IBIB: A Protocol for Measuring Enterprise AI Systems by Serving Route, Not Model Identifier
IBIB protocol evaluates enterprise AI systems via serving routes, not model identifiers, improving reliability measurement.
Key Findings
Methodology
The IBIB protocol consists of three components: gold-blind capability-binding preflight, reliability-inclusive scoring rules, and score-blind adjudication. Key algorithms include capability-binding preflight (Algorithm 1), blind adjudication and recovery, fail-closed comparability, and suite-stratified task-set bootstrapping.
Key Results
- Result 1: Across 11 systems, two single-route runs with identical weights failed distinct predicates of the binding gate, while a third passed, demonstrating measurable capability availability.
- Result 2: Serving route choice improved precision from 77.38% to 82.54%, with an interval of [0.11, 10.60].
- Result 3: Four of seven suites saturated within a six-system band, with differences driven by database work and multi-tab joins.
Significance
This study addresses the limitations of traditional benchmarks that rely solely on model identifiers, offering a more comprehensive method for evaluating enterprise AI systems. By binding serving routes, IBIB provides a more accurate reflection of real-world system capabilities, aiding enterprise AI procurement and deployment decisions.
Technical Contribution
IBIB introduces serving route binding into AI system evaluation, proposes reliability-inclusive scoring, and defines task-binding protocols. Unlike existing benchmarks, IBIB evaluates not only model capabilities but also the impact of serving routes, filling a critical gap in model-identifier-based benchmarks.
Novelty
IBIB is the first protocol to explicitly evaluate serving route binding, overcoming the limitations of traditional benchmarks and introducing a novel reliability-inclusive scoring method.
Limitations
- Limitation 1: The protocol relies on a closed task set, limiting its applicability to open-domain tasks.
- Limitation 2: It lacks a human baseline, restricting the commercial interpretability of absolute scores.
- Limitation 3: Multi-user states and real-time retrieval scenarios are not covered.
Future Work
Future research could extend IBIB to dynamic task sets and multi-user scenarios, enhancing its applicability. Additionally, integrating the protocol with real-time enterprise AI deployments could improve its practicality.
AI Executive Summary
The IBIB protocol addresses the limitations of traditional enterprise AI evaluations that rely solely on model identifiers. By binding serving routes, IBIB provides a more accurate reflection of real-world system capabilities. Its core components include capability-binding preflight, reliability-inclusive scoring, and score-blind adjudication. Experiments show that serving routes significantly impact system performance, with precision improvements from 77.38% to 82.54%. Additionally, IBIB highlights the critical role of database work and multi-tab joins in capability evaluations.
IBIB's technical contributions include introducing serving route binding into AI evaluation, proposing reliability-inclusive scoring, and defining task-binding protocols. Unlike traditional benchmarks, IBIB evaluates both model capabilities and serving route impacts, filling a critical gap in the field.
Despite its strengths, IBIB has limitations, including reliance on a closed task set and lack of multi-user state coverage. Future research could explore dynamic task sets and real-time enterprise AI deployments to enhance its utility further.
Deep Analysis
Background
Traditional AI benchmarks typically evaluate systems based on model identifiers, ignoring the impact of serving routes on system capabilities. However, enterprise AI systems rely on more than just model weights; factors like serving routes, precision, and output contracts significantly affect performance. None of the 18 audited benchmarks address this issue, leading to discrepancies between evaluation results and real-world capabilities.
Core Problem
The core problem is that traditional benchmarks cannot distinguish between model limitations and serving route constraints. This measurement error makes it difficult for enterprises to accurately assess system capabilities, especially in complex scenarios like multi-image requests or long-text generation.
Innovation
IBIB's core innovations include:
- �� Serving route binding: Verifies whether routes meet evaluation contracts.
- �� Reliability-inclusive scoring: Incorporates failures into scores to avoid overestimating capabilities.
- �� Score-blind adjudication: Ensures unbiased recovery decisions.
- �� Task binding: Defines the evaluation object as an eight-tuple including model, route, precision, and more.
Methodology
The IBIB protocol involves:
- �� Capability-binding preflight: Verifies serving routes against ten predicates.
- �� Initial scoring rules: Records all failures and incorporates them into scores while excluding unsupported capabilities.
- �� Score-blind adjudication: Handles non-OK responses without visibility into correctness.
