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 "param": "predation_enabled",
 "change": "False \u2192 False",
 "reason": "initialized",
 "success": true,
 "trigger_metrics": {
 "strength": 1.0,
 "stability": 0.0
 },
 "expected_impact": "Improve highlander performance",
 "actual_impact": "Performance: 100.00%"
 }
 ],
 "system_id": "reality_sim",
 "total_atoms": 34,
 "domains": {
 "neural": 4,
 "learning": 6,
 "evolution": 6,
 "simulation": 4,
 "language": 6,
 "illumination": 3,
 "highlander": 5
 },
 "avg_stability": 0.074401240485955,
 "avg_strength": 1.0
 },
 "tuning_interval_frames": 5,
 "min_confidence_threshold": 0.6
 },
 "neural_trainer": {
 "autotune_metrics": {
 "avg_loss": 0.0,
 "min_loss": Infinity,
 "max_loss": 0.0,
 "loss_variance": 0.0,
 "loss_history": [],
 "organisms_trained_total": 0,
 "training_steps_completed": 0,
 "improvement_rate": 0.0,
 "language_loss_total": 0.0,
 "rl_loss_total": 0.0,
 "concept_loss_total": 0.0,
 "avg_training_time_ms": 0.0,
 "buffer_size": 43692,
 "loss_history_size": 0,
 "window_size": 50,
 "atomic_config_connected": true
 }
 },
 "ml_analyzer": {
 "autotune_metrics": {
 "cluster_count_history": [],
 "anomaly_ratio_history": [],
 "silhouette_score_history": [],
 "avg_cluster_count": 0.0,
 "avg_anomaly_ratio": 0.0,
 "avg_silhouette_score": 0.0,
 "cluster_stability": 0.0,
 "analysis_count": 0,
 "buffer_size": 0,
 "window_size": 20,
 "atomic_config_connected": true
 }
 },
 "explorer": {
 "phase": "genesis",
 "vp_calculations": 22,
 "sovereign_ids_count": 604,
 "breath_cycle": 26,
 "breath_depth": 0.9814544085094277
 },
 "djinn_kernel": {
 "violation_pressure": 0.709369879898522,
 "vp_classification": "VP2",
 "vp_calculations": 45,
 "trait_count": 4,
 "tape_cells": 45,
 "tape_position": 45
 },
 "language": {
 "vocab_size": 10000,
 "word_count": 10000,
 "organism_word_assignments": 3500263,
 "organisms_with_words": 5440,
 "language_anchors": 33568,
 "total_associations": 3489402
 },
 "commerce": {
 "holders": 200,
 "total_semantic_credit": 2311.1956,
 "sc_gini": 0.442,
 "sc_top1_share": 0.0258,
 "sc_top4_share": 0.0946,
 "market_vwap": 0.8705776633986222,
 "market_volume": 1993.778778,
 "market_spread": null,
 "open_bids": 0,
 "open_asks": 100,
 "total_trades": 1048,
 "commerce_balance_total": 2010.0,
 "note": "live parallel power axis; killing stays apex. See commerce_substrate.py / commerce_market.py."
 },
 "phase_sync": {
 "network": {
 "organism_count": 214,
 "collapse_proximity": 0.4427825368703331,
 "is_collapsed": false,
 "clustering": 0.08271245208229518,
 "modularity": 0.4042599190254466,
 "path_length": 3.1165466841186737
 },
 "explorer": {
 "sovereign_ids": 604,
 "genesis_proximity": 0.8320000000000001,
 "is_ready": false,
 "phase": "genesis",
 "vp_calculations": 22,
 "stability_score": 1.0,
 "breath_cycles": 26
 },
 "synchronization": {
 "aligned": true,
 "network_proximity": 0.4427825368703331,
 "explorer_proximity": 0.8320000000000001,
 "proximity_difference": 0.389217463129667
 }
 },
 "exploration_tracking": {
 "exploration_to_precision_ratio": 10.0,
 "reality_sim_explorations": 214,
 "explorer_explorations": 22,
 "target_ratio": "500:50",
 "current_ratio": "214:22",
 "ratio_maintained": false,
 "progress_to_transition": 0.4427825368703331
 },
 "unified_health": {
 "overall_health": 0.5,
 "reality_sim_health": 0.5,
 "health_score": 0.5,
 "state": "unknown",
 "components": {},
 "explorer_health": 1.0,
 "djinn_kernel_health": 0.82,
 "integration_health": 0.610782536870333,
 "phase_alignment_health": 0.610782536870333
 },
 "transition_status": {
 "reality_sim_ready": false,
 "explorer_ready": false,
 "djinn_kernel_ready": false,
 "unified_transition_triggered": false,
 "estimated_time_to_transition": "~inf cycles"
 },
 "config": {
 "_case_study": "STUDY 6: BOOM-BUST WAVES - Grounded learning with periodic Highlander concentration",
 "_hypothesis": "Organisms that build foundations (6\u2192200\u21925000\u21927500\u219210000 word capacity, with smaller advancement exam windows) before facing Highlander waves develop more coherent intelligence than those thrown into instant chaos",
 "_hardware_profile_help": "Set to 'beast', 'workstation', 'standard', 'laptop', 'potato', or 'cpu_only' to override auto-detection. null = auto-detect",
 "language": {
 "_help": "Grounded Language Mode: Organisms learn language through action-outcome associations before semantic understanding",
 "mode": "grounded",
 "_mode_options": "grounded | semantic | hybrid",
 "grounded": {
 "enabled": true,
 "mastery_gating": true,
 "vocabulary_masking": "soft",
 "_vocabulary_masking_options": "soft | hard | none",
 "initial_mastery_level": 0,
 "_initial_mastery_level_help": "0=6 words, 1=200 cap, 2=5000 protected strategist cap, 3=7500 protected transition cap, 4=10000 semantic graduation",
 "mastery_vocab_sizes": [
 6,
 200,
 5000,
 7500,
 10000
 ],
 "_mastery_vocab_sizes_help": "Per-level vocabulary ownership capacity. L2/L3 remain protected from Highlander because direct battles require L4+.",
 "mastery_advancement_vocab_windows": [
 6,
 200,
 1000,
 5000
 ],
 "_mastery_advancement_vocab_windows_help": "Per-level advancement exam window. Keeps L2 able to know thousands of words without requiring thousands of active/deep words to choose L3.",
 "mastery_advancement_ratio": 0.3,
 "mastery_depth_ratio": 0.15,
 "mastery_min_experiences": [
 10,
 40,
 120,
 300
 ],
 "always_allow_tokens": [
 ".",
 ",",
 "!",
 "?",
 "and",
 "the",
 "a",
 "is",
 "to",
 "of",
 "in"
 ]
 },
 "semantic": {
 "enabled": false,
 "knowledge_web_bootstrap": false,
 "semantic_reward_at_level_4": true,
 "semantic_reward_weight": 0.25
 },
 "action_hints": {
 "enabled": true,
 "bias_strength": 0.3,
 "expectation_window_seconds": 10.0
 },
 "organism_learning_berth": {
 "_help": "Bounded lane for learning from human-selected suggestions and operation-sequence prompts. Inside-game only; no outside command authority.",
 "enabled": true,
 "space_window_seconds": 3600,
 "config_window_seconds": 1200,
 "max_interaction_context_words": 64,
 "human_choice_reward_bonus": 0.08,
 "operation_sequence_reward_bonus": 0.05,
 "allowed_operation_axes": [
 "inside",
 "around",
 "above",
 "below",
 "between",
 "through"
 ],
 "inside_boundary": "inside_game_only",
 "forbidden_outside_actions": [
 "deployment",
 "money",
 "identity",
 "safety_systems",
 "authority_override"
 ]
 }
 },
 "agency": {
 "confidence_threshold": 0.0001,
 "decision_precision": 1e-05,
 "initial_mode": "manual_only",
 "learning_rate_resolution": 1e-06,
 "performance_tracking_precision": 0.0001
 },
 "semantic_convergence": {
