The Overall metric in the AesCode Infographics suite acts as an aggregator of two main dimensions: verifiable correctness (Rule) and visual quality (Visual). While it offers a synthetic view of performance, interpreting it in isolation masks specific failures in design or content. This analysis examines the evaluation architecture, the limits of cross-model comparability, and a protocol for reading these results with methodological rigor.

21 Oct 2026 ANALISIS

AA-LCR v1.1: Methodology, Metrics, and Limits of Long-Context Reasoning Evaluation

A technical audit of the AA-LCR benchmark: how 100 questions are constructed over 234 documents, the role of the Qwen3 judge model in scoring, and the execution conditions that ensure comparability between language models.

20 Oct 2026 ANALISIS

AesCode Infographics Layout: Visual Composition Metrics and Their Isolation in the Evaluation Suite

A functional analysis of the 'Layout' metric within the AesCode architecture. Unlike previous audits on 'Content' or 'Chart' that focused on the lack of access to the formula, this piece addresses the structural function of the metric: how separating layout evaluation (composition, hierarchy, spacing) from content evaluation (text, data) allows for diagnosing specific failures in infographic generation. It explains how an evaluation harness must isolate the 'design' variable from the 'information' variable so that a low Layout score is not confused with a content error, and defines the execution conditions (resolution, HTML/CSS output format) necessary for the metric to be reproducible.

19 Oct 2026 ANALISIS

AesCode Infographics Content: What It Measures, How It Runs, and Why Direct Comparison Is Blocked

A methodological audit of the 'Content' metric within the AesCode benchmark. This analysis defines its role in evaluating semantic fidelity of code-generated infographics, its integration with other visual metrics, and the specific limitations that prevent direct comparison with other industry standards.

17 Oct 2026 ANALISIS

AesCode Infographics Chart: what the evidence lets us verify—and what is missing for comparison

Public documentation describes the Chart channel, reports results, and provides evaluation code. But the datasets are not published, limiting independent reproduction and interpretation of the scores.

16 Oct 2026 ANALISIS

AesCode Infographics Bound: What It Measures, How It Runs, and What It Cannot Compare

A methodological audit of the 'Bound' metric within Microsoft's AesCode evaluation suite. This analysis examines its role as a programmatic constraint in infographic generation, its integration with other metrics, and the limitations it imposes on cross-model comparability.

16 Oct 2026

From benchmark to reproducible decision

A public benchmark provides guidance, but does not replace an evaluation with your data, tools, budget, and error tolerance.

Identify the exact version

A moving alias and a snapshot may produce different results. Record the model, date, and provider.

Reconstruct the conditions

Prompt, reasoning, tools, number of attempts, and budget must be equivalent for comparison.

Look for uncertainty

A small difference may disappear between runs. Keep samples, dispersion, and failures, not just the average.

Check transferability

Validate whether the improvement holds across languages, formats, and real cases close to your product.

Traceability

Published results and conditions declared by their sources. Figures are comparable only when version, configuration, metric and date match.

Primary and technical sources

References used to expand and review this page.

MARCOHELM: holistic and reproducible evaluationcrfm.stanford.edu↗ MULTIMODALVHELM: vision-language model evaluationcrfm.stanford.edu↗