The rapid progress of large language models (LLMs) has increased the demand for efficient and reliable evaluation of question answering (QA) systems. Existing evaluation methods either rely on rule-based matching with shallow semantic understanding or adopt LLM-as-a-Judge approaches that incur high cost and latency while offering limited error interpretability. Accordingly, we propose HiEval, a curriculum learning based hierarchical framework for QA task evaluation that supports both quick scoring and fine-grained error analysis. HiEval contains a quick scoring model (HiEval-QS) that predicts three-level correctness labels, and an error analysis model (HiEval-EA) that identifies incorrect responses into five error types. HiEval incorporates a class-balanced focal loss to handle label imbalance, experience replay to prevent forgetting, and contrastive unlikelihood optimization to improve error discrimination. We also construct two large-scale human-annotated evaluation datasets collected from 50 QA-related datasets, covering 8 task types and release two challenging benchmarks. Extensive experiments show that HiEval achieves state-of-the-art performance on both quick scoring and error analysis tasks, outperforming all baseline methods, including GPT-5, while being approximately 25× faster.

Large language models (LLMs) are increasingly deployed in high-stakes domains reliant on tabular data (e.g., financial reporting), where undetected logical inconsistencies such as mismatched totals and components can lead to critical errors. Yet, the ability of LLMs to identify such inconsistencies remains poorly understood, hindered by the absence of standardized evaluation frameworks and cell-level annotated datasets. To bridge this gap, we propose a comprehensive benchmark SEC-Fintables comprising 103,395 real-world and error-injected table instances, alongside a novel evaluation protocol that decomposes inconsistency detection into granular sub-tasks. Through evaluating both proprietary and open-source LLMs on SEC-Fintables, we find that contemporary LLMs exhibit only partial competence in detecting logical inconsistencies. Our study reveals key limitations and improvement opportunities for LLMs. We believe SEC-Fintables and our evaluation protocol can serve as a practical resource for advancing reliable inconsistency detection of LLMs in tabular reasoning. We release SEC-Fintables at https://github.com/XIEFOX/SEC-Fintables.

Knowledge-Based Visual Question Answering (KB-VQA) requires grounding visual queries to external knowledge beyond directly observable content in images.While recent multi modal large language models (MLLMs) show strong perceptual abilities, they struggle on KB-VQA tasks requiring groundings from both fine-grained entity and evidence levels.Most existing multi-modal retrieval augmented generation (MM-RAG) methods tightly couple entity discrimination and section-level evidence ranking into a single re-ranking stage, leading to high cost and limited generalization.In this work, we revisit existing MM-RAG solutions from a workflow perspective and argue both entity-level and fact-level groundings are key bottlenecks.We observe that although MLLMs often fail under open-ended entity naming, they can better identify the correct entity when selecting from a small set of candidate names.Based on this insight, we propose a simple and training-free identify-before-answer IBA framework that decouples entity identification from section-level re-ranking.Our approach prompts an MLLM to select high-confidence entities using only candidate names, followed by an off-the-shelf textual re-ranker for evidence selection.Experiments on Encyclopedic-VQA and InfoSeek show that our method consistently outperforms fine-tuned multi-modal re-ranking baselines while reducing training and inference complexity.Additional analyses reveal that the improvements arise not only from better entity identification, but also from selecting more informative evidence once correct entity is fixed.Our implementation is made public to ease reproducibility