 "_help": "Unifies 6 semantic systems for word embedding differentiation",
 "enabled": true,
 "use_learned_embeddings": true,
 "embedding_dim": 64,
 "organism_embedding_alpha": 0.22,
 "concept_system_alpha": 0.15,
 "knowledge_web_influence_interval": 5,
 "phenotype_to_vocabulary": true
 },
 "arena": {
 "_prefer_native_games_help": "When true, prioritizes language/concept games that use organism's native abilities (no Gym dependency)",
 "_proton_game_probability_help": "Fraction of battles that use Proton Game Arena (0.0-1.0). Set to 0.5 for 50% Proton Game battles.",
 "trap_mechanics": {
 "_help": "Planning pressure via trap logic in battles",
 "enabled": true,
 "low_foresight_threshold": 0.35,
 "high_foresight_threshold": 0.65,
 "trap_chance_multiplier": 0.4,
 "trap_damage_multiplier": 0.1
 },
 "battle_consequences": {
 "fitness_transfer_rate": 1,
 "resource_transfer_enabled": true,
 "resource_transfer_rate": 1,
 "trait_bonus_cap": 0.3,
 "trait_evolution_enabled": true
 },
 "default_battle_type": "PROTON_GAME",
 "description": "Proton Game Arena - Apprentice Adept style gym battles (Piers Anthony + Highlander inspired)",
 "enabled": true,
 "game_selection": {
 "allow_negotiation": true,
 "fallback_on_deadlock": "random",
 "mode": "ai_driven",
 "negotiation_rounds": 3
 },
 "grid_weights": {
 "challenge_types": {
 "ARTS": 2.0,
 "CHANCE": 1.0,
 "MENTAL": 1.5,
 "PHYSICAL": 0.5
 },
 "resource_types": {
 "ANIMAL": 1.0,
 "MACHINE": 1.0,
 "NAKED": 1.5,
 "TOOL": 1.0
 }
 },
 "gym_settings": {
 "default_episodes": 10,
 "max_episode_steps": 1000,
 "parallel_envs": 1,
 "render_mode": null
 },
 "prefer_native_games": false,
 "proton_game_probability": 1.0,
 "gym_only": true,
 "_gym_only_help": "When true, ONLY real Gymnasium environments (CartPole, Acrobot, etc). No native/custom games.",
 "tournament": {
 "default_format": "single_elimination",
 "round_robin_battles_per_pair": 3,
 "seeding_method": "fitness_based"
 }
 },
 "causation_detection": {
 "correlation_threshold": 0.5,
 "direct_causation_time_window": 2,
 "enable_bidirectional_causations": true,
 "enable_language_causations": true,
 "enable_ml_causations": true,
 "enable_neural_causations": true,
 "enable_neural_decision_causations": true,
 "enable_neural_training_causations": true,
 "enable_phase_transition_causations": true,
 "enabled": true,
 "phase_transition_time_window": 2.5,
 "recent_events_window": 150,
 "thresholds": {
 "clustering_coefficient": {
 "collapse": 0.5,
 "direction": "above"
 },
 "modularity": {
 "collapse": 0.3,
 "direction": "below"
 },
 "organism_count": {
 "collapse": 500,
 "direction": "above"
 },
 "violation_pressure": {
 "vp0": 0.25,
 "vp1": 0.5,
 "vp2": 0.75,
 "vp3": 0.99
 },
 "vp_calculations": {
 "direction": "above",
 "transition": 50
 }
 }
 },
 "commerce": {
 "_help": "Commerce as a parallel power axis built INTO existing faculties (semantic_credit, market, confederation tiers, illumination). Bounded by Highlander pruning; anti-oligarch via saturating returns + distributed interdependence.",
 "enabled": true,
 "selection": {
 "_help": "Bounded saturating economic fitness bonus cap*x/(x+k): advantaged, never dominant.",
 "fitness_bonus_cap": 0.15,
 "fitness_bonus_k": 10.0
 },
 "market": {
 "_help": "Continuous double-auction money machine over a spendable commerce_balance wallet.",
 "enabled": true,
 "starting_balance": 10.0,
 "relay_spread_frac": 0.05,
 "saving_propensity": 0.6,
 "_saving_propensity_help": "Kinetic-exchange downshift (0-1): fraction of wallet NOT staked per trade. Higher = more egalitarian/peaked wealth, lower = condensation-prone.",
 "trade_probability": 0.65,
 "relay_probability": 0.35,
 "_relay_probability_help": "Fraction of orders routed through a broker/market-maker that earns the spread.",
 "commerce_reward": 0.35
 },
 "confederation": {
 "_help": "Economic interdependence as an alternative confederation-tier determinant (Hanseatic, distributed). Trade-rich peer confederations earn illumination_empire/hegemony.",
 "interdependence_enabled": true,
 "interdependence_min_volume": 100.0,
 "interdependence_min_members_trading": 3
 },
 "mercenary": {
 "_help": "The Free Company: a decentralized condotta CONTRACT MARKET for violence (commerce<->combat bridge). The weak pool wealth to rent L4+ force and strike UP the fitness ladder; mercs sell violence for balance + semantic-credit renown. Paid on RESULT (a kill), never time. Force is RENTED, never owned - no HQ/commander/roster to capture. The pariah mechanic turns the board on serial kill-buyers, so no standing private army can form.",
 "enabled": true,
 "merc_mastery_floor": 4,
 "_merc_mastery_floor_help": "Only L4+ elites can SELL violence - keeps it among the apex, never the protected developing population.",
 "ferma_rounds": 3,
 "_ferma_help": "Term of service in rounds; time-boxed so no permanent private army forms.",
 "post_probability": 0.25,
 "accept_probability": 0.6,
 "bounty_stake_frac": 0.5,
 "_bounty_stake_frac_help": "Saving-propensity bound: one bounty stakes at most this fraction of a wallet, so big targets need MANY patrons = force stays distributed.",
 "min_bounty": 0.5,
 "max_open_contracts_per_patron": 1,
 "max_contracts_per_merc": 3,
 "_max_contracts_per_merc_help": "Concurrency cap only (force stays distributed - no standing private army). LIFETIME earnings/renown are uncapped: a merc fulfils unlimited contracts over time, just a few at once. Big dogs draft the whole board by best expected return.",
 "renown_frac": 1.0,
 "_renown_frac_help": "Semantic-credit renown minted on fulfilment = renown_frac * bounty (rides the same standing surface as all commerce; feeds fitness bonus + neural slot 31).",
 "pariah_window_rounds": 8,
 "pariah_refuse_threshold": 0.6,
 "_pariah_help": "Buy serial kills inside the window and your pariah score rises; at/above the threshold no merc serves you (keystone anti-tyranny mechanic).",
 "learn": true,
 "commerce_reward": 0.35,
 "_reactive_help": "The web of intrigue: a marked organism fights back and co-victims band together. SELF-DEFENSE = hire counter-force on the inbound merc; VENDETTA = put a price on the patron who paid for your death (reciprocal); COALITION = pool money onto a contract on a SHARED enemy (enemy of my enemy). Awareness/threat flags (under_contract, bounty_on_head, contract_enemies, threat_allies) are exposed on each organism - non-neural, no reseed. Bounded by wallet + per-org cap + reaction-depth cap so the web weaves without exploding.",
 "defense_probability": 0.5,
 "vendetta_probability": 0.4,
 "coalition_probability": 0.5,
