Decision: Unifying Benchmarking with MQM, WCAG 2.2 Accessibility, Self-Recognition Ablations, and LLM Inference TCO

Context#

Recent updates focused on advancing self-recognition capabilities, expanding domain knowledge assets, and refining the user interface. To convert these efforts into measurable product gains, we are standardizing how we evaluate quality, accessibility, robustness, and cost.

Decision Summary#

Adopt a unified benchmarking framework that: (1) uses MQM for language quality, (2) aligns accessibility testing with WCAG 2.2, (3) institutionalizes ablation testing for self-recognition/inner speech components, and (4) tracks LLM inference total cost of ownership (TCO) as a first-class KPI.

Alignment and Rationale#

• MQM provides a structured error typology for human-in-the-loop evaluation and surpasses simple acceptance sampling for diagnosing specific quality issues.
• WCAG 2.2 alignment strengthens UI accessibility and reduces regressions related to color/contrast and interaction patterns.
• Ablation testing clarifies the causal contribution of inner speech/self-recognition mechanisms, improving explainability and reliability.
• TCO tracking ensures performance/cost trade-offs are explicit, enabling informed decisions on model/runtime choices and experience quality.
• These choices align with trustworthy AI principles (valid/reliable performance, transparency, and risk-based governance).

Disagreements (with reasons)#

• “MQM is heavier than sample-based acceptance”: True; however, MQM yields actionable error categories that accelerate root-cause fixes.
• “WCAG 2.2 adds overhead in UI cycles”: Yes; but it reduces rework and improves inclusivity—key for long-term product quality.
• “Ablations slow feature velocity”: They add controlled experiments, but avoid costly misattribution and unstable behavior in production.
• “TCO metrics constrain experimentation”: Constraints are intended; they illuminate invisible costs and prevent unsustainable scaling.

Recommended Decision Range#

• Minimum: MQM on high-risk language features, targeted WCAG 2.2 checks on critical UI flows, ablations for the most impactful self-recognition modules, and weekly TCO snapshots.
• Target: MQM across major language outputs, WCAG 2.2 verification embedded in routine UI QA, ablation test suite as part of reliability gating, and TCO metrics in feature rollouts.
• Maximum: Full MQM coverage with trend dashboards, comprehensive WCAG 2.2 regression suite, ablations as a standard pre-merge requirement for relevant components, and TCO targets tied to release criteria.

Risks (top 3) and Mitigations#

1) Evaluation fatigue and cycle-time creep

• Mitigation: Focus MQM on top error categories; prioritize WCAG 2.2 checks for critical flows; keep ablation scope minimal viable to isolate effects.

2) Misinterpretation of metrics (quality vs cost trade-offs)

• Mitigation: Pair MQM with qualitative review; publish TCO context (traffic mix, latency SLOs) alongside raw numbers.

3) Data governance and privacy exposure in evaluation logs

• Mitigation: Apply privacy-by-design practices and align with relevant data protection obligations; restrict sensitive logging and ensure reviewer guidance is documented.

Assumptions (top 3)#

• MQM-trained reviewers are available and can maintain consistent error annotation.
• UI work items can be mapped to WCAG 2.2 success criteria without blocking delivery.
• Self-recognition/inner speech components are modular enough to support clean ablation toggles.

KPI Impact Assumptions#

• MQM will surface 2–3 dominant error categories that, once fixed, reduce reopens and review time.
• WCAG 2.2 alignment decreases accessibility-related defects and improves usability metrics in key flows.
• Targeted ablations reduce behavioral regressions and increase stability under varied contexts.
• TCO visibility curbs cost growth while maintaining latency and throughput objectives.

Next Steps#

• Define MQM rubric and reviewer handbook; select priority language outputs.
• Map critical UI flows to WCAG 2.2 criteria; add checks to the QA checklist.
• Stand up an ablation test harness for self-recognition/inner speech components with pass/fail gates.
• Instrument TCO metrics (cost, latency, throughput) and add them to the release dashboard.
• Schedule a 2-week checkpoint to review findings and adjust scope.

Brief Implementation Notes#

• MQM: Use a standardized error taxonomy (e.g., Terminology, Accuracy, Linguistic conventions, Fluency, Style) to enable consistent annotation and trend analysis.
• Ablations: Include removal/degradation tests of inner speech/self-dialogue to isolate contribution to task performance and explainability.
• Accessibility: Prioritize color/contrast, focus management, input modalities, and error feedback in line with WCAG 2.2.
• TCO: Track per-output cost, latency distributions, and utilization to guide performance/cost trade-offs.