Tool-integrated reasoning (TIR) enables LLM agents to solve tasks through planning, tool use, and iterative revision, but outcome-only reinforcement learning in this setting suffers from sparse, delayed rewards and weak step-level credit assignment. In long-horizon TIR trajectories, an early irrecoverable mistake can determine success or failure, making it crucial to localize the first irrecoverable step and leverage it for fine-grained credit assignment. We propose Error-Localized Policy Optimization (ELPO), which localizes the first irrecoverable step via binary-search rollout trees under a fixed rollout budget, converts the resulting tree into stable learning signals through hierarchical advantage attribution, and applies error-localized adaptive clipping to strengthen corrective updates on the critical step and its suffix. Across TIR benchmarks in math, science QA, and code execution, ELPO consistently outperforms strong Agentic RL baselines under comparable sampling budgets, with additional gains in Pass@K and Major@K scaling, rollout ranking quality, and tool-call efficiency. Our code is publicly released for reproducibility at https://anonymous.4open.science/r/ELPO-7C19.

Multilingual Retrieval-Augmented Generation (mRAG) leverages cross-lingual evidence to ground Large Language Models (LLMs) in global knowledge. However, we show that current mRAG systems suffer from a language bias during reranking, systematically favoring English and the query’s native language. By introducing an estimated oracle evidence analysis, we quantify a substantial performance gap between existing rerankers and the achievable upper bound. Further analysis reveals a critical distributional mismatch: while optimal predictions require evidence scattered across multiple languages, current systems systematically suppress such “answer-critical” documents, thereby limiting downstream generation performance. To bridge this gap, we propose Language-Agnostic Utility-driven Reranker Alignment (LAURA), Experiments across diverse languages and generation models show that LAURA effectively mitigates language bias and consistently improves mRAG performance.

Tabular data is widely used in fields such as finance and healthcare. Traditional tree-based models are prevalent for tabular prediction tasks due to their ability to handle heterogeneous features. However, their heavy reliance on feature engineering limits both their generalizability and their human-readable interpretability. On the other hand, Large Language Models (LLMs) naturally provide intermediate reasoning steps, thus offering greater transparency in decision-making. Nevertheless, LLMs often fail to match the predictive performance of tree-based models on tabular data. To address these challenges, we propose a novel Logic-Graph-Enhanced LLM Reasoning (LogGER) framework that integrates the strengths of tree-based models and LLMs. Specifically, we reformulate the traditional decision tree as a human-readable logic graph, which explicitly models the causal relationships between features and targets. This logic graph is automatically constructed using LLMs based on data priors and serves as the foundation for LogGER. To fully leverage the logic graph, we further introduce a logic-graph-guided process supervision approach, which evaluates and enhances the quality of LLM’s intermediate reasoning steps using logic-graph-aided process reward. Extensive experiments demonstrate that LogGER consistently outperforms both tree-based models and state-of-the-art LLM methods on a variety of tabular prediction tasks, achieving superior accuracy and interpretability.