Ali Ramezani-Kebrya

Daniel Kaiser, Arnoldo Frigessi, Ali Ramezani-Kebrya et al.

Large language models trained for reasoning trade off inference tokens against accuracy, yet standard evaluations report only final accuracy, obscuring where tokens are spent or wasted. We introduce a trace-optional framework that decomposes token efficiency into interpretable factors: completion under a fixed token budget (avoiding truncation), conditional correctness given completion, and verbosity (token usage). When benchmark metadata provides per-instance workload proxies, we further factor verbosity into two components: mean verbalization overhead (tokens per work unit) and a coupling coefficient capturing how overhead scales with task workload. When reasoning traces are available, we add deterministic trace-quality measures (grounding, repetition, prompt copying) to separate degenerate looping from verbose-but-engaged reasoning, avoiding human labeling and LLM judges. Evaluating 25 models on CogniLoad, we find that accuracy and token-efficiency rankings diverge (Spearman $ρ=0.63$), efficiency gaps are often driven by conditional correctness, and verbalization overhead varies by about 9 times (only weakly related to model scale). Our decomposition reveals distinct bottleneck profiles that suggest different efficiency interventions.

Kjetil Indrehus, Adrian Duric, Changkyu Choi et al.

Document Visual Question Answering (DocVQA) requires vision-language models to reason not only about what information in a document is relevant to a question, but also where the answer is grounded on the page. Existing DocVQA models entangle question-relevant evidence and answer localization and operate largely as black boxes, offering limited means to verify how predictions depend on visual evidence. We propose CoExVQA, a self-explainable DocVQA framework with a grounded reasoning process through a chain-of-explanation design. CoExVQA first identifies question-relevant evidence, then explicitly localizes the answer region, and finally decodes the answer exclusively from the grounded region. Prediction via CoExVQA's chain-of-explanation enables direct inspection and verification of the reasoning process across modalities. Empirical results show that restricting decoding to grounded evidence achieves SotA explainable DocVQA performance on PFL-DocVQA, improving ANLS by 12% over the current explainable baselines while providing transparent and verifiable predictions.