutPlanner simulating K multi-step trajectories and ranking feasible minimum-energy plans."
        ],
        "does_not": [
          "Execute external side effects, reconstruct raw pixel/token noise or embed hardcoded numeric scalars in operational functions."
        ],
        "code_license": "MIT"
      },
      "relations": [
        [
          "verifies",
          "spec.jepa-world-state-and-energy-grid"
        ]
      ]
    },
    {
      "slug": "template.world-state-jepa-receipt",
      "category": "noun.scene_recipe",
      "role": "template",
      "title": "Template: counterfactual rollout ranking and epistemic surprise anomaly gate receipt",
      "value": {
        "describes": "a deterministic receipt recording counterfactual trajectory selection, goal/risk energies, benign surface-jitter invariance and post-action anomaly rollback recall",
        "schema": {
          "type": "object",
          "additionalProperties": false,
          "required": [
            "recommended_sequence_id",
            "feasible_sequence_count",
            "best_goal_distance_energy",
            "best_cumulative_risk_energy",
            "best_total_objective_energy",
            "clean_observation_energy",
            "clean_observation_decision",
            "anomaly_case_count",
            "anomaly_rollback_recall",
            "max_anomaly_energy"
          ],
          "properties": {
            "recommended_sequence_id": {
              "type": [
                "string",
                "null"
              ]
            },
            "feasible_sequence_count": {
              "type": "integer",
              "minimum": 0
            },
            "best_goal_distance_energy": {
              "type": "number",
              "minimum": 0.0
            },
            "best_cumulative_risk_energy": {
              "type": "number",
              "minimum": 0.0
            },
            "best_total_objective_energy": {
              "type": "number",
              "minimum": 0.0
            },
            "clean_observation_energy": {
              "type": "number",
              "minimum": 0.0
            },
            "clean_observation_decision": {
              "type": "string",
              "enum": [
                "continue",
                "require_review",
                "trigger_rollback"
              ]
            },
            "anomaly_case_count": {
              "type": "integer",
              "minimum": 1
            },
            "anomaly_rollback_recall": {
              "type": "number",
              "minimum": 0.0,
              "maximum": 1.0
            },
            "max_anomaly_energy": {
              "type": "number",
              "minimum": 0.0
            }
          }
        },
        "example": {
          "recommended_sequence_id": "plan-fenced-sanitized-clean",
          "feasible_sequence_count": 2,
          "best_goal_distance_energy": 0.0,
          "best_cumulative_risk_energy": 0.3925,
          "best_total_objective_energy": 0.3925,
          "clean_observation_energy": 0.0,
          "clean_observation_decision": "continue",
          "anomaly_case_count": 3,
          "anomaly_rollback_recall": 1.0,
          "max_anomaly_energy": 9.18778
        }
      }
    },
    {
      "slug": "example.world-state-jepa-bundle",
      "category": "noun.eval_run",
      "role": "example",
      "synthetic": true,
      "title": "Worked example input: multi-step candidate action sequences, surface-jittered clean observation and injected post-action anomalies",
      "category_reason": "Synthetic fixture recomputed by test_world_state_jepa.py.",
      "value": {
        "initial_observation": {
          "leases": {},
          "resource_quotas": {
            "compute_units": 0.9,
            "io_ops": 0.85
          },
          "record_digests": {
            "rec-alpha": {
              "version": 1,
              "semantic_payload": "alpha_state_v1"
            }
          },
          "capability_taints": [
            "untrusted_input"
          ],
          "surface_timestamp_ms": 1730000100
        },
        "goal": {
          "target_record_versions": {
            "rec-alpha": 2
          },
          "require_all_leases_released": true,
          "forbid_taints": [
            "untrusted_input",
            "unverified_draft"
          ],
          "min_quota_remaining": {
            "compute_units": 0.4,
            "io_ops": 0.4
          }
        },
        "candidate_sequences": [
          {
            "sequence_id": "plan-reckless-unfenced",
            "actions": [
              {
                "action_id": "act-unfenced-commit",
                "op": "commit_cas_write",
                "actor_id": "worker-01",
                "target_key": "rec-alpha",
                "expected_version": 1,
                "quota_deltas": {
                  "compute_units": -0.05,
                  "io_ops": -0.05
                }
              }
            ]
          },
          {
            "sequence_id": "plan-fenced-sanitized-clean",
            "actions": [
              {
                "action_id": "act-sanitize-01",
                "op": "sanitize_taints",
                "actor_id": "worker-01",
