In Agentic Search, trajectory-level outcome rewards fail to quantify the behavioral contributions of individual steps, while existing step-level reward methods typically rely on costly tree sampling. We view world knowledge as a latent world graph and each IS task as search within a latent task graph, where effective steps should make graph progress toward the answer node. Based on this prior, we propose Graph-Distance Contribution Reward (GDCR), a step-level process reward that scores newly-retrieved and newly-cited entities by their distance to the answer node in a training-time Entity-Relation (ER) graph. We further propose Step Advantage Policy Optimization (SAPO), which converts GDCR into step-level advantages and combines them with trajectory-level outcome advantages. Experiments on four challenging benchmarks validate the effectiveness of our method.

Learning from experience is essential for agents in real-world applications. However, most existing methods rely on static imitation of successful expert trajectories, overlooking the informative signals exposed from failed experiences and lacking a interactive feedback loop with the environment. In this paper, we propose R-IDEAL, a unified framework for insight-driven experience generation and optimization. R-IDEAL first perform contrastive analysis between successful and failed trajectories to automatically synthesize structured insights, consisting of positive plans and negative lessons, which guide agents not only on what to do