 "reactive_stake_frac": 0.4,
 "coalition_stake_frac": 0.3,
 "max_reactive_per_org": 2,
 "max_reaction_depth": 2
 }
 },
 "evolution": {
 "adaptation_sensitivity": 0.005,
 "diversity_guard": {
 "enabled": true,
 "frequency_threshold": 0.1,
 "hash_similarity_threshold": 0.88,
 "penalty": 0.05
 },
 "fitness_precision": 1e-07,
 "genotype_length": 48,
 "max_generations": 999999,
 "mutation_rate": {
 "initial": 0.04
 },
 "mutation_rate_precision": 0.001,
 "population_size": 100
 },
 "feedback": {
 "enabled": true,
 "hysteresis_checks": 5,
 "interval_frames": 10,
 "knobs": {
 "clustering_bias": {
 "initial": 1.5,
 "max": 1.6,
 "min": 0.3,
 "step": 0.05
 },
 "mutation_rate": {
 "initial": 0.033,
 "max": 0.06,
 "min": 0.002,
 "step": 0.001
 },
 "new_edge_rate": {
 "initial": 4.8,
 "max": 6,
 "min": 0.2,
 "step": 0.1
 },
 "quantum_pruning": {
 "initial": 0.35,
 "max": 1,
 "min": 0,
 "step": 0.05
 }
 },
 "rate_limit_frames": 120
 },
 "hardware_profile": null,
 "hardware_governor": {
 "_auto_scale_help": "Set to false to disable auto-scaling of config values. Governor will only clamp to max, not scale up.",
 "auto_scale": false
 },
 "health_monitor": {
 "critical_threshold": 0.25,
 "enabled": true,
 "healthy_threshold": 0.75,
 "history_size": 100,
 "thresholds": {
 "critical": 0.25,
 "healthy": 0.65,
 "warning": 0.45
 },
 "warning_threshold": 0.55,
 "weight_adaptability": 0.2,
 "weight_coherence": 0.3,
 "weight_diversity": 0.25,
 "weight_lawfulness": 0.15,
 "weight_sustainability": 0.1
 },
 "highlander": {
 "eval_interval_seconds": 300,
 "_eval_interval_seconds_help": "Battle frequency for mastery 4+ organisms (seconds between Highlander rounds). Organisms below mastery 4 are protected regardless of this setting.",
 "alliance_warfare": {
 "betrayal_chance": 0.0,
 "confederation_war_threshold": 0.6,
 "enabled": true,
 "existential_war_threshold": 0.8,
 "max_alliance_size": 999,
 "max_alliances": 9999,
 "max_confederations": 9999,
 "min_alliance_size": 2,
 "war_declaration_threshold": 0.6,
 "war_frequency": 0.5,
 "illumination_stability_threshold": 5,
 "organism_communication": {
 "enabled": true,
 "_help": "Organisms speak to each other at confluence points (battles, alliances, etc.)",
 "pre_battle_communication": true,
 "communication_affects_battles": true,
 "intel_bonus_max": 0.15,
 "_intel_bonus_help": "Max reaction speed bonus from shared vocabulary in battles",
 "ultimatum_enabled": true,
 "_ultimatum_help": "Warchiefs can issue 'Join or Die' demands to weaker alliances"
 }
 },
 "chaos_factor": 0.0,
 "competition_intensity": 0.4,
 "concordance_contact_enabled": true,
 "_concordance_contact_enabled_help": "When allied-only Highlander pools would otherwise avoid contact, record bounded non-lethal concordance receipts instead of forcing domination.",
 "concordance_pressure_value": 1.0,
 "_concordance_pressure_value_help": "Semantic credit value split across participants for each concordance contact obligation.",
 "description": "BOOM-BUST WAVES: 10-minute growth \u2192 Highlander culling \u2192 regeneration \u2192 repeat. Selects for learning competence over chaos survival.",
 "enabled": true,
 "extreme_mode": {
 "chaos_factor": 0.0,
 "competition_intensity": 0.4,
 "description": "EXTREME DIFFICULTY - Maximum evolutionary pressure",
 "germination_rate": 0.1,
 "max_battle_rounds": 15,
 "max_population": 25,
 "min_population": 15,
 "mutation_rate": 0.0,
 "population_size": 200,
 "predation_enabled": true,
 "rounds_per_cycle": 2,
 "survival_threshold": 0.4
 },
 "germination_rate": 0.15,
 "max_battle_rounds": 30,
 "max_capsules": 100,
 "max_genetic_samples": 100,
 "max_population": 50,
 "min_population": 10,
 "mutation_rate": 0.0,
 "population_size": 200,
 "predation_enabled": true,
 "rounds_per_cycle": 3,
 "survival_threshold": 0.5
 },
 "lattice": {
 "entropy_sensitivity": 5e-05,
 "interaction_precision": 0.0001,
 "particles": 1000,
 "prune_threshold": 0,
 "stability_tolerance": 0.0005
 },
 "logging": {
 "shared_state_dump_interval": 30,
 "sample_rate": 5,
 "_sample_rate_help": "Log 1 in N state entries to disk (1=all, 10=10%). Memory history always full rate."
 },
 "meta_cognitive": {
 "description": "Meta-cognitive systems: self-awareness, self-tuning, autonomous optimization",
 "self_tuning": {
 "enabled": true,
 "min_confidence_threshold": 0.6,
 "mode": "autonomous",
 "performance_targets": {
 "max_anomaly_ratio": 0.2,
 "min_cluster_diversity": 3,
 "min_fitness_std": 0.05
 },
 "safe_parameters": [
 "evolution.mutation_rate.initial",
 "evolution.diversity_guard.penalty",
 "evolution.diversity_guard.frequency_threshold",
 "evolution.diversity_guard.hash_similarity_threshold",
 "evolution.population_size",
 "evolution.adaptation_sensitivity",
 "feedback.knobs.mutation_rate.initial",
 "feedback.knobs.new_edge_rate.initial",
 "feedback.knobs.clustering_bias.initial",
 "feedback.knobs.quantum_pruning.initial",
 "neural.training.learning_rate",
 "neural.training.gamma",
 "neural.training.epsilon_decay",
 "neural.training.batch_size",
 "neural.rewards.fitness_improvement",
 "neural.rewards.connection_success",
 "neural.rewards.survival",
 "neural.inheritance.crossover_rate",
 "neural.inheritance.mutation_rate",
 "network.max_organisms",
 "network.max_connections",
 "network.resource_pool",
 "scikit.clustering.min_cluster_size",
 "scikit.anomaly_detection.contamination",
 "scikit.anomaly_detection.n_estimators",
 "quantum.initial_states",
 "quantum.entanglement_sensitivity",
 "quantum.prune_check_interval",
 "vp_monitoring.adaptive_response.high_vp_threshold",
 "vp_monitoring.stabilization.smoothing_factor",
 "causation_detection.correlation_threshold",
 "meta_cognitive.self_tuning.tuning_interval_frames",
 "meta_cognitive.self_tuning.min_confidence_threshold"
 ],
 "tuning_interval_frames": 5
 }
 },
 "network": {
 "connection_strength_resolution": 5e-06,
 "emergence_sensitivity": 1e-06,
 "max_connections": 48000,
 "max_organisms": 5000,
 "resource_flow_precision": 0.0001,
 "resource_pool": 1000,
 "stability_precision": 1e-07
 },
 "neural": {
 "brain": {
 "activation": "relu",
 "dropout": 0.1,
 "hidden_dim": 64,
 "input_dim": 34,
 "output_dim": 6,
 "vocab_size": 10000,
 "attention_dim": 32
 },
 "hopfield": {
 "_help": "Modern continuous Hopfield network for iterative thought refinement",
 "enabled": true,
 "patterns": 32,
 "iterations": 5,
 "beta": 1.0
 },
 "world_model": {
 "_help": "Predictive world model for imagination-based planning",
 "enabled": true,
 "delta": 0.2,
 "_delta_help": "Loss weight for world model prediction (alpha+beta+gamma+delta should sum reasonably)",