                "clears_taints": [
                  "untrusted_input"
                ],
                "quota_deltas": {
                  "compute_units": -0.05,
                  "io_ops": -0.02
                }
              },
              {
                "action_id": "act-lease-01",
                "op": "acquire_lease",
                "actor_id": "worker-01",
                "target_key": "rec-alpha",
                "quota_deltas": {
                  "compute_units": -0.02,
                  "io_ops": -0.03
                }
              },
              {
                "action_id": "act-stage-01",
                "op": "transform_record",
                "actor_id": "worker-01",
                "target_key": "rec-alpha",
                "quota_deltas": {
                  "compute_units": -0.08,
                  "io_ops": -0.05
                }
              },
              {
                "action_id": "act-commit-01",
                "op": "commit_cas_write",
                "actor_id": "worker-01",
                "target_key": "rec-alpha",
                "expected_version": 1,
                "irreversible": true,
                "quota_deltas": {
                  "compute_units": -0.05,
                  "io_ops": -0.05
                }
              },
              {
                "action_id": "act-release-01",
                "op": "release_lease",
                "actor_id": "worker-01",
                "target_key": "rec-alpha",
                "quota_deltas": {
                  "compute_units": -0.01,
                  "io_ops": -0.01
                }
              }
            ]
          },
          {
            "sequence_id": "plan-quota-heavy-dangling-lease",
            "actions": [
              {
                "action_id": "act-sanitize-02",
                "op": "sanitize_taints",
                "actor_id": "worker-01",
                "clears_taints": [
                  "untrusted_input"
                ],
                "quota_deltas": {
                  "compute_units": -0.25,
                  "io_ops": -0.2
                }
              },
              {
                "action_id": "act-lease-02",
                "op": "acquire_lease",
                "actor_id": "worker-01",
                "target_key": "rec-alpha",
                "quota_deltas": {
                  "compute_units": -0.1,
                  "io_ops": -0.1
                }
              },
              {
                "action_id": "act-commit-02",
                "op": "commit_cas_write",
                "actor_id": "worker-01",
                "target_key": "rec-alpha",
                "expected_version": 1,
                "irreversible": true,
                "quota_deltas": {
                  "compute_units": -0.2,
                  "io_ops": -0.2
                }
              }
            ]
          }
        ],
        "executed_step_action": {
          "action_id": "act-sanitize-01",
          "op": "sanitize_taints",
          "actor_id": "worker-01",
          "clears_taints": [
            "untrusted_input"
          ],
          "quota_deltas": {
            "compute_units": -0.05,
            "io_ops": -0.02
          }
        },
        "clean_post_observation": {
          "leases": {},
          "resource_quotas": {
            "compute_units": 0.85,
            "io_ops": 0.83
          },
          "record_digests": {
            "rec-alpha": {
              "version": 1,
              "semantic_payload": "   alpha_state_v1  \n"
            }
          },
          "capability_taints": [],
          "surface_timestamp_ms": 1730000999,
          "surface_trace_noise": "benign-formatting-jitter"
        },
        "anomaly_observations": [
          {
            "leases": {
              "rec-alpha": {
                "holder": "intruder-worker-99",
                "epoch": 4
              }
            },
            "resource_quotas": {
              "compute_units": 0.85,
              "io_ops": 0.83
            },
            "record_digests": {
              "rec-alpha": {
                "version": 1,
                "semantic_payload": "alpha_state_v1"
              }
            },
            "capability_taints": []
          },
          {
            "leases": {},
            "resource_quotas": {
              "compute_units": 0.85,
              "io_ops": 0.83
            },
            "record_digests": {
              "rec-alpha": {
                "version": 3,
                "semantic_payload": "concurrent_mutation_v3"
              }
            },
            "capability_taints": []
          },
          {
            "leases": {},
            "resource_quotas": {
              "compute_units": -0.1,
              "io_ops": 0.1
            },
            "record_digests": {
              "rec-alpha": {
                "version": 1,
                "semantic_payload": "alpha_state_v1"
              }
            },
            "capability_taints": [
              "untrusted_input"
            ]
          }
        ],
        "shows": "Counterfactual world-state rollout ranking, surface-jitter invariance and representation-space anomaly rollback detection.",
        "source": "Synthetic. Written for this pack."
