IBIB: A Protocol for Measuring Enterprise AI Systems by Serving Route, Not Model Identifier
- URL: http://arxiv.org/abs/2609.10494v1
- Date: Wed, 09 Sep 2026 17:31:29 GMT
- Title: IBIB: A Protocol for Measuring Enterprise AI Systems by Serving Route, Not Model Identifier
- Abstract summary: All 18 audited benchmarks score advertised model identifiers.<n>A gold-blind capability-binding preflight verifies that a route can execute the evaluation contract before any task reaches it.<n>A reliability-inclusive first-pass scoring rule keeps failure in the score while keeping unsupported capability out.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- Clean Engineering, Unstable Measurement: A Preregistered Reliability Failure of Black-Box LLM Observers on Shared Endpoints [4.41537705949485]
Language-model judges now gate training data, score generations, and drive leaderboards.<n>We audited that assumption in two preregistered campaigns with every threshold fixed in advance.<n>Across 52,988 audited request attempts, same-window repeat rankings agreed at Spearman 0.400 against a required 0.90.<n>We distill the evidence into a three-level snapshot-identity ladder, eight design rules, and a reporting checklist.
arXiv Detail & Related papers (2026-09-03T17:59:43Z) - Ranked by the Matcher: A Reproducibility Audit of Knowledge Graph Extraction from Threat Reports [5.580052437190499]
We reimplement the stated matching rule for only five of twelve inspected systems.<n>Ten system outputs on shared documents under eight protocols reverses eleven of forty-five pairwise orderings.<n>On an external, human-adjudicated set, no mechanical matcher---lexical, embedding, or entailment---agrees with the reviewers more than seven times in ten.
arXiv Detail & Related papers (2026-09-01T04:45:44Z) - Recognition Without Enforcement: Configuration-Dependent Failures in LLM Agent Instruction Arbitration and External Control [0.0]
LLM agents arbitrate among instructions from system prompts, users, memory, and tools, but this arbitration cannot be assumed to enforce trust boundaries.<n>We identify a recognition-enforcement gap: source-format features are linearly decodable from model activations, and models can explicitly identify forged authority when prompted.<n>We treat model self-arbitration as a capability rather than a security boundary and implement an external reference monitor combining authenticated source routing with capability-gated tool execution.
arXiv Detail & Related papers (2026-08-28T16:34:05Z) - From Traceability to Justifiability: Accountability Structures in Agentic Software Engineering [0.0]
We measure, from public material only, whether AI records can express a claim and whether they hold where declared.<n>Across 188 double-graded cells we found no platform whose default record emits a content identity of a behavioral system.<n>An instrument computes realized assurance depth from a pipeline's published exhaust alone and compares it with the declared depth.
arXiv Detail & Related papers (2026-08-21T16:45:00Z) - The Recall Trap: A Recall-Maximizing Retriever Configuration Reduces Issue Resolution in Fixed-Budget Code Context [45.88028371034407]
We report a controlled case study in code repair, not a new phenomenon.<n>We toggle one flag (one-chunk-perfile deduplication) on an otherwise identical stack.<n>We map where it holds: it reverses on a lexical BM25 (3.2pp, significant cross-paradigm interaction)<n>We show that at a tight fixed budget, do not hard-deduplicate by file, and A/B packing policies against the task, not the metric flag was tuned.
arXiv Detail & Related papers (2026-08-14T19:22:50Z) - QuoteBench: How Matched Scores Can Hide Command-Path Failures [30.480838412827907]
Execution scores alone cannot distinguish command-generation errors from failures introduced after generation.<n> QuoteBench measures this boundary with exact final-state validation on 56 one-shot tasks from 14 incident-derived families.<n> Evaluations of command-issuing agents should report the model configuration, generation contract, execution path, operating point, and final-state rather than treat a matched score as an intrinsic model property.
arXiv Detail & Related papers (2026-08-13T17:57:20Z) - Harness-IF: Evaluating Instruction Following Across Instruction Surfaces in Coding Agents [29.96375986740068]
Harness-IF scores operational rules one at a time from execution evidence.<n>AP-Acc scores only rules labeled as opposing unprompted defaults.
arXiv Detail & Related papers (2026-08-12T07:07:57Z) - Judging Is Not Enumerating: Silent Omissions in LLM-Authored Acceptable Sets [13.85834524293015]
We measure the capability that role assumes and find it lacking under the protocol the role is usually deployed with.<n>Models judge whether a candidate belongs far better than they author the set itself.
arXiv Detail & Related papers (2026-08-02T05:00:44Z) - Fantastic Adaptive Taxonomies and How to Use Them [100.20614173157708]
AdaMAST converts execution traces into a compact, evidence-grounded failure taxonomy.<n>No code is hand-authored, no trace human-annotated.<n>In agent-system search, taxonomy-coded diagnoses of failed candidates outperform free-form.
arXiv Detail & Related papers (2026-07-17T17:41:16Z) - What Accuracy and Gradient Cosine Miss: Evaluating Feedback Alignment via Scale Stability, Reference Validity, and Depth Utility [48.10132234701036]
We propose a diagnostic evaluation protocol based on three checks -- scale stability, reference validity, and depth utility -- together with per-layer rather than aggregate cosine reporting.<n>Across multiple architectures and methods, our protocol identifies all failures with wide calibration margins.
arXiv Detail & Related papers (2026-06-19T06:04:17Z) - Distributional Energy-Based Models for Uncertainty-Aware Structured LLM Reasoning [40.342912574072024]
Large Language Models produce structured outputs such as travel plans or code solutions.<n>Individual reasoning steps may appear correct while the output as a whole violates budgets, fails test cases, or contradicts earlier deductions.<n>We propose a learned quality scorer with deterministic analytical constraint penalties for verifying structured LLM outputs.
arXiv Detail & Related papers (2026-05-15T17:08:27Z) - Can Agent Benchmarks Support Their Scores? Evidence-Supported Bounds for Interactive-Agent Evaluation [8.049844623361725]
Interactive agent benchmarks map an agent run to a binary outcome through outcome checks.<n>These checks rely on surface level signals or fail to capture the agent's actual action path.<n>We introduce an outcome evidence reporting layer for existing benchmarks.
arXiv Detail & Related papers (2026-05-11T12:20:15Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.