Evaluating LLMs’ instruction-following ability in multi-topic dialogues is essential yet challenging. Existing benchmarks are limited to a fixed number of turns, susceptible to saturation and failing to account for users’ interactive experience. In this work, we propose a novel framework featuring a three-layer tracking mechanism and a query synthesis agent to mimic sequential user behaviors. Grounded in Flow Theory, we introduce process-centric metrics and terminate a conversational evaluation only upon exhausting user patience. Leveraging this framework, we present EvolIF, an evolving benchmark covering 12 constraint groups. Our analysis reveals deficiencies in failure recovery and fine-grained instruction following, with performance stratification becoming evident as conversational depth increases. GPT-5 demonstrates the most sustained resilience, maintaining a 66.40% stability score, outperforming Gemini-3-Pro by 5.59%, while other models lag behind.

Recent advancements in large language models (LLMs) have shifted focus toward scaling inference-time compute—improving performance without retraining the model. A common approach is to sample multiple outputs in parallel, and select one of these as the final output. While existing work has focused on English and specific domains, we study how to robustly scale inference-time compute in a multilingual, multi-task setting: spanning open-ended generations, math and translation tasks, for open models at 8B and 111B scale, across seven languages. Our findings highlight the need for tailored sampling and selection strategies. We propose novel solutions tailored for this multi-faceted inference scenario, demonstrating notable gains across languages and tasks. Our methods achieve an average +6.8 jump in win-rates for 8B models on m-ArenaHard-v2.0 prompts in non-English languages against proprietary models like Gemini. At larger scale, our 111B model shows a +9.0 improvement with just five samples compared to single-sample decoding. These results emphasize the importance of language- and task-aware approaches to democratize inference-time improvements.