Unsolvability Ceiling in Multi-LLM Routing: An Empirical Study of Evaluation Artifacts

A comprehensive empirical study reveals that reported inefficiencies in multi-LLM routing systems are substantially inflated by evaluation artifacts rather than genuine model limitations. Researchers found that LLM-as-a-judge biases, output truncation, and format mismatches account for a significant portion of measured failures, suggesting current routing cost-quality tradeoff estimates significantly overstate the actual unsolvability ceiling.

This research addresses a fundamental problem in optimizing multi-model LLM systems: determining whether routing limitations stem from actual model capabilities or evaluation methodology. The study's analysis of 206,000 query-model pairs across major benchmarks reveals three critical evaluation artifacts that distort performance assessments. LLM judges systematically favor verbose responses over correct concise ones, fixed generation budgets truncate longer outputs, and strict format matching rejects valid alternative answer structures.

The findings expose a cascading problem: routers trained on corrupted signals collapse toward selecting the cheapest models regardless of query difficulty, achieving only 79% optimal performance on smallest-tier models. This represents a 13-17 percentage point efficiency loss that operators attribute to inherent model limitations when it actually reflects training corruption. The research demonstrates this through rigorous controls including random-feature and shuffled-label experiments.

For the broader AI infrastructure industry, these findings challenge current assumptions about LLM routing economics. Organizations designing multi-model systems have likely overestimated headroom constraints and underestimated achievable cost savings through better evaluation protocols. The study's recommendations—dual-judge validation, exact-match grounding, and cost-sensitive training objectives—provide concrete methodological improvements. However, implementing these changes requires significant engineering effort and could reveal that existing routing decisions were substantially suboptimal.

Looking forward, the research highlights growing pains in AI infrastructure standardization. As organizations scale multi-model deployments, evaluation methodology becomes as critical as model selection itself. The study suggests future work should focus on developing robust, artifact-free evaluation frameworks before deploying routing systems at scale.

• →Evaluation artifacts inflate measured unsolvability by systematically favoring verbose over correct answers, causing routers to misallocate queries to cheaper models.
• →Router training signals collapse under corrupted evaluation metrics, reducing optimization efficiency by 13-17 percentage points despite standard methodologies.
• →Dual-judge validation and exact-match anchoring can substantially reduce false unsolvability measurements across diverse benchmarks and model families.
• →Current multi-LLM routing headroom estimates are substantially inflated, potentially underutilizing expensive higher-capability models unnecessarily.
• →Routers trained on standard evaluation metrics select smallest-tier models 79% of the time, suggesting evaluation corruption rather than true cost-quality tradeoffs.