Large language models (LLMs) are increasingly deployed as web-browsing agents, yet current benchmarks mostly evaluate explicit queries over clean factual evidence. We introduce Melonbench, a 665-item benchmark for coded entity resolution over noisy open-web user-generated content, instantiated with mid-to-long-tail public figures in Chinese entertainment blind-item discourse. Solving Melonbench requires agents to decode obfuscated clues, retrieve fragmented evidence, and identify the target entity. The benchmark is constructed through a five-stage semi-automatic pipeline with atomic event stratification, adversarial faithfulness auditing, and expert verification. Across 30 systems, the best non-search model reaches 25.1% accuracy and the best search-enabled system reaches 49.9%. Controlled comparisons of the same models with and without web search confirm consistent gains from web search, but also substantial remaining headroom. Diagnostic experiments identify semantic de-obfuscation and open-ended entity recall as key bottlenecks, and show that reasoning-centric search can outperform report-oriented agents. An 80-item finance pilot provides preliminary evidence of transfer beyond entertainment within a Chinese-language setting. Melonbench offers a challenging initial testbed for search agents under intentional obfuscation and noisy evidence. The dataset is publicly available at https://huggingface.co/datasets/janehaha/MelonBench.

Instruction tuning plays a crucial role in enhancing large language models (LLMs) to better understand complex user instructions. While various data selection and revision methods have been explored to optimize instruction tuning datasets, they face two main challenges: unreasonable pruning of potentially valuable low-quality data and the persistence of noise or semantic drift during revision. To address these issues, we propose a novel automated iterative framework for instruction data optimization. Our framework introduces Instruction Quality Differentiation to identify valuable high-quality and low-quality data across multiple dimensions. For low-quality data, we propose a Feedback-driven Iterative Refinement mechanism with an “evaluate-refine-review” process and design an Output Alignment module to improve data quality. Experiments on seven public benchmark datasets show that our framework outperforms state-of-the-art methods, achieving 2.09% and 2.60% improvements on the Alpaca and Dolly datasets, respectively, with high data efficiency. Our code and data are available at the anonymous link https://github.com/surihuhang/From-Selection-to-Refinement–-Iterative-Optimization-for-Instruction-Data.

Visual scale recognition is a fundamental aspect for humans to perceive physical quantities in the real world, and it is crucial for enabling human-like intelligence in multimodal large language models (MLLMs). However, existing benchmarks typically focus on a single type of quantity (e.g., time) or a specific format (e.g., dials), lacking a comprehensive evaluation of scale recognition capabilities. To address these problems, we propose ScaleBench, a visual scale recognition benchmark built using images from COCO, Open Images, and Flickr, designed to comprehensively evaluate the scale recognition capabilities of MLLMs. To ensure high data quality, we develop detailed annotation guidelines and procedures, resulting in a total of 6,574 annotated samples. Based on this benchmark, we evaluate multiple closed-source and open-source MLLMs. Experimental results reveal that the best-performing model achieves only 42.60% accuracy, far lower than the 97.40% of humans. Furthermore, we conduct in-depth experimental analyses and provide future research directions. Our benchmark and implementation codes are available at https://github.com/Sonder-hang/ScaleBench.

Multimodal large language models (MLLMs) have achieved remarkable progress in recent years, yet their ability to perform left–right reasoning in mirror contexts—a fundamental element of spatial cognition—remains underexplored. To address this gap, we introduce MirrorQA, a manually constructed benchmark with 5,549 samples, designed to evaluate MLLMs’ capability to distinguish left from right from a subject-centered perspective. MirrorQA is built through a three-stage pipeline (annotation, verification, and final review) to ensure high-quality labeling. Comprehensive evaluations on both open- and closed-source MLLMs show that even the best-performing models achieve only 65.40% accuracy, far below the 99.28% accuracy of humans. These results highlight substantial challenges in current MLLMs when reasoning about left and right, and point to promising directions for future research. MirrorQA and its code are publicly available at anonymous link https://github.com/stargazer-zeno/MirrorQA.