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jung-geun
813433000a
Migrate the package and examples to the tensor-native PyTorch implementation, add benchmark evidence, and add the guarded post-training convergence protocol with TensorBoard progress monitoring and hash-verified recovery. Constraint: Preserve one-shot official-test sealing and auditable research artifacts Rejected: Commit local .omc runs and downloaded datasets | multi-gigabyte runtime state is machine-local Confidence: high Scope-risk: broad Not-tested: Production CUDA run on pieroot-server
8912 lines
239 KiB
JSON
8912 lines
239 KiB
JSON
{
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"full_mnist_protocol_version": "1.0.0",
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"pso_version": "4.0.0",
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"torch_version": "2.13.0",
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"hardware": {
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"platform": "macOS-26.5.2-arm64-arm-64bit",
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"system": "Darwin",
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"machine": "arm64",
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"processor": "arm",
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"python_version": "3.11.15",
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"torch_version": "2.13.0",
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"pso_version": "4.0.0",
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"device_type": "mps",
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"device_str": "mps",
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"mps_available": true
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},
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"device": "mps",
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"timestamp": "2026-09-02 00:16:58",
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"baseline_path": "benchmark_results/pso_v4_tuning.json",
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"subset_study_path": "benchmark_results/pso_v4_epoch_convergence.json",
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"sample_counts": {
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"train_samples": 60000,
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"test_samples": 10000
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},
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"pca_provenance": {
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"n_components": 32,
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"whiten": true,
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"random_state": 42,
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"fit_scope": "official_train_split_60000_only",
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"train_samples_fit": 60000,
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"test_samples_transformed": 10000,
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"explained_variance_ratio_sum": 0.743599534034729
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},
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"data_fingerprint": "3586f0b2d63ab546",
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"fitness_evaluation_contract": {
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"selector": "full",
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"fitness_size": null,
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"train_samples_per_particle_per_epoch": 60000,
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"particle_evaluations": 144000,
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"particle_sample_evaluations": 8640000000,
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"test_samples_per_checkpoint": 10000
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},
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"candidate_label": "am_b0.06_s0.5",
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"config": {
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"method": "adaptive_moment",
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"velocity_limit_ratio": 0.025,
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"mutation_swarm": 0.02,
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"negative_swarm": 0.0,
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"particle_min": -3.0,
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"particle_max": 3.0,
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"boundary_strategy": "reflect",
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"initialization": "model_noise",
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"initial_position_noise": 0.05,
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"evaluation": "full",
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"convergence": "none",
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"refinement": "none",
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"c0": 1.49618,
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"c1": 1.49618,
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"w_min": 0.7298,
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"w_max": 0.7298,
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"method_options": {
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"moment_step_size": 0.5,
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"moment_beta1": 0.9
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},
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"n_particles": 120,
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"batch_size": 60000,
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"renewal": "loss",
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"checkpoint_interval": 20
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},
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"plugins": {
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"movement": {
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"title": "Adaptive Path-Moment PSO",
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"source": null,
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"fidelity": "experimental",
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"gradient_required": false,
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"options": {
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"c0": 1.49618,
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"c1": 1.49618,
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"w_max": 0.7298,
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"moment_blend": 0.06,
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"moment_beta1": 0.9,
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"moment_epsilon": 1e-08
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}
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},
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"initialization": {
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"title": "Model Weight + Uniform Noise Initialization",
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"source": null,
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"fidelity": "canonical",
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"gradient_required": false,
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"options": {
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"noise": 0.05
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}
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},
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"evaluation": {
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"title": "Full Dataset Evaluation",
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"source": null,
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"fidelity": "canonical",
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"gradient_required": false,
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"options": {}
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},
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"convergence": {
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"title": "No Convergence Action",
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"source": null,
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"fidelity": "canonical",
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"gradient_required": false,
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"options": {}
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},
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"refinement": {
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"title": "No Refinement",
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"source": null,
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"fidelity": "canonical",
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"gradient_required": false,
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"options": {}
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}
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},
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"timing_scope": "fit_only_after_full-evaluation_two-epoch_warmup",
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"diagnostics": {
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},
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"descriptive_subset_comparison": {
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"subset_study_path": "benchmark_results/pso_v4_epoch_convergence.json",
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"disclaimer": "The full and subset studies optimize different training objectives and the evaluation plugin changes RNG consumption (all 60,000 train samples versus a fixed 2,000-sample fitness subset). Matching-seed and matching-epoch comparisons are descriptive, not an exact paired causal isolation.",
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"matching_epoch_deltas": {
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"summary": {
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240
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