Extend pre-flight performance estimation beyond document-centric tasks

Extend multimodal pre-flight performance estimation from document-centric tasks to non-visual, text-dominant applications such as general-purpose question answering, code generation, and open-ended creative tasks.

Background

The paper evaluates the Document-Reasoning Balancer (DRB) exclusively on document-centric tasks, including document classification, tabular question answering, and form-checkbox extraction. These tasks depend substantially on visual layout and structural complexity, so the estimator’s demonstrated effectiveness does not establish that it can predict reasoning performance in domains where inputs are primarily textual.

The authors explicitly identify extending pre-flight performance estimation to non-visual, text-dominant applications—including general-purpose question answering, code generation, and open-ended creative tasks—as an unresolved direction for future work. Such an extension would test whether the approach generalizes beyond multimodal document understanding.

References

Extending pre-flight performance estimation to non-visual, text-dominant applications such as general-purpose QA, code generation, or open-ended creative tasks remains an open direction for future work.

— Can a Lightweight Multimodal Model Estimate LLM Reasoning Performance? A Study for Compute-Optimal Document Inference  (2608.18591 - Ahmad et al., 19 Aug 2026) in Limitations section

It does not establish that the preview representation is sufficient a priori. This is an empirical question, which we evaluate through the feature ablations and the observed utility headroom recovered relative to the Per-Sample Oracle.

— Beyond One-Size-Fits-All: Sample-Adaptive Strategy Routing for Vision Token Pruning in MLLMs  (2609.10346 - Liang et al., 9 Sep 2026) in Appendix, Section “Theoretical Motivation: Utility Prediction from a Compact Representation,” paragraph “Scope of the analysis”