Training and experiment protocol

This protocol defines the financial-language, temporal-event and graph training program. The current public-record release has acquired source evidence and executed retrieval baselines. These baselines use lexical statistics; supervised language-model training and the larger representation-learning curriculum remain separate experiments. For the completed structured-loan baseline training, see measured results and the result manifest. The curriculum below defines the separate event and language-model training methodology.

Staged curriculum

Stage	Objective	Required evidence
0. Data qualification	Validate rights, source versions, identity partitions, dates, and financial consistency	Dataset manifest, exclusion report, leakage checks, annotation quality.
1. Descriptor adaptation	Learn counterparty roles and transaction purpose from adjudicated examples	Bilingual labels, weak-label provenance, unseen-entity evaluation.
2. Event pretraining	Learn typed financial histories without outcome labels	Masked-field losses by field, reconstruction error, data exposure accounting.
3. Relationship learning	Learn candidate matching and source-event linkage	Independently labeled pairs, hard negatives, temporal and tenant boundaries.
4. Downstream adaptation	Fit underwriting, change detection, and forecasting tasks	Task-specific outcomes, frozen splits, matched baselines.
5. Calibration and explanation	Calibrate predictions and generate evidence-grounded summaries	Held-out calibration, faithful explanations, abstention and numerical tests.
6. Independent validation	Challenge every material claim	Frozen predictions, reviewer findings, reproducibility and intended-use approval.

Losses and sampling

A candidate pretraining objective combines masked categorical cross-entropy, robust numeric reconstruction, and contrastive consistency between genuinely linked source views. Weight losses by task relevance and measured scale, not by the count of convenient fields. Report each loss separately so a dominant easy field cannot hide failure on a critical one. Mask at field, event, and source levels. A mask must not leave the target trivially visible in a duplicate source record. Cross-source alignment needs verified positive pairs and plausible hard negatives. For a forecasting task, restrict attention to the past. Bidirectional reconstruction is permissible inside a historical window whose complete contents were available at the cutoff; it cannot access later observations. Sample by subject and activity regime to prevent a few high-volume accounts from dominating. Cap or weight repeated near-identical records. Keep related entities in the same partition. Fit vocabulary, normalization, imputation, and retrieval indexes using the allowed training data only.

Supervision and adaptation

Use weak provider labels for candidate generation or auxiliary training, then measure their noise against independent adjudication. Teacher-model labels are not independent ground truth. Preserve label source and confidence; hold out a human-reviewed set that was never used to revise prompts or teachers. Compare frozen embeddings plus a simple head, full fine-tuning, and parameter-efficient adaptation such as LoRA. Train equivalent from-scratch and no-pretraining controls. Reuse neither competitor labels nor restricted model outputs without appropriate permission.

Model selection and compute

Freeze the search space, resource cap, random seeds, early-stopping criterion, and primary endpoint before the final test. Use training for fitting, development for selection, calibration for probability mapping and operating thresholds, and a locked holdout for the final report. If resources require nested cross-validation, document the nesting and all data exposure. Record wall time, accelerator type/count, precision, batch size, accumulated tokens or events, optimizer, schedule, clipping, checkpoint policy, and failures. Distinguish model inference time from retrieval, normalization, reconciliation, and human review. Compare resource-matched alternatives as well as best-performing alternatives. Do not extrapolate a CPU prototype into production throughput or cost.

Candidate search space

The following is a bounded starting design for an authorized pilot, not a description of completed production runs. Dataset qualification and a resource estimate must precede execution.

Component	Proposed candidates	Selection constraint
Event encoder	Width 64, 128, or 256; depth 2 or 4; four attention heads	Compare under a fixed optimization budget, with the same allowed history.
Sequence construction	Recent windows plus hierarchical daily/monthly summaries	Evaluate lost rare events and material obligations; record exact truncation.
Masked pretraining	Field mask rates 0.15 or 0.30; independent source-dropout ablation	Prevent duplicate-view leakage; report loss per field and source.
Optimization	AdamW; learning rate 0.0001 or 0.0003; effective batch 64 or 128 subjects	Select on development loss and task metrics, never the final holdout.
Downstream head	Regularized linear model, boosting, or shallow neural head	Hold the representation and cohort constant when attributing gains.
Graph integration	No graph, explicit graph-derived features, or learned neighborhood aggregation	Use only permitted as-of neighbors; retain an entity-disjoint test.
Language adaptation	Frozen encoder, structured extraction, or parameter-efficient adaptation	Follow the separate annotation protocol.

Do not launch the Cartesian product blindly. Predeclare a staged search or a bounded optimization method and record trials that fail. Use at least three fixed training seeds for shortlisted neural configurations. Keep equal-compute and equal-data comparisons separate. Record the configuration and data exposure for every completed run, including unsuccessful candidates.

Objective specification

For a candidate event encoder, define the training loss as a weighted sum of categorical reconstruction, numeric reconstruction, and verified cross-view alignment:

L = lambda_category * L_category
  + lambda_numeric * L_numeric
  + lambda_alignment * L_alignment

Normalize each term over its eligible, observed targets. Missing fields are not zero-valued targets. Use a robust numeric loss with source-specific training-set scaling and report unscaled errors in meaningful units. Choose and freeze the weights on development data; include each objective’s removal as an ablation. No specific weight or claimed optimum is asserted before that experiment. For a binary outcome head, record the outcome definition, horizon, censoring eligibility, and loss weighting. Class weighting or negative subsampling changes probability interpretation, so calibrate on a representative held-out cohort and report its prevalence. For censored follow-up, compare a survival formulation with an explicitly mature binary cohort rather than labeling unobserved outcomes as negative.

Run manifest

{
  "experiment_id": "research-candidate-001",
  "evidence_class": "proposed_method",
  "dataset_manifest_sha256": null,
  "split_manifest_sha256": null,
  "base_checkpoint_revision": null,
  "training_purposes_approved": false,
  "code_commit": null,
  "configuration_sha256": null,
  "outcome_definition_version": null,
  "primary_metric": "task_specific",
  "test_access_count": 0,
  "run_status": "not_executed"
}

Nulls explicitly denote unavailable artifacts. Replace them with original run evidence when an experiment occurs; do not generate plausible identifiers or historical timestamps. Material changes after seeing the holdout require a new holdout or an explicitly exploratory result.

Research program

Data and models

Risk analysis

Evaluation and evidence

Training and experiment protocol

Staged curriculum

Losses and sampling

Supervision and adaptation

Model selection and compute

Candidate search space

Objective specification

Run manifest

​Staged curriculum

​Losses and sampling

​Supervision and adaptation

​Model selection and compute

​Candidate search space

​Objective specification

​Run manifest

Staged curriculum

Losses and sampling

Supervision and adaptation

Model selection and compute

Candidate search space

Objective specification

Run manifest