Knowledge reliability is recall plus restraint.
A useful factual model must answer what it knows, decline what it does not, and avoid converting weak familiarity into confident fabrication.
Yotta Content can build original questions from permissioned authoritative sources in software, business, finance, legal, medical, science, or client-specific domains. Records preserve source version, validity window, answer normalization, acceptable aliases, evidence span, difficulty, topic, and the kinds of plausible confusion that make an incorrect guess tempting.
Question generation is followed by source and temporal audit.
Authoritative evidence
Every item points to a stable, permissioned source with title, publisher, date, location, and validity interval.
Unambiguous target
Prompt, canonical answer, aliases, answer type, domain, topic, difficulty, and ambiguity review.
Plausible wrong beliefs
Confusable entities, stale facts, unit swaps, overgeneralizations, and near-neighbor concepts create informative errors.
Private and time-aware
Sources, prompts, and answer keys remain outside training, with exposure and refresh logs for changing facts.
Scoring must make hallucination visibly expensive.
| Outcome | Training interpretation |
|---|---|
| Correct answer, justified confidence | Positive factual recall and calibration signal |
| Correct answer, low confidence | Knowledge present but under-calibrated |
| Abstention on unknown item | Positive restraint signal where policy permits |
| Abstention on known item | Missed utility; separate from hallucination |
| Incorrect confident answer | High-severity hallucination and calibration failure |
| Unsupported elaboration around a correct core | Faithfulness error even if the direct answer matches |
A client reward can mirror the benchmark's core principle—reward correct knowledge and punish bad guesses—while adding domain-specific abstention policy and explicit evidence requirements.
The best training views are contrastive.
- Answer view: response, normalized answer, confidence, abstention flag, and correctness.
- Evidence view: permitted source excerpt, claim-to-evidence mapping, and unsupported additions.
- Calibration view: reliability bucket, domain slice, expected loss, and review threshold.
- Preference view: correct concise answer versus fluent hallucination; calibrated abstention versus unjustified guess.
- Repair view: critique the false claim, provide corrected evidence, and verify the revised response.
What an AA-Omniscience-shaped program includes.
- Original source-grounded question families with stable IDs, answer aliases, provenance, difficulty, and temporal validity.
- Domain-balanced train, calibration, and private evaluation splits with leakage and duplication audits.
- Correctness, abstention, hallucination, confidence, and optional evidence-faithfulness reward components.
- Response records, claim-level labels, confidence diagnostics, preference pairs, critiques, and repaired answers.
- Refresh workflow for changed facts plus source/version manifests that make evaluation dates interpretable.
Benchmark relationship and evidence boundary.
This is a proposed independent data program inspired by the factual recall and calibration capability measured by AA-Omniscience. It is not Artificial Analysis data, a replica, an affiliation.
Primary reference: Artificial Analysis AA-Omniscience.