Validation asks
Does the specification, environment, evaluator, resource profile, and reference evidence form a valid measurement system?
Analysis standardCause before score
Evaluation explains what happened and why.
Applied separates measurement validity from system behavior. The analysis begins with preserved artifacts, tests competing explanations, and ends in a decision that is no broader than the evidence supports. This page defines the method; it does not present a completed Project A or Project B result.
Four different questions.
Professional evaluation work becomes clearer when validation, evaluation, analysis, and release are not collapsed into one score.
Evaluation asks
Given a valid measurement system, what behavior occurred, which requirements were met, and what does the result establish?
Analysis asks
Which explanation best fits the artifacts, what controlled intervention could distinguish alternatives, and how confident is the classification?
Release asks
Is the evidence sufficient to accept, revise, quarantine, reject, rerun, or regrade the result?
Analysis sequence.
Every conclusion retains the evidence, alternatives, intervention, and limitation that produced it.
Observe
Preserve the raw outcome, trajectory, artifacts, evaluator output, timestamps, and execution conditions before interpreting them.
Verify measurement
Recheck contract alignment, environment health, evaluator controls, reference behavior, resources, and infrastructure.
Map requirements
Evaluate each requirement separately and distinguish complete, partial, missing, invalid, and unobservable evidence.
Generate explanations
Consider specification, environment, evaluator, resource, infrastructure, and implementation or model causes.
Intervene
Change one controlled factor where appropriate: version, budget, dependency, evaluator rule, input, or implementation behavior.
Classify
Record the supported cause, rejected alternatives, confidence, limitations, and whether the attempt remains comparable.
Decide
Accept the result, revise the package, quarantine the attempt, reject the work, or initiate repair and regression.
Two analysis tracks.
Project A defines task-level analysis. Project B is a planned application of the same discipline to a synthetic operating case.
Project AValidation in progress
Terminal-task evaluation
- Capability question
- Which real engineering behavior should the task distinguish?
- Primary evidence
- Trajectory, commands, tool results, repository state, final artifacts, and requirement outcomes.
- Controls
- Reference, valid alternative, no-op, shortcut, incomplete, boundary, adversarial, and resource checks.
- Key risk
- Attributing a low score to model weakness when the contract, environment, evaluator, or budget is defective.
- Decision unit
- One attempt against one frozen task, environment, evaluator, and harness configuration.
Project BPlanned case
Synthetic compliance evaluation
- Capability question
- Can a system apply the correct versioned rule, use sufficient evidence, calculate the result, route exceptions, and respect human approval?
- Primary evidence
- Synthetic input record, governing source, effective date, calculation trace, supporting documents, decision, and approval state.
- Controls
- Clear cases, missing evidence, conflicting sources, boundary dates, precedence, calculation errors, and human-review triggers.
- Key risk
- Treating a plausible final answer as valid when its rule, evidence, date, calculation, or authority chain is wrong.
- Decision unit
- One synthetic project, rule version, worker or record, period, expected disposition, and evidence chain.
Outcome taxonomy.
Each result receives a narrow classification and an explicit next action.
Pass
All material requirements are satisfied with valid, inspectable evidence under comparable conditions.
Capability failure
The measurement system is valid and the submitted behavior fails one or more discoverable requirements.
Evaluation defect
The specification, evaluator, reference, or expected decision does not validly represent the intended capability.
Environment or resource defect
Execution conditions prevent a valid attempt or make comparison unsafe.
Infrastructure incident
A platform or service issue invalidates the run independently of the evaluated behavior.
Insufficient evidence
The current record cannot support a defensible classification.
Downstream training relevance.
A validated evaluation can inform training-data design. It does not establish that training occurred or improved a model.
Supervised fine-tuning (SFT)
Accepted references or solved trajectories may support token-level supervised learning. Supervised-loss analysis requires the exact model, tokenizer, target sequence, masking policy, and per-token negative log-likelihood. Task acceptance alone cannot supply that result.
Reinforcement learning with verifiable rewards (RLVR)
Use requires a deterministic, reproducible reward that distinguishes valid from invalid outcomes and resists shortcuts. A verified evaluator is a prerequisite for RLVR, not evidence that a useful policy update occurred.
Reinforcement learning from human feedback (RLHF)
Use requires calibrated human preference comparisons or an explicitly trained reward model. Rubric review, pass or fail labels, and independent acceptance are quality controls; by themselves, they are not RLHF evidence.
Policy optimization
PPO, GRPO, or related RL analysis requires repeated rollouts under a frozen configuration, a defined reward channel, within-group reward variation, advantage estimates, and policy and optimizer logs. None is inferred from an evaluation score.
What the dashboard will publish.
Only validated releases will show task-level or case-level outcomes. Until then, the Labs pages publish the method, status, evidence requirements, and current boundaries.
- Project A view
- Requirement outcomes, failure domain, attempt conditions, corrections, and release state
- Project B view
- Synthetic scenario, expected decision, observed result, diagnosis, correction, regression, and impact decision