Given a strategically complex board game, human players can quickly learn to devise strategies after playing a few rounds. Autonomous agents require similar capabilities in realistic interactive environments, yet existing agent benchmarks often fail to fully capture such strategic and evolving decision-making scenarios. We present **PTCG-Bench**, a benchmark built on the Pok’emon Trading Card Game (PTCG) that evaluates LLM agents at two complementary levels: (1) their decision-making performance within a single complex environment, and (2) their ability to self-evolving through accumulated experience. We further include a modular harness ablation to better interpret agent performance without conflating it with model capability. Our experiments show that, although LLM agents can achieve non-trivial gameplay performance, sustained and stable self-evolution remains challenging, and performance is sensitive to harness design. We hope that PTCG-Bench will facilitate future research on harness-aware and self-evolving agents in realistic interactive environments.

Automating repository-level software engineering tasks is a foundational challenge for autonomous code agents, largely due to the difficulty of configuring executable environments. However, manual configuration remains a labor-intensive bottleneck, necessitating a transition toward fully automated environment configuration. Existing approaches often rely on pre-defined artifacts or are restricted to specific programming languages, limiting their applicability to diverse real-world repositories. In this paper, we first propose **RAT** (RunAnyThing), a modular and extensible agent framework for fully automated configuration across programming languages on arbitrary repositories. RAT adopts a multi-stage pipeline that integrates language-aware abstraction, image initialization, specialized configuration toolset, and robust sandbox. Furthermore, to enable rigorous evaluation, we propose **RATBench**, a benchmark reflects the comprehensive coverage of real-world repositories. Extensive experiments demonstrate that RAT achieves state-of-the-art performance, improving Environment Setup Success Rate (ESSR) by an average of 36.1% over strong baselines.

Aligning large language models (LLMs) with multiple behavioral objectives from a certain corpus remains challenging, as joint optimization of attributes often leads to interference and trade-offs. While recent representation steering methods enable lightweight control over individual behaviors through hidden-state interventions, their composability for multi-attribute alignment remains underexplored. We present POLAR, Poly-attribute Alignment via Modular Representation Steering, a modular framework that composes independently trained alignment interventions within a frozen LLM. To mitigate conflicts between steering modules, we introduce calibrated and compatibility-aware intervention composition in hidden-state space. Experiments across heterogeneous alignment attributes show that POLAR achieves the best overall trade-off, with a 37.4% improvement in average rank over the strongest multi-attribute baseline while preserving response quality and controllability. The code and data will be publicly available.