Mobile GUI agents deployed in real settings must work across different applications that support the same functionality. Most existing benchmarks test each task in only one app, so a high score can mean the agent understands the task, or only that it knows that particular app. We introduce AnyAppBench, a category-controlled live Android benchmark that evaluates cross-application generalization while keeping the user goal fixed. It spans 10 functional categories, 100 task templates, and 520 task–application pairs over 52 applications. Agents run from raw instructions and with app-independent sub-goals, and a VLM judge labels every failed run under a fixed failure taxonomy whose reliability is measured by human annotation. We find that, across 13 agents, success on the original application does not transfer reliably to new applications with the same goal. Furthermore, providing high-level sub-goal decomposition produces only small, category-dependent changes that do not close the gap, and the mix of failure types changes with the target interface. Based on those insights, we believe the AnyAppBench benchmark provides an important stepping stone toward robust real-world deployment of mobile GUI agents. Our code, data and the leaderboard can be found at the project website https://anyappbench.github.io/.

Concurrent multi-agent LLM systems increasingly rely on role-specialized Workers that act in parallel, but effective coordination depends not only on what Workers generate, but also on what task state each Worker observes before generating. Existing systems usually fix this observation interface through workflow rules, retrieval heuristics, or full-history sharing, so information flow cannot adapt as task state and feedback evolve. We introduce Dynamic Context Allocation (DCA), which treats per-Worker observation control as a trainable coordination problem for a fixed-role team. An RL-trained Context Allocator maintains a bounded Context Board and decides before each round which evolving task-state entries should be retained, revised, removed, or exposed to each Worker, rather than learning agent selection, execution order, or workflow topology. We evaluate DCA on cooperative code generation, Customer Relationship Management (CRM) lead qualification, and MultiWebGen, a new benchmark for multi-agent full-stack web development. Across these settings, DCA improves over fixed-protocol coordination and fixed-context multi-agent RL baselines, and it outperforms all evaluated frontier single-agent LLMs on CRM lead qualification despite using smaller role-specialized Workers.

A critical component of the current generation of language models is preference alignment, which aims to precisely control the model’s behavior to meet human needs and values. The most notable among such methods is Reinforcement Learning with Human Feedback (RLHF) and its offline variant Direct Preference Optimization (DPO), both of which seek to maximize a reward model based on human preferences. In particular, DPO derives reward signals directly from the offline preference data, but in doing so overfits the reward signals and generates suboptimal responses that may contain human biases in the dataset. In this work, we propose a practical application of a diversity-seeking RL algorithm called GFlowNet-DPO (GDPO) in an offline preference alignment setting to curtail such challenges. Empirical results show GDPO can generate far more diverse responses than the baseline methods that are still relatively aligned with human values in dialog generation and summarization tasks.