Beyond training-time optimization, scaling test-time computation has emerged as a key paradigm to extend the reasoning capabilities of Large Language Models (LLMs). However, most existing methods adopt a rigid Planning-before-Trial (PbT) policy, which inefficiently allocates test-time compute by incurring planning overhead even on directly solvable problems. We propose Planning-after-Trial (PaT), an adaptive policy for code generation that invokes a planner only upon verification failure. This adaptive policy naturally enables a heterogeneous model configuration: a cost-efficient model handles generation attempts, while a powerful model is reserved for targeted planning interventions. Empirically, across multiple benchmarks and model families, our approach significantly advances the cost-performance Pareto frontier. Notably, our heterogeneous configuration achieves performance comparable to a large homogeneous model while reducing inference cost by approximately 69%.

With the rapid advancements in Large Language Models (LLMs), an increasing number of studies have leveraged LLMs as the cognitive core of agents to address complex task decision-making challenges. Specially, recent research has demonstrated the potential of LLM-based agents on automating GUI operations. However, existing methodologies exhibit two critical challenges: (1) static agent architectures struggle to adapt to diverse GUI application scenarios, leading to inadequate scenario generalization; (2) the agent workflows lack fault tolerance mechanism, necessitating complete process re-execution for GUI agent decision error. To address these limitations, we introduce COLA, a collaborative multi-agent framework for automating GUI operations. In this framework, a scenario-aware agent Task Scheduler decomposes task requirements into atomic capability units, dynamically selects the optimal agent from a decision agent pool, effectively responds to the capability requirements of diverse scenarios. Furthermore, we develop an interactive backtracking mechanism that enables human to intervene to trigger state rollbacks for non-destructive process repair. Experiments on the GAIA dataset show that COLA achieves competitive performance among GUI Agent methods, with an average accuracy of 31.89%. On WindowsAgentArena, it performs particularly well in Web Browser (33.3%), Media & Video (33.3%), and Windows Utils (25.0%), suggesting the effectiveness of specialized agent design and dynamic strategy allocation. The code is available at https://github.com/Alokia/COLA-demo.