- �� Task binding: Evaluates systems as tuples of model, route, precision, etc.
Experiments
The experiments include 128 locked tasks and 987 assertions across documents, spreadsheets, charts, tools, and databases. The study compares 11 systems, analyzing the impact of serving routes on capabilities through multiple route-binding tests.
Results
Results show serving routes significantly affect system capabilities. For example, two routes with identical weights failed distinct binding gate predicates, while a third passed. Serving route choice improved precision from 77.38% to 82.54%. Database work and multi-tab joins were key factors in capability evaluations.
Applications
IBIB is ideal for evaluating enterprise AI systems, especially in complex task scenarios. Its results provide reliable insights for AI procurement and deployment, improving transparency and accuracy in system selection.
Limitations & Outlook
IBIB's limitations include:
- �� Dependence on a closed task set, limiting open-domain applicability.
- �� Lack of multi-user state and real-time retrieval coverage.
- �� Absence of a human baseline, restricting commercial interpretability.
Plain Language Accessible to non-experts
Imagine you're ordering food at a restaurant. Traditional evaluations only look at the menu item names, ignoring the chef's skills, ingredient quality, and service process. IBIB is like a taste test, ensuring the dish meets your expectations. By verifying serving routes, IBIB ensures AI systems perform as evaluated in real-world deployments.
ELI14 Explained like you're 14
Think of playing a video game. Traditional evaluations just look at the character's name, like 'Warrior' or 'Mage.' But IBIB checks the character's gear, skills, and controls to ensure they can actually beat the boss! It's like giving every character a full check-up to see if they're truly hero material!
Glossary
Serving Route
The complete execution path from input to output in an AI system, including model, tool calls, and parsers.
Used to define the evaluation object.
Capability-Binding Preflight
A process to verify if serving routes meet evaluation contracts.
Ensures routes can execute evaluation tasks.
Score-Blind Adjudication
Deciding recovery strategies without visibility into correctness.
Prevents scoring bias.
Reliability-Inclusive Scoring
A scoring rule that incorporates failures to reflect true capabilities.
Used to measure real-world system performance.
Task Binding
The process of defining the evaluation object as a tuple of model, route, precision, etc.
Establishes the basic evaluation unit.
Open Questions Unanswered questions from this research
- 1 How can IBIB be extended to support open-domain task evaluations?
- 2 How can IBIB integrate with real-time enterprise AI deployments for greater utility?
Applications
Immediate Applications
Enterprise AI Procurement
Helps enterprises evaluate AI systems' real-world capabilities more accurately, optimizing procurement decisions.
Complex Task Evaluation
Applicable for capability measurement in scenarios like multi-image requests and long-text generation.
Long-term Vision
Dynamic Task Evaluation
Developing protocols for dynamic task sets to adapt to evolving enterprise needs.
Abstract
Enterprises deploy systems, not checkpoints. Usable capability depends jointly on weights, serving route, precision, output contract, and harness, yet all 18 audited benchmarks score advertised model identifiers. We treat this as measurement error and give a protocol that makes it reportable. It has three parts. A gold-blind capability-binding preflight verifies that a route can execute the evaluation contract before any task reaches it; a reliability-inclusive first-pass scoring rule keeps failure in the score while keeping unsupported capability out; and adjudication is structurally score-blind. We call the protocol IB2 and release its algorithms, classification tables, request contract, and manifest schemas. Its reference instantiation, 128 locked tasks and 987 assertions over document, spreadsheet, chart, tool and database work, stays sealed: the procedure is the artifact, not the corpus. Across eleven systems, four results. Capability availability is measurable: two complete single-route runs on identical weights later failed distinct predicates of the finalized binding gate, while a third passed that gate before a fresh run. The advertised identifier exposed neither limit. Discrimination is not uniform: four of seven suites saturate under a six-system band, with the spread almost entirely from governed database work and multi-tab joins, so we report interval-backed resolution groups, not ranks; two of the nominal five-label output's four cuts fail multiplicity adjustment. Serving-arm choice moved one declared revision and precision from 77.38 to 82.54, paired interval [0.11,10.60], though the arms differ in access mode, harness generation, and the serving tool-call parser, and harness generation is a property of our evaluator, not any endpoint. Excluding failed responses from denominators changes the point ordering, so reliability inclusion changes a conclusion, not its wording.