 "curiosity_scale": 0.05,
 "_curiosity_scale_help": "How much prediction error contributes to intrinsic reward",
 "planning_trust": 0.25,
 "_planning_trust_help": "Blend factor: (1-trust)*DQN + trust*imagination",
 "imagination_temperature": 3.0,
 "_imagination_temperature_help": "Sharpness of imagination softmax (higher = more decisive)",
 "imagination_enabled": true,
 "candidate_threshold": 0.1,
 "_candidate_threshold_help": "Only imagine actions with base probability above this",
 "language_magnetism_weight": 0.15,
 "_language_magnetism_weight_help": "How much word magnetism biases imagination scoring",
 "max_imagination_steps": 5,
 "_max_imagination_steps_help": "Maximum planning horizon for high-foresight organisms",
 "foresight_scaling": true,
 "_foresight_scaling_help": "If true, steps = 1 + int(foresight * max_steps). If false, always use max_steps",
 "pruning_threshold": 0.15,
 "_pruning_threshold_help": "Skip imagining branches with cumulative probability below this",
 "discount_factor": 0.9,
 "_discount_factor_help": "Gamma for discounting future imagined rewards"
 },
 "concept_system": {
 "concept_loss_weight": 0.15,
 "embed_dim": 64,
 "enabled": true,
 "num_key_compositions": 30,
 "utility_update_alpha": 0.15
 },
 "device": "cuda",
 "enabled": true,
 "inheritance": {
 "crossover_rate": 0.9,
 "enabled": true,
 "mutation_rate": 0.2
 },
 "initialization": {
 "deterministic": false,
 "seed": null
 },
 "language_model": {
 "attention": {
 "attention_dim": 32,
 "enabled": true,
 "num_heads": 4
 },
 "curriculum": {
 "enabled": true,
 "ml_quality": {
 "enabled": true,
 "high_quality_threshold": 0.55,
 "low_quality_threshold": 0.3,
 "max_sequence_length": 64,
 "min_sequence_length": 8,
 "sequence_length_step": 2
 },
 "sequence_lengths": {
 "stage_0": 8,
 "stage_1": 16,
 "stage_2": 32,
 "stage_3": 128
 },
 "vp_thresholds": {
 "stage_1": 0.5,
 "stage_2": 0.4,
 "stage_3": 0.3
 }
 },
 "enabled": true,
 "min_action_history": 1,
 "_min_action_history_help": "Read directly by language_teacher.py; mirrors teacher.min_action_history so action-grounded teaching starts after one recorded action.",
 "generation": {
 "max_length": 128,
 "temperature": 1.5,
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 },
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 },
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 },
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 }
 },
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 "reason": "antennae_adjustment",
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 "expected_impact": "Improve highlander performance",
 "actual_impact": "Performance: 50.00%"
 },
 {
 "param": "competition_intensity",
 "change": "0.37122879004193454 \u2192 0.3197378293326806",
 "reason": "antennae_adjustment",
 "success": false,
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 "expected_impact": "Improve highlander performance",
 "actual_impact": "Performance: 50.00%"
 },
 {
 "param": "survival_threshold",
 "change": "0.3643856049790327 \u2192 0.3901310853336597",
 "reason": "antennae_adjustment",
 "success": false,
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 "expected_impact": "Improve highlander performance",
 "actual_impact": "Performance: 50.00%"
 },
 {
 "param": "germination_rate",
 "change": "0.1 \u2192 0.1",
 "reason": "initialized",
 "success": true,
 "trigger_metrics": {
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 },
 "expected_impact": "Improve highlander performance",
 "actual_impact": "Performance: 100.00%"
 },
 {
 "param": "predation_enabled",
 "change": "False \u2192 False",
 "reason": "initialized",
 "success": true,
 "trigger_metrics": {
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 },
 "expected_impact": "Improve highlander performance",
 "actual_impact": "Performance: 100.00%"
 }
 ],
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 "domains": {
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 "learning": 6,
 "evolution": 6,
 "simulation": 4,
 "language": 6,
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 },
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 },
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 }
 },
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 "sovereign_ids_count": 604,
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 },
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 "trait_count": 4,
 "tape_cells": 45,
 "tape_position": 45
 },
 "language": {
 "vocab_size": 10000,
 "word_count": 10000,
 "organism_word_assignments": 3500263,
 "organisms_with_words": 5440,
 "language_anchors": 33568,
 "total_associations": 3489402
 },
 "commerce": {
 "holders": 200,
 "total_semantic_credit": 2311.1956,
 "sc_gini": 0.442,
 "sc_top1_share": 0.0258,
 "sc_top4_share": 0.0946,
 "market_vwap": 0.8705776633986222,
 "market_volume": 1993.778778,
 "market_spread": null,
 "open_bids": 0,
 "open_asks": 100,
 "total_trades": 1048,
 "commerce_balance_total": 2010.0,
 "note": "live parallel power axis; killing stays apex. See commerce_substrate.py / commerce_market.py."
 },
 "phase_sync": {
 "network": {
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 "clustering": 0.08271245208229518,
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 },
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 "phase": "genesis",
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 },
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 }
 },
 "exploration_tracking": {
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 "reality_sim_explorations": 214,
 "explorer_explorations": 22,
 "target_ratio": "500:50",
 "current_ratio": "214:22",
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 "progress_to_transition": 0.4427825368703331
 },
 "unified_health": {
 "overall_health": 0.5,
 "reality_sim_health": 0.5,
 "health_score": 0.5,
 "state": "unknown",
 "components": {},
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 "phase_alignment_health": 0.610782536870333
 },
 "transition_status": {
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 "explorer_ready": false,
 "djinn_kernel_ready": false,
 "unified_transition_triggered": false,
 "estimated_time_to_transition": "~inf cycles"
 },
 "config": {
 "_case_study": "STUDY 6: BOOM-BUST WAVES - Grounded learning with periodic Highlander concentration",
 "_hypothesis": "Organisms that build foundations (6\u2192200\u21925000\u21927500\u219210000 word capacity, with smaller advancement exam windows) before facing Highlander waves develop more coherent intelligence than those thrown into instant chaos",