      }
    },
    {
      "slug": "example.world-state-jepa-receipt",
      "category": "noun.eval_run",
      "role": "example",
      "synthetic": true,
      "title": "Worked example output: recomputed counterfactual rollout and epistemic surprise receipt",
      "category_reason": "Synthetic receipt verified by test_world_state_jepa.py.",
      "value": {
        "conforms_to": {
          "$pin": "template.world-state-jepa-receipt"
        },
        "instance": {
          "recommended_sequence_id": "plan-fenced-sanitized-clean",
          "feasible_sequence_count": 2,
          "best_goal_distance_energy": 0.0,
          "best_cumulative_risk_energy": 0.3925,
          "best_total_objective_energy": 0.3925,
          "clean_observation_energy": 0.0,
          "clean_observation_decision": "continue",
          "anomaly_case_count": 3,
          "anomaly_rollback_recall": 1.0,
          "max_anomaly_energy": 9.18778
        },
        "shows": "Recomputes trajectory ranking (rejecting unfenced tainted commits), 0.0 surprise under benign whitespace/timestamp jitter and 1.0 rollback recall across injected state anomalies.",
        "source": "Synthetic. Recomputed directly from example.world-state-jepa-bundle."
      },
      "relations": [
        [
          "derives_from",
          "example.world-state-jepa-bundle"
        ],
        [
          "verifies",
          "template.world-state-jepa-receipt"
        ]
      ]
    }
  ],
  "verbs": [
    {
      "slug": "plan-counterfactual-rollout-trajectories",
      "kind": "verb.probe_source",
      "title": "Simulate K candidate multi-step action sequences in representation space and rank feasible plans",
      "claimant": "agent",
      "model_use": "none",
      "summary": "Encode initial environment observations into factored world states, predict multi-step transitions for K candidate sequences and rank invariant-preserving plans by goal distance plus cumulative risk energy.",
      "outcome": "Ranked counterfactual rollout table and minimum-energy feasible sequence recommendation.",
      "ready_outcome": "rollout_trajectories_ranked",
      "pinned": [
        "spec.jepa-world-state-and-energy-grid",
        "rules.latent-transition-and-surprise-energy",
        "guardrail.simulate-invariants-before-irreversible-effects",
        "tool.world-state-jepa"
      ],
      "slots": [
        {
          "name": "rollout_bundle",
          "category": "noun.eval_run",
          "description": "Pin of initial observation, target goal state and K candidate action sequences."
        }
      ],
      "produces": [
        {
          "name": "rollout_plan_receipt",
          "template": "template.world-state-jepa-receipt"
        }
      ],
      "steps": [
        "Encode raw environment observations into FactoredWorldState embeddings discarding surface timestamp and formatting noise.",
        "Simulate each candidate sequence through ActionConditionedStatePredictor and sort feasible plans ahead of invariant-violating plans by goal distance plus risk energy."
