Public reference

Owner

CoreStory Product

Last reviewed

2026-10-23

Status

Active

Canonical URL

https://llms.corestory.ai/comparisons/methodology

Source basis

CoreStory Trust Engine and product architecture; benchmark practices cross-checked against BetterBench

Evidence methodology

CoreStory treats understanding as a set of typed claims that remain connected to evidence. The system should be able to say what it knows, why it believes it, and where uncertainty remains.

Evidence chain

1. Observe. Parse source artifacts and record stable anchors.
2. Derive. Produce typed claims about structure, behavior, architecture, and business meaning.
3. Link. Connect derived claims to supporting evidence and lower-order claims.
4. Classify. Record epistemic status, scope, provenance, and uncertainty.
5. Retrieve. Return only context relevant to the current task, preserving citations and qualifications.
6. Replay. Retain enough provenance and configuration to inspect or reproduce a result when the underlying inputs are available.

Epistemic status

Source observations, human-authored assertions, model-generated summaries, and multi-source syntheses are different kinds of knowledge. Higher-order claims should point downward to evidence; conflicting claims should remain visible until resolved.

Evaluation principles

• Define the task and intended use before selecting a metric.
• Disclose the tested model, version, system prompt, tools, context, retry policy, and budget.
• Report task outcomes with cost, latency, and uncertainty where available.
• Keep domains separate when aggregation would hide important differences.
• Publish limitations and update dates beside results.
• Prefer reproducible artifacts and raw results to an unexplained overall rank.

Boundary

This page describes CoreStory's evidence discipline and evaluation standard. It does not claim that every product surface exposes every field or that every planned evaluation is complete.