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.

Tabular data forms the backbone of high-stakes decision systems in finance, healthcare, and beyond. Yet industrial tabular datasets are inherently difficult: high-dimensional, riddled with missing entries, and rarely labeled at scale. While foundation models have revolutionized vision and language, tabular learning still leans on handcrafted features and lacks a general self-supervised framework. We present MaskTab, a unified pre-training framework designed specifically for industrial-scale tabular data. MaskTab encodes missing values via dedicated learnable tokens, enabling the model to distinguish structural absence from random dropout. It jointly optimizes a hybrid supervised pre-training scheme—utilizing a twin-path architecture to reconcile masked reconstruction with task-specific supervision—and an MoE-augmented loss that adaptively routes features through specialized subnetworks. On industrial-scale benchmarks, it achieves +5.04% AUC and +8.28% KS over prior art under rigorous scaling. Moreover, its representations distill effectively into lightweight models, yielding +2.55% AUC and +4.85% KS under strict latency and interpretability constraints, while improving robustness to distribution shifts. Our work demonstrates that tabular data admits a foundation-model treatment—when its structural idiosyncrasies are respected.

Agentic RAG, which decomposes a complex question into dependency-aware sub-questions, has become the dominant paradigm for multi-hop QA. Yet existing systems can only repair errors at the failed step or simply return to the previous step and retry, and thus fail to handle two pervasive failure modes: inaccurate decomposition, where the plan is flawed before execution begins, and error propagation, where an upstream mistake surfaces downstream. We propose that addressing both requires global self-correction, the ability to identify the root cause of a failure and repair it at the right location. To this end, we propose **LoCoRAG**, a globally self-correcting agentic RAG framework. LoCoRAG maintains a persistent dual memory through a two-stage thinker-generator pipeline, separating what the corpus contains from what the system has concluded. Then, a memory-grounded failure localization module attributes each failure to specific causes and pinpoints its true origin. Together, LoCoRAG drives a self-correcting global replan that selects a matched repair, from local refinement to upstream rollback with downstream regeneration. Across existing multi-hop QA benchmarks, LoCoRAG achieves SOTA performance on all datasets and all metrics, with a 10.8 points average LLM-Acc gain over the strongest baseline on 3-hop-or-deeper scenarios.

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.