 "_hardware_profile_help": "Set to 'beast', 'workstation', 'standard', 'laptop', 'potato', or 'cpu_only' to override auto-detection. null = auto-detect",
 "language": {
 "_help": "Grounded Language Mode: Organisms learn language through action-outcome associations before semantic understanding",
 "mode": "grounded",
 "_mode_options": "grounded | semantic | hybrid",
 "grounded": {
 "enabled": true,
 "mastery_gating": true,
 "vocabulary_masking": "soft",
 "_vocabulary_masking_options": "soft | hard | none",
 "initial_mastery_level": 0,
 "_initial_mastery_level_help": "0=6 words, 1=200 cap, 2=5000 protected strategist cap, 3=7500 protected transition cap, 4=10000 semantic graduation",
 "mastery_vocab_sizes": [
 6,
 200,
 5000,
 7500,
 10000
 ],
 "_mastery_vocab_sizes_help": "Per-level vocabulary ownership capacity. L2/L3 remain protected from Highlander because direct battles require L4+.",
 "mastery_advancement_vocab_windows": [
 6,
 200,
 1000,
 5000
 ],
 "_mastery_advancement_vocab_windows_help": "Per-level advancement exam window. Keeps L2 able to know thousands of words without requiring thousands of active/deep words to choose L3.",
 "mastery_advancement_ratio": 0.3,
 "mastery_depth_ratio": 0.15,
 "mastery_min_experiences": [
 10,
 40,
 120,
 300
 ],
 "always_allow_tokens": [
 ".",
 ",",
 "!",
 "?",
 "and",
 "the",
 "a",
 "is",
 "to",
 "of",
 "in"
 ]
 },
 "semantic": {
 "enabled": false,
 "knowledge_web_bootstrap": false,
 "semantic_reward_at_level_4": true,
 "semantic_reward_weight": 0.25
 },
 "action_hints": {
 "enabled": true,
 "bias_strength": 0.3,
 "expectation_window_seconds": 10.0
 },
 "organism_learning_berth": {
 "_help": "Bounded lane for learning from human-selected suggestions and operation-sequence prompts. Inside-game only; no outside command authority.",
 "enabled": true,
 "space_window_seconds": 3600,
 "config_window_seconds": 1200,
 "max_interaction_context_words": 64,
 "human_choice_reward_bonus": 0.08,
 "operation_sequence_reward_bonus": 0.05,
 "allowed_operation_axes": [
 "inside",
 "around",
 "above",
 "below",
 "between",
 "through"
 ],
 "inside_boundary": "inside_game_only",
 "forbidden_outside_actions": [
 "deployment",
 "money",
 "identity",
 "safety_systems",
 "authority_override"
 ]
 }
 },
 "agency": {
 "confidence_threshold": 0.0001,
 "decision_precision": 1e-05,
 "initial_mode": "manual_only",
 "learning_rate_resolution": 1e-06,
 "performance_tracking_precision": 0.0001
 },
 "semantic_convergence": {
 "_help": "Unifies 6 semantic systems for word embedding differentiation",
 "enabled": true,
 "use_learned_embeddings": true,
 "embedding_dim": 64,
 "organism_embedding_alpha": 0.22,
 "concept_system_alpha": 0.15,
 "knowledge_web_influence_interval": 5,
 "phenotype_to_vocabulary": true
 },
 "arena": {
 "_prefer_native_games_help": "When true, prioritizes language/concept games that use organism's native abilities (no Gym dependency)",
 "_proton_game_probability_help": "Fraction of battles that use Proton Game Arena (0.0-1.0). Set to 0.5 for 50% Proton Game battles.",
 "trap_mechanics": {
 "_help": "Planning pressure via trap logic in battles",
 "enabled": true,
 "low_foresight_threshold": 0.35,
 "high_foresight_threshold": 0.65,
 "trap_chance_multiplier": 0.4,
 "trap_damage_multiplier": 0.1
 },
 "battle_consequences": {
 "fitness_transfer_rate": 1,
 "resource_transfer_enabled": true,
 "resource_transfer_rate": 1,
 "trait_bonus_cap": 0.3,
 "trait_evolution_enabled": true
 },
 "default_battle_type": "PROTON_GAME",
 "description": "Proton Game Arena - Apprentice Adept style gym battles (Piers Anthony + Highlander inspired)",
 "enabled": true,
 "game_selection": {
 "allow_negotiation": true,
 "fallback_on_deadlock": "random",
 "mode": "ai_driven",
 "negotiation_rounds": 3
 },
 "grid_weights": {
 "challenge_types": {
 "ARTS": 2.0,
 "CHANCE": 1.0,
 "MENTAL": 1.5,
 "PHYSICAL": 0.5
 },
 "resource_types": {
 "ANIMAL": 1.0,
 "MACHINE": 1.0,
 "NAKED": 1.5,
 "TOOL": 1.0
 }
 },
 "gym_settings": {
 "default_episodes": 10,
 "max_episode_steps": 1000,
 "parallel_envs": 1,
 "render_mode": null
 },
 "prefer_native_games": false,
 "proton_game_probability": 1.0,
 "gym_only": true,
 "_gym_only_help": "When true, ONLY real Gymnasium environments (CartPole, Acrobot, etc). No native/custom games.",
 "tournament": {
 "default_format": "single_elimination",
 "round_robin_battles_per_pair": 3,
 "seeding_method": "fitness_based"
 }
 },
 "causation_detection": {
 "correlation_threshold": 0.5,
 "direct_causation_time_window": 2,
 "enable_bidirectional_causations": true,
 "enable_language_causations": true,
 "enable_ml_causations": true,
 "enable_neural_causations": true,
 "enable_neural_decision_causations": true,
 "enable_neural_training_causations": true,
 "enable_phase_transition_causations": true,
 "enabled": true,
 "phase_transition_time_window": 2.5,
 "recent_events_window": 150,
 "thresholds": {
 "clustering_coefficient": {
 "collapse": 0.5,
 "direction": "above"
 },
 "modularity": {
 "collapse": 0.3,
 "direction": "below"
 },
 "organism_count": {
 "collapse": 500,
 "direction": "above"
 },
 "violation_pressure": {
 "vp0": 0.25,
 "vp1": 0.5,
 "vp2": 0.75,
 "vp3": 0.99
 },
 "vp_calculations": {
 "direction": "above",
 "transition": 50
 }
 }
 },
 "commerce": {
 "_help": "Commerce as a parallel power axis built INTO existing faculties (semantic_credit, market, confederation tiers, illumination). Bounded by Highlander pruning; anti-oligarch via saturating returns + distributed interdependence.",
 "enabled": true,
 "selection": {
 "_help": "Bounded saturating economic fitness bonus cap*x/(x+k): advantaged, never dominant.",
 "fitness_bonus_cap": 0.15,
 "fitness_bonus_k": 10.0
 },
 "market": {
 "_help": "Continuous double-auction money machine over a spendable commerce_balance wallet.",
 "enabled": true,
 "starting_balance": 10.0,
 "relay_spread_frac": 0.05,
 "saving_propensity": 0.6,
 "_saving_propensity_help": "Kinetic-exchange downshift (0-1): fraction of wallet NOT staked per trade. Higher = more egalitarian/peaked wealth, lower = condensation-prone.",
 "trade_probability": 0.65,
 "relay_probability": 0.35,
 "_relay_probability_help": "Fraction of orders routed through a broker/market-maker that earns the spread.",
 "commerce_reward": 0.35
 },
 "confederation": {
 "_help": "Economic interdependence as an alternative confederation-tier determinant (Hanseatic, distributed). Trade-rich peer confederations earn illumination_empire/hegemony.",
 "interdependence_enabled": true,
 "interdependence_min_volume": 100.0,
 "interdependence_min_members_trading": 3
 },