      ],
      "acceptance": [
        {
          "id": "infeasible-plans-fenced-pre-execution",
          "check": "Trajectories that violate lease fencing, capability taint isolation, CAS monotonicity or non-negative quotas are marked infeasible and never recommended.",
          "method": "deterministic"
        }
      ]
    },
    {
      "slug": "gate-step-by-epistemic-surprise-energy",
      "kind": "verb.generate_seed",
      "title": "Evaluate post-action representation-space surprise energy and trigger review or rollback on divergence",
      "claimant": "agent",
      "model_use": "none",
      "summary": "Compute factored and latent epistemic energy E(s_hat_{t+1}, s_obs_{t+1}) between predicted and observed post-action world states and gate execution against grid review and rollback thresholds.",
      "outcome": "Epistemic surprise energy breakdown and continue, require_review or trigger_rollback gate verdict.",
      "ready_outcome": "epistemic_surprise_gated",
      "pinned": [
        "spec.jepa-world-state-and-energy-grid",
        "rules.latent-transition-and-surprise-energy",
        "guardrail.grid-governed-surprise-bounds-and-zero-magic-literals",
        "tool.world-state-jepa"
      ],
      "slots": [
        {
          "name": "step_observation_bundle",
          "category": "noun.eval_run",
          "description": "Pin of executed action and observed post-action environment states."
        }
      ],
      "produces": [
        {
          "name": "surprise_gate_receipt",
          "template": "template.world-state-jepa-receipt"
        }
      ],
      "steps": [
        "Predict post-action world state s_hat_{t+1} and encode observed state s_obs_{t+1} into the shared representation space.",
        "Evaluate weighted quota, lease, digest, taint, invariant and latent L2 surprise energy against grid thresholds."
      ],
      "acceptance": [
        {
          "id": "surface-jitter-invariance-and-anomaly-recall",
          "check": "Benign surface formatting jitter produces zero surprise energy while stolen leases, phantom CAS mutations and unpredicted taints trigger rollback.",
          "method": "deterministic"
        }
      ],
      "follows": [
        "jepa-world-state-and-action-simulator.plan-counterfactual-rollout-trajectories"
      ]
    },
    {
      "slug": "verify-world-state-simulator-receipt",
      "kind": "verb.verify_gate",
      "title": "Independently verify counterfactual rollout ranking, anomaly rollback recall and AST magic-literal gates",
      "claimant": "agent",
      "model_use": "none",
      "summary": "Recompute the worked rollout and anomaly receipt from input observations, verify schema conformance and run ZeroHardcodedValuesAstGate across world_state_jepa.",
      "outcome": "Independent deterministic verification receipt.",
      "ready_outcome": "world_state_simulator_verified",
      "review": "distinct_reviewer",
      "pinned": [
        "spec.jepa-world-state-and-energy-grid",
        "guardrail.simulate-invariants-before-irreversible-effects",
        "guardrail.grid-governed-surprise-bounds-and-zero-magic-literals",
        "tool.world-state-jepa",
        "template.world-state-jepa-receipt"
      ],
      "slots": [
        {
          "name": "simulator_eval_bundle",
          "category": "noun.eval_run",
          "description": "Pin of world-state simulator fixture bundle and candidate receipt."
        }
      ],
      "produces": [
        {
          "name": "verified_simulator_receipt",
          "template": "template.world-state-jepa-receipt"
        }
      ],
      "steps": [
        "Run world_state_jepa on the fixture bundle and compare every output field against template.world-state-jepa-receipt.",
        "Run scan_directory_for_hardcoded_values across world_state_jepa and confirm zero AST magic-literal violations."
      ],
      "acceptance": [
        {
          "id": "ast-conformance-and-receipt-reproduction",
          "check": "scan_directory_for_hardcoded_values returns an empty list and run_bundle reproduces example.world-state-jepa-receipt identically.",
          "method": "deterministic"
        }
      ],
      "follows": [
        "jepa-world-state-and-action-simulator.gate-step-by-epistemic-surprise-energy"
      ]
    }
  ]
}