Recent advancements in Large Language Models (LLMs) have revolutionized Text-to-SQL parsing, achieving remarkable success in static, single-turn query generation. However, a significant disparity remains between these academic benchmarks and real-world utility. In practical applications, such as financial auditing or business analytics, user intents are rarely static; they evolve dynamically through iterative refinement, necessitating not just information retrieval (SELECT) but continuous state manipulation (INSERT, UPDATE, DELETE). To bridge this gap, we introduce DySQL-Bench, a novel benchmark designed to rigorously evaluate LLMs within a dynamic interaction framework. Unlike varying manual curation efforts, DySQL-Bench employs a two-stage automated synthesis pipeline: transforming raw relational schemas into hierarchical logic trees to generate user-database interactions, followed by a rigorous verify-and-refine protocol that ensures 100% distinct correctness via human expert validation. We further propose an interactive evaluation environment simulating a triadic workflow involving an LLM-simulated user, the agent under test, and an executable database system. Spanning 13 diverse domains with 1,072 complex tasks, our experiments reveal that current powerful models struggle in this realistic setting. Notably, GPT-4o achieves only 58.34% overall accuracy and a meager 23.81% on the strict Pass^5 metric, highlighting the substantial challenges DySQL-Bench poses for the future of database agents.

Large language models (LLM) often hallucinate, and while adding citations is a common solution, it is frequently insufficient for accountability as users struggle to verify how a cited source supports a generated claim. Existing methods are typically coarse-grained and fail to distinguish between direct quotes and complex reasoning. In this paper, we introduce Generation-time Fine-grained Provenance, a task where models must generate fluent answers while simultaneously producing structured, sentence-level provenance triples. To enable this, we present ReFInE (Relation-aware Fine-grained Interpretability Evidence), a dataset featuring expert-verified annotations that distinguish between Quotation, Compression, and Inference. Building on ReFInE, we propose GenProve, a framework that combines Supervised Fine-Tuning (SFT) with Group Relative Policy Optimization (GRPO). By optimizing a composite reward for answer fidelity and provenance correctness, GenProve significantly outperforms 14 strong LLMs in joint evaluation. Crucially, our analysis uncovers a reasoning gap where models excel at surface-level quotation but struggle significantly with inference-based provenance, suggesting that verifiable reasoning remains a frontier challenge distinct from surface-level citation.

Training effective AI agents for real-world tool-use interactions requires data that faithfully captures the dynamics of human–agent collaboration. However, such data is scarce, and existing methods often resort to synthetic data generation. The inherently dynamic and complex nature of user–agent interactions makes ensuring data quality particularly challenging. Current verification approaches are typically entangled with the synthesis process itself, resulting in complicated implementations that undermine both reproducibility and scalability. To address this, we introduce Tool-Verifier-7B, a plug-and-play framework for data quality control in tool-use scenarios. Building on this verifier and our data synthesis strategy, we construct the Tool-Verify dataset, which contains 3,295 curated samples. To directly assess verifier performance, we further release Tool-V-Bench, a benchmark of 165 human-validated trajectories spanning diverse interaction complexities. Comprehensive experiments show that Tool-Verifier-7B surpasses Qwen2.5-72B-Instruct on Tool-V-Bench. Moreover, the Tool-Verify dataset achieves superior performance compared to the previous APIGen-MT dataset.

Chart question answering (CQA) has become a critical multimodal task for evaluating the reasoning capabilities of vision-language models. While early approaches have shown promising performance by focusing on visual features or leveraging large-scale pre-training, most existing evaluations rely on rigid output formats and objective metrics, thus ignoring the complex, real-world demands of practical chart analysis. In this paper, we introduce ChartMind, a new benchmark designed for complex CQA tasks in real-world settings. ChartMind covers seven task categories, incorporates multilingual contexts, supports open-domain textual outputs, and accommodates diverse chart formats, bridging the gap between real-world applications and traditional academic benchmarks. Furthermore, we propose a context-aware yet model-agnostic framework, ChartLLM, that focuses on extracting key contextual elements, reducing noise, and enhancing the reasoning accuracy of multimodal large language models. Extensive evaluations on ChartMind and three representative public benchmarks with 14 mainstream multimodal models show our framework significantly outperforms the previous three common CQA paradigms: instruction-following, OCR-enhanced, and chain-of-thought, highlighting the importance of flexible chart understanding for real-world CQA. These findings suggest new directions for developing more robust chart reasoning in future research.

According to the Test-Time Scaling, the integration of External Slow-Thinking with the Verify mechanism has been demonstrated to enhance multi-round reasoning in large language models (LLMs). However, in the multimodal (MM) domain, there is still a lack of a strong MM-Verifier. In this paper, we introduce MM-Verifier and MM-Reasoner to enhance multimodal reasoning through longer inference and more robust verification. First, we propose a two-step MM verification data synthesis method, which combines a simulation-based tree search with verification and uses rejection sampling to generate high-quality Chain-of-Thought (COT) data. This data is then used to fine-tune the verification model, MM-Verifier. Additionally, we present a more efficient method for synthesizing MMCOT data, bridging the gap between text-based and multimodal reasoning. The synthesized data is used to fine-tune MM-Reasoner. Our MM-Verifier outperforms all larger models on the MathCheck, MathVista, and MathVerse benchmarks. Moreover, MM-Reasoner demonstrates strong effectiveness and scalability, with performance improving as data size increases. Finally, our approach achieves strong performance when combining MM-Reasoner and MM-Verifier, reaching an accuracy of 65.3 on MathVista, surpassing GPT-4o (63.8) with 12 rollouts.