 "mercenary": {
 "_help": "The Free Company: a decentralized condotta CONTRACT MARKET for violence (commerce<->combat bridge). The weak pool wealth to rent L4+ force and strike UP the fitness ladder; mercs sell violence for balance + semantic-credit renown. Paid on RESULT (a kill), never time. Force is RENTED, never owned - no HQ/commander/roster to capture. The pariah mechanic turns the board on serial kill-buyers, so no standing private army can form.",
 "enabled": true,
 "merc_mastery_floor": 4,
 "_merc_mastery_floor_help": "Only L4+ elites can SELL violence - keeps it among the apex, never the protected developing population.",
 "ferma_rounds": 3,
 "_ferma_help": "Term of service in rounds; time-boxed so no permanent private army forms.",
 "post_probability": 0.25,
 "accept_probability": 0.6,
 "bounty_stake_frac": 0.5,
 "_bounty_stake_frac_help": "Saving-propensity bound: one bounty stakes at most this fraction of a wallet, so big targets need MANY patrons = force stays distributed.",
 "min_bounty": 0.5,
 "max_open_contracts_per_patron": 1,
 "max_contracts_per_merc": 3,
 "_max_contracts_per_merc_help": "Concurrency cap only (force stays distributed - no standing private army). LIFETIME earnings/renown are uncapped: a merc fulfils unlimited contracts over time, just a few at once. Big dogs draft the whole board by best expected return.",
 "renown_frac": 1.0,
 "_renown_frac_help": "Semantic-credit renown minted on fulfilment = renown_frac * bounty (rides the same standing surface as all commerce; feeds fitness bonus + neural slot 31).",
 "pariah_window_rounds": 8,
 "pariah_refuse_threshold": 0.6,
 "_pariah_help": "Buy serial kills inside the window and your pariah score rises; at/above the threshold no merc serves you (keystone anti-tyranny mechanic).",
 "learn": true,
 "commerce_reward": 0.35,
 "_reactive_help": "The web of intrigue: a marked organism fights back and co-victims band together. SELF-DEFENSE = hire counter-force on the inbound merc; VENDETTA = put a price on the patron who paid for your death (reciprocal); COALITION = pool money onto a contract on a SHARED enemy (enemy of my enemy). Awareness/threat flags (under_contract, bounty_on_head, contract_enemies, threat_allies) are exposed on each organism - non-neural, no reseed. Bounded by wallet + per-org cap + reaction-depth cap so the web weaves without exploding.",
 "defense_probability": 0.5,
 "vendetta_probability": 0.4,
 "coalition_probability": 0.5,
 "reactive_stake_frac": 0.4,
 "coalition_stake_frac": 0.3,
 "max_reactive_per_org": 2,
 "max_reaction_depth": 2
 }
 },
 "evolution": {
 "adaptation_sensitivity": 0.005,
 "diversity_guard": {
 "enabled": true,
 "frequency_threshold": 0.1,
 "hash_similarity_threshold": 0.88,
 "penalty": 0.05
 },
 "fitness_precision": 1e-07,
 "genotype_length": 48,
 "max_generations": 999999,
 "mutation_rate": {
 "initial": 0.04
 },
 "mutation_rate_precision": 0.001,
 "population_size": 100
 },
 "feedback": {
 "enabled": true,
 "hysteresis_checks": 5,
 "interval_frames": 10,
 "knobs": {
 "clustering_bias": {
 "initial": 1.5,
 "max": 1.6,
 "min": 0.3,
 "step": 0.05
 },
 "mutation_rate": {
 "initial": 0.033,
 "max": 0.06,
 "min": 0.002,
 "step": 0.001
 },
 "new_edge_rate": {
 "initial": 4.8,
 "max": 6,
 "min": 0.2,
 "step": 0.1
 },
 "quantum_pruning": {
 "initial": 0.35,
 "max": 1,
 "min": 0,
 "step": 0.05
 }
 },
 "rate_limit_frames": 120
 },
 "hardware_profile": null,
 "hardware_governor": {
 "_auto_scale_help": "Set to false to disable auto-scaling of config values. Governor will only clamp to max, not scale up.",
 "auto_scale": false
 },
 "health_monitor": {
 "critical_threshold": 0.25,
 "enabled": true,
 "healthy_threshold": 0.75,
 "history_size": 100,
 "thresholds": {
 "critical": 0.25,
 "healthy": 0.65,
 "warning": 0.45
 },
 "warning_threshold": 0.55,
 "weight_adaptability": 0.2,
 "weight_coherence": 0.3,
 "weight_diversity": 0.25,
 "weight_lawfulness": 0.15,
 "weight_sustainability": 0.1
 },
 "highlander": {
 "eval_interval_seconds": 300,
 "_eval_interval_seconds_help": "Battle frequency for mastery 4+ organisms (seconds between Highlander rounds). Organisms below mastery 4 are protected regardless of this setting.",
 "alliance_warfare": {
 "betrayal_chance": 0.0,
 "confederation_war_threshold": 0.6,
 "enabled": true,
 "existential_war_threshold": 0.8,
 "max_alliance_size": 999,
 "max_alliances": 9999,
 "max_confederations": 9999,
 "min_alliance_size": 2,
 "war_declaration_threshold": 0.6,
 "war_frequency": 0.5,
 "illumination_stability_threshold": 5,
 "organism_communication": {
 "enabled": true,
 "_help": "Organisms speak to each other at confluence points (battles, alliances, etc.)",
 "pre_battle_communication": true,
 "communication_affects_battles": true,
 "intel_bonus_max": 0.15,
 "_intel_bonus_help": "Max reaction speed bonus from shared vocabulary in battles",
 "ultimatum_enabled": true,
 "_ultimatum_help": "Warchiefs can issue 'Join or Die' demands to weaker alliances"
 }
 },
 "chaos_factor": 0.0,
 "competition_intensity": 0.4,
 "concordance_contact_enabled": true,
 "_concordance_contact_enabled_help": "When allied-only Highlander pools would otherwise avoid contact, record bounded non-lethal concordance receipts instead of forcing domination.",
 "concordance_pressure_value": 1.0,
 "_concordance_pressure_value_help": "Semantic credit value split across participants for each concordance contact obligation.",
 "description": "BOOM-BUST WAVES: 10-minute growth \u2192 Highlander culling \u2192 regeneration \u2192 repeat. Selects for learning competence over chaos survival.",
 "enabled": true,
 "extreme_mode": {
 "chaos_factor": 0.0,
 "competition_intensity": 0.4,
 "description": "EXTREME DIFFICULTY - Maximum evolutionary pressure",
 "germination_rate": 0.1,
 "max_battle_rounds": 15,
 "max_population": 25,
 "min_population": 15,
 "mutation_rate": 0.0,
 "population_size": 200,
 "predation_enabled": true,
 "rounds_per_cycle": 2,
 "survival_threshold": 0.4
 },
 "germination_rate": 0.15,
 "max_battle_rounds": 30,
 "max_capsules": 100,
 "max_genetic_samples": 100,
 "max_population": 50,
 "min_population": 10,
 "mutation_rate": 0.0,
 "population_size": 200,
 "predation_enabled": true,
 "rounds_per_cycle": 3,
 "survival_threshold": 0.5
 },
 "lattice": {
 "entropy_sensitivity": 5e-05,
 "interaction_precision": 0.0001,
 "particles": 1000,
 "prune_threshold": 0,
 "stability_tolerance": 0.0005
 },
 "logging": {
 "shared_state_dump_interval": 30,
 "sample_rate": 5,
 "_sample_rate_help": "Log 1 in N state entries to disk (1=all, 10=10%). Memory history always full rate."
