{
  "schema_version": 1,
  "study_id": "dial_lexical_differentiation_v1",
  "title": "Differentiate Before You Integrate? When Embedding Factorization Becomes an Inductive Bias in Data-Limited Language Acquisition",
  "frozen_at_utc": "2026-07-19T21:14:38Z",
  "fixed_compute_deadline_utc": "2026-07-20T09:14:38Z",
  "status": "prospective_before_any_dial_training_or_outcome_access",
  "primary_question": "Does temporarily protecting complementary relational signals at lexical access reduce cross-talk and change acquisition before shared Transformer integration?",
  "claim_boundary": {
    "positive": "A temporary input-level relational scaffold changes the acquisition trajectory relative to an information-matched overlapping-support control.",
    "excluded": [
      "persistent cognitive modules",
      "human neural equivalence",
      "grounded conceptual representations",
      "universal architectural optimality",
      "leaderboard superiority",
      "Poincare geometry"
    ]
  },
  "conditions": {
    "aligned": "Real H/O/A relations in three disjoint orthogonal rank-128 input supports.",
    "shared": "The exact same real H/O/A relations in three fixed dense overlapping rank-128 supports; each block is orthonormal and the concatenated condition number must be below 3.",
    "shuffled": "Aligned supports with one identity permutation shared across all three relation views and restricted within frozen matching strata."
  },
  "operational_relations": {
    "H": "Hierarchical-distributional Brown radial-angular targets.",
    "O": "Order-sensitive directional relative-position PPMI at offsets -3,-2,-1,+1,+2,+3.",
    "A": "Associative-distributional symmetric PPMI."
  },
  "architecture": {
    "backbone": "dense PRISM GPT-BERT",
    "hidden_size": 384,
    "rank_allocation": [128, 128, 128],
    "embedding_tables": 1,
    "transformer_streams": 1,
    "shared_transformer_from_layer": 1,
    "input_output_embeddings_tied": false,
    "final_factor_readout": false,
    "parser": false,
    "attention_masks_from_priors": false,
    "block_specific_normalization": false
  },
  "scaffold": {
    "loss": "L_LM + lambda(t) * sum_k alpha_k L_k",
    "lambda_schedule": "lambda0 * max(0, 1 - words_seen / 10000000)",
    "withdrawal_words": 10000000,
    "calibration_minibatch": 0,
    "alpha_rule": "equalize initial H/O/A gradient norms on the input embedding table",
    "lambda0_rule": "aggregate auxiliary gradient norm divided by LM gradient norm equals 0.25",
    "outcome_tuning": false,
    "orthogonality_penalty": false,
    "cross_covariance_role": "diagnostic_only",
    "structured_initialization": "0.75 fixed relational solution plus 0.25 paired isotropic initialization, renormalized to the registered initializer RMS"
  },
  "design": {
    "paired_seeds": [0, 1, 2, 3, 4, 5],
    "conditions_per_seed": 3,
    "fresh_models": 18,
    "terminal_exposure_words": 40000000,
    "learning_rate_schedule_words": 100000000,
    "checkpoints_words": [1000000, 2000000, 3000000, 4000000, 5000000, 6000000, 7000000, 8000000, 9000000, 10000000, 20000000, 30000000, 40000000],
    "same_within_pair": [
      "non-embedding initialization",
      "document and batch order",
      "dropout stream",
      "edge batches",
      "optimizer",
      "gradient clipping",
      "corpus",
      "tokenizer",
      "relation arrays",
      "auxiliary schedule"
    ],
    "intended_difference": "support overlap or the frozen shared identity permutation only"
  },
  "primary_endpoint": {
    "battery": ["BLiMP", "COMPS", "EWoK", "Entity Tracking"],
    "model_outputs": "native LM probabilities without fine-tuning or probes",
    "trajectory": "equal-weight composite AUC over log exposure from 1M through terminal exposure",
    "contrast": "aligned minus shared",
    "falsification_gate": "aligned greater than shuffled",
    "normalization": "independent frozen baseline only; otherwise equal-accuracy mean"
  },
  "validity_gates": {
    "lm_loss_noninferiority_nats_per_token": 0.02,
    "minimum_shuffle_direction": "aligned greater than shuffled",
    "random_basis_controls": 32,
    "random_basis_matching": ["rank", "energy"],
    "maximum_failed_seeds_per_condition": 1,
    "systematic_throughput_or_clipping_difference_allowed": false
  },
  "mechanism": {
    "checkpoint_ages_words": [1000000, 10000000, 40000000],
    "intervention": "input-block donor swaps matched by frequency, subword length, norm, coverage and degree",
    "controls": "32 Haar-random equal-rank equal-energy bases",
    "joint_interventions": "all 2^3 H/O/A swaps",
    "interaction": "non-additive HxOxA contrast",
    "gradient_audit_minibatches": 64,
    "gradient_audit": "LM versus H/O/A cosine and conflict rates",
    "imposed_not_result": "between-support orthogonality in aligned"
  },
  "cognitive_bridge": {
    "primary": "AoA prediction with frequency, length, contextual diversity and lexical class controls, including within-frequency-bin effects",
    "secondary": "cross-validated log1p self-paced reading time, grouped by the 205 sentences, with frequency, length, position, spillover and paired shared-condition surprisal controls",
    "exploratory": "ERP only; no post-hoc mapping from subspaces to components",
    "human_causality_claimed": false
  },
  "statistics": {
    "unit": "paired training seed",
    "primary_test": "two-sided exact paired sign-flip",
    "minimum_attainable_two_sided_p": 0.03125,
    "report": ["every seed", "paired effect", "confidence interval"],
    "no_checkpoint_pseudoreplication": true
  },
  "compute_fallback": {
    "decision_input": "preflight throughput only, before outcome access",
    "if_predicted_wall_time_to_40m_exceeds_hours": 8.5,
    "fallback_terminal_exposure_words": 30000000,
    "node_failure": "retain complete balanced pairs only, first five then first four seeds",
    "forbidden": "asymmetric condition truncation or metric-driven adaptation"
  },
  "priority_order": [
    "primary acquisition composite",
    "AoA",
    "input swaps",
    "gradient conflict",
    "reading time",
    "full zero-shot for one preregistered model",
    "SuperGLUE",
    "ERP",
    "embedding visualization"
  ],
  "artifact_policy": {
    "model_checkpoints": "all registered ages plus 40M main and recovery on Hugging Face",
    "priors_tokenizer_corpus": "pinned Hugging Face revisions",
    "study_archive": "data and machine-readable provenance only",
    "adaptive_result_access_before_all_lineages_sealed": false
  },
  "retrospective_context_only": [
    "FACTORIZED tournament",
    "FACTORIZED mechanism study",
    "factorized resource substitution study",
    "STRUCTURED-DS tournament"
  ]
}