Parameter-efficient fine-tuning (PEFT) enables low-cost adaptation of large language models but often suffers from limited representational flexibility. To address this, we incorporate a Mixture-of-Experts (MoE) design and propose Efficient and Expressive split-path experts that enhance specialization while maintaining low resource overhead. Split-Path Adaptive Representation Mixture-of-Experts (SparMoE) replaces discrete hard routing with a soft routing and fully-activated mixture, enabling stable optimization. Each expert is parameterized as a split-path modulation module, consisting of a scaling path that promotes expert specialization and a bias path that preserves expert-specific signals. This design significantly enhances expressive capacity while maintaining strict parameter efficiency and architectural compatibility with PEFT. Extensive evaluations on GLUE, GSM8K, MBPP, and a text rewriting task from SmolTalk show that our approach consistently outperforms or matches state-of-the-art PEFT methods under comparable parameter budgets, achieving a favorable trade-off between adaptability and efficiency.

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.