 },
 "meta_cognitive": {
 "description": "Meta-cognitive systems: self-awareness, self-tuning, autonomous optimization",
 "self_tuning": {
 "enabled": true,
 "min_confidence_threshold": 0.6,
 "mode": "autonomous",
 "performance_targets": {
 "max_anomaly_ratio": 0.2,
 "min_cluster_diversity": 3,
 "min_fitness_std": 0.05
 },
 "safe_parameters": [
 "evolution.mutation_rate.initial",
 "evolution.diversity_guard.penalty",
 "evolution.diversity_guard.frequency_threshold",
 "evolution.diversity_guard.hash_similarity_threshold",
 "evolution.population_size",
 "evolution.adaptation_sensitivity",
 "feedback.knobs.mutation_rate.initial",
 "feedback.knobs.new_edge_rate.initial",
 "feedback.knobs.clustering_bias.initial",
 "feedback.knobs.quantum_pruning.initial",
 "neural.training.learning_rate",
 "neural.training.gamma",
 "neural.training.epsilon_decay",
 "neural.training.batch_size",
 "neural.rewards.fitness_improvement",
 "neural.rewards.connection_success",
 "neural.rewards.survival",
 "neural.inheritance.crossover_rate",
 "neural.inheritance.mutation_rate",
 "network.max_organisms",
 "network.max_connections",
 "network.resource_pool",
 "scikit.clustering.min_cluster_size",
 "scikit.anomaly_detection.contamination",
 "scikit.anomaly_detection.n_estimators",
 "quantum.initial_states",
 "quantum.entanglement_sensitivity",
 "quantum.prune_check_interval",
 "vp_monitoring.adaptive_response.high_vp_threshold",
 "vp_monitoring.stabilization.smoothing_factor",
 "causation_detection.correlation_threshold",
 "meta_cognitive.self_tuning.tuning_interval_frames",
 "meta_cognitive.self_tuning.min_confidence_threshold"
 ],
 "tuning_interval_frames": 5
 }
 },
 "network": {
 "connection_strength_resolution": 5e-06,
 "emergence_sensitivity": 1e-06,
 "max_connections": 48000,
 "max_organisms": 5000,
 "resource_flow_precision": 0.0001,
 "resource_pool": 1000,
 "stability_precision": 1e-07
 },
 "neural": {
 "brain": {
 "activation": "relu",
 "dropout": 0.1,
 "hidden_dim": 64,
 "input_dim": 34,
 "output_dim": 6,
 "vocab_size": 10000,
 "attention_dim": 32
 },
 "hopfield": {
 "_help": "Modern continuous Hopfield network for iterative thought refinement",
 "enabled": true,
 "patterns": 32,
 "iterations": 5,
 "beta": 1.0
 },
 "world_model": {
 "_help": "Predictive world model for imagination-based planning",
 "enabled": true,
 "delta": 0.2,
 "_delta_help": "Loss weight for world model prediction (alpha+beta+gamma+delta should sum reasonably)",
 "curiosity_scale": 0.05,
 "_curiosity_scale_help": "How much prediction error contributes to intrinsic reward",
 "planning_trust": 0.25,
 "_planning_trust_help": "Blend factor: (1-trust)*DQN + trust*imagination",
 "imagination_temperature": 3.0,
 "_imagination_temperature_help": "Sharpness of imagination softmax (higher = more decisive)",
 "imagination_enabled": true,
 "candidate_threshold": 0.1,
 "_candidate_threshold_help": "Only imagine actions with base probability above this",
 "language_magnetism_weight": 0.15,
 "_language_magnetism_weight_help": "How much word magnetism biases imagination scoring",
 "max_imagination_steps": 5,
 "_max_imagination_steps_help": "Maximum planning horizon for high-foresight organisms",
 "foresight_scaling": true,
 "_foresight_scaling_help": "If true, steps = 1 + int(foresight * max_steps). If false, always use max_steps",
 "pruning_threshold": 0.15,
 "_pruning_threshold_help": "Skip imagining branches with cumulative probability below this",
 "discount_factor": 0.9,
 "_discount_factor_help": "Gamma for discounting future imagined rewards"
 },
 "concept_system": {
 "concept_loss_weight": 0.15,
 "embed_dim": 64,
 "enabled": true,
 "num_key_compositions": 30,
 "utility_update_alpha": 0.15
 },
 "device": "cuda",
 "enabled": true,
 "inheritance": {
 "crossover_rate": 0.9,
 "enabled": true,
 "mutation_rate": 0.2
 },
 "initialization": {
 "deterministic": false,
 "seed": null
 },
 "language_model": {
 "attention": {
 "attention_dim": 32,
 "enabled": true,
 "num_heads": 4
 },
 "curriculum": {
 "enabled": true,
 "ml_quality": {
 "enabled": true,
 "high_quality_threshold": 0.55,
 "low_quality_threshold": 0.3,
 "max_sequence_length": 64,
 "min_sequence_length": 8,
 "sequence_length_step": 2
 },
 "sequence_lengths": {
 "stage_0": 8,
 "stage_1": 16,
 "stage_2": 32,
 "stage_3": 128
 },
 "vp_thresholds": {
 "stage_1": 0.5,
 "stage_2": 0.4,
 "stage_3": 0.3
 }
 },
 "enabled": true,
 "min_action_history": 1,
 "_min_action_history_help": "Read directly by language_teacher.py; mirrors teacher.min_action_history so action-grounded teaching starts after one recorded action.",
 "generation": {
 "max_length": 128,
 "temperature": 1.5,
 "vp_gate_threshold": 0.5
 },
 "knowledge_web": {
 "embedding_dim": 64,
 "enabled": true,
 "max_concepts": 6000,
 "grounded_web_expansion": {
 "enabled": true,
 "max_words": 64,
 "return_words": 64,
 "track_words": 64
 },
 "quality_control": {
 "confidence_growth_rate": 0.0016,
 "enabled": true,
 "exploration_decay_generations": 1000,
 "exploration_end": 0.08,
 "exploration_start": 0.45,
 "max_discoveries_per_generation": 40,
 "min_confidence_threshold": 0.25,
 "min_discovery_count": 2,
 "pruning_confidence_threshold": 0.2,
 "pruning_failure_rate": 0.7,
 "pruning_unused_generations": 100,
 "review_frequency": 100,
 "validation_required": true,
 "vp_boost_exploration": true,
 "vp_boost_threshold": 0.7
 }
 },
 "relationship_learning": {
 "enabled": true,
 "quality_evaluation": {
 "coherent_threshold": 0.45,
 "garbled_threshold": 0.25,
 "max_word_count": 25,
 "min_word_count": 2,
 "min_word_count_for_evaluation": 3,
 "relationship_strength_threshold": 0.4,
 "unk_ratio_threshold": 0.35
 },
 "semantic_guidance": {
 "enabled": true,
 "high_strength_boost": 0.3,
 "max_similar_words": 16,
 "min_strength_threshold": 0.15,
 "semantic_boost": 0.6
 }
 },
 "sequence": {
 "context_window": 32,
 "max_length": 128
 },
 "teacher": {
 "embedding_dim": 64,
 "enabled": true,
 "min_action_history": 1,
 "min_confidence": 0.1,
 "teaching_mastery_floor": 0,
 "_teaching_mastery_floor_help": "Grounded-mode mastery level at/above which the action-grounded teacher injects. 4 = only combat-graduated organisms (legacy); 0 = teach EVERY organism words for what it is doing (the whole unit learns together). Pool-side (context-memory), so it enriches diversity + grounds actions without bypassing per-organism mastery vocab caps - no climb stall.",
 "teaching_frequency": 1,
 "use_knowledge_web": true,
 "use_semantic_embeddings": true,
 "vocab_size": 50000,
 "staged_knowledge": {
 "_help": "Delay loading expanded knowledge web to let organisms build foundations with innate vocab first",
 "enabled": true,
 "delay_seconds": 0,
 "start_with_innate_only": false
 },
 "entity": {
 "enabled": true,
 "entity_id": "convergence.teacher.entity",
 "name": "Convergence Teacher",
 "model": "Smol-3B",
 "role": "bounded_packet_teacher",
 "controller_model": "nvidia/nemotron-3-super-120b-a12b:free",
 "controller_role": "hosted_cra_teacher_controller",
 "controller_surface": "/api/cra/chat",
 "controller_actuator": "/api/butterfly/chat",
 "frequency": 1,
 "max_packets_per_generation": 8,
 "max_organisms_per_generation": 128,
 "max_words_per_packet": 128,
 "max_receipts": 120,
 "os_search_enabled": true,
 "packet_sources": [
 "builtin",
 "external_cocoon_reference"
 ],
 "search_queries": [
 "teacher packet",
 "language training",
 "resource os diagnostics",
 "packet receipt"
 ],
 "autonomous_controller": {
 "enabled": true,
 "llm_provider": "openrouter",
 "startup_delay_seconds": 60,
 "interval_seconds": 60,
 "error_cooldown_seconds": 300,
 "hf_timeout_seconds": 120,
 "controller_plan_timeout_seconds": 75,
 "controller_cycle_deadline_seconds": 300,
 "controller_packet_dispatch_deadline_seconds": 180,
 "openrouter_base_url": "https://openrouter.ai/api/v1",
 "openrouter_site_url": "https://huggingface.co/spaces/tostido/Butterfly-Field-Station",
 "openrouter_app_name": "Butterfly Field Station",
 "openrouter_free_only": true,
 "openrouter_teacher_safe_only": true,
 "openrouter_model_min_context": 1000000,
 "openrouter_fallback_enabled": true,
 "openrouter_fallback_catalog_limit": 6,
 "openrouter_fallback_models": [
 "qwen/qwen3-coder:free",
 "nvidia/nemotron-3-super-120b-a12b:free"
 ],
 "qwen_max_tokens": 4096,
 "max_packets_per_cycle": 8,
 "max_organisms_per_packet": 128,
 "max_words_per_packet": 128,
 "notepad_recent_limit": 12,
 "notepad_query": "teacher language vocabulary hypothesis",
 "os_search_query": "teacher controller scientific notepad language teaching campaign illumination hegemony alliance commerce evidence",
 "os_search_limit": 16,
 "message_max_chars": 2048,
 "keyos_gizmo_enabled": true,
 "keyos_gizmo_base_url": "https://tostido-keyos-gizmo.hf.space",
 "keyos_gizmo_timeout_seconds": 20,
 "keyos_gizmo_max_chars": 12000,
 "keyos_gizmo_probe": "cocoon tool training semantic associations file workflow curriculum",
 "conductor_enabled": true,
 "conductor_focus": "illumination confederacy hegemony commerce coordination",
 "key_os_telemetry_enabled": true,
 "key_os_base_url": "https://tostido-k-os.hf.space",
 "key_os_timeout_seconds": 2,
 "key_os_payload_max_chars": 6000
 }
 }
 },
 "training": {
 "alpha": 0.5,
 "beta": 0.4,
 "gamma": 0.1,
 "vp_temperature_scale": true,
 "entropy_bonus": 0.015,
 "label_smoothing": 0.12,
 "language_epsilon_start": 0.4,
 "language_epsilon_end": 0.08,
 "language_epsilon_decay": 0.996,
 "_entropy_bonus_help": "Higher entropy for creative specialist - encourages more diverse, creative output",
 "_label_smoothing_help": "Slightly higher smoothing for creative exploration",
 "_language_epsilon_help": "Higher language epsilon for creative specialist - more random token exploration encourages novel combinations."
 },
 "vocabulary": {
 "max_size": 10000,
 "special_tokens": [
 "<PAD>",
 "<UNK>",
 "<START>",
 "<END>",
 "<VP_GATE>"
 ]
 }
 },
 "optimization": {
 "amp": {
 "enabled": true,
 "dtype": "auto",
 "_dtype_help": "'auto' detects optimal dtype (BF16 for Ampere+, FP16 for older). Can also be 'float16' or 'bfloat16'",
 "description": "Mixed precision training - 2-3x GPU speedup on Tensor Core GPUs"
 },
 "compile_mode": "max-autotune",
 "reuse_optimizers": true,
 "use_compile": true,
 "use_scripted_inference": false
 },
 "rewards": {
 "connection_failure": -0.2,
 "connection_success": 2.5,
 "fitness_improvement": 4.5,
 "resource_gain": 1,
 "resource_loss": -0.3,
 "survival": 2
 },
 "training": {
 "batch_size": 256,
 "early_stopping": {
 "enabled": true,
 "min_delta": 0.0001,
 "patience": 10
 },
 "enabled": true,
 "epsilon": 0.95,
 "epsilon_decay": 0.99,
 "epsilon_end": 0.01,
 "epsilon_start": 0.8,
 "gamma": 0.995,
 "language_reward_scaling": 0.6,
 "learning_rate": 0.005,
 "lr_scheduler": {
 "enabled": true,
 "gamma": 0.95,
 "min_lr": 0.0001,
 "step_size": 100,
 "type": "cosine",
 "warmup_steps": 100
 },
 "memory_size": 1000000000,
 "update_frequency": 1
 },
 "vp_aware_planning": {
 "base_boost": 0.18,
 "enabled": true,
 "high_threshold": 0.45,
 "low_threshold": 0.25,
 "strong_boost": 0.3
 },
 "checkpointing": {
 "_help": "Auto-save training state to prevent data loss. Checkpoint every N generations or minutes.",
 "enabled": false,
 "auto_save_interval_generations": 100,
 "auto_save_interval_minutes": 30,
 "max_checkpoints": 5,
 "checkpoint_dir": "data/neural_checkpoints",
 "include_experience_buffer": false,
 "include_optimizer_states": false,
 "compression": true,
 "auto_resume": false
 }
 },
 "quantum": {
 "entanglement_sensitivity": 2.5e-06,
 "fitness_weights": {
 "entanglement": 0.3,
 "entropy": 0.2,
 "measurements": 0.25,
 "superposition": 0.25
 },
 "initial_states": 80,
 "performance_thresholds": {
 "fitness_std_threshold": 0.3,
 "iteration_time_ms": 10,
 "memory_percentage": 5.0,
 "min_fitness_to_keep": 0.1
 },
 "probability_precision": 1e-06,
 "prune_check_interval": 40,
 "superposition_tolerance": 0.0005
 },
 "ray": {
 "enabled": false,
 "actor_pool_size": 2,
 "batch_inference_size": 64,
 "description": "Ray DISABLED",
 "fallback_on_error": true,
 "logging_level": "warning",
 "memory_management": {
 "actor_pool_lru_eviction": true,
 "cleanup_on_organism_death": true,
 "max_object_refs": 100
 },
 "num_cpus": 4,
 "num_gpus": 1,
 "object_store_memory": null,
 "parallelization_threshold": 100,
 "state_synchronization": {
 "consistency_model": "sequential",
 "max_state_age_ms": 100,
 "snapshot_strategy": "breath_cycle"
 },
 "training_threshold": 16
 },
 "rendering": {
 "enable_visualizations": true,
 "frame_rate": 15,
 "metric_display_precision": 6,
 "mode": "god",
 "performance_monitoring": false,
 "render_quality": "low",
 "resolution": [
 1280,
 720
 ],
 "text_interface": true,
 "visualization_update_precision": 0.001
 },
 "scikit": {
 "anomaly_detection": {
 "algorithm": "isolation_forest",
 "contamination": 0.18,
 "enabled": true,
 "n_estimators": 450
 },
 "clustering": {
 "algorithm": "hdbscan",
 "enabled": true,
 "min_cluster_size": 4,
 "min_samples": 1,
 "use_neural_embeddings": true
 },
 "concept_tracking": {
 "enabled": true,
 "persistence_threshold": 3,
 "stale_threshold": 10
 },
 "dimensionality_reduction": {
 "algorithm": "pca",
 "enabled": true,
 "n_components": 3
 },
 "enabled": true
 },
 "simulation": {
 "log_level": "INFO",
 "max_runtime": 31536000.0,
 "measurement_precision": 4,
 "performance_sampling_rate": 200,
 "save_interval": 60.0,
 "target_fps": 0,
 "_target_fps_help": "0 = unlimited speed, >0 = rate limit to N cycles/sec",
 "time_resolution_ms": 1.0,
 "world_snapshot_enabled": false,
 "world_snapshot_auto_restore": false,
 "world_snapshot_interval_minutes": 15,
 "world_snapshot_max_keep": 5
 },
 "vp_monitoring": {
 "adaptive_response": {
 "high_vp_threshold": 0.85,
 "streak_threshold": 3
 },
 "adaptive_thresholds": {
 "enabled": true
 },
 "adaptive_thresholds_enabled": true,
 "component_decomposition_enabled": true,
 "component_weights": {
 "evolution_pressure": 0.15,
 "network_coherence": 0.15,
 "phase_mismatch": 0.1,
 "quantum_entropy": 0.15,
 "trait_divergence": 0.15
 },
 "diagnostics_enabled": true,
 "stabilization": {
 "enabled": true,
 "history_size": 15,
 "max_jump": 0.1,
 "smoothing_factor": 0.4
 },
 "stabilization_enabled": true
 }
 }
 },
 "timestamp": 1783915997.912772,
 "measurement_precision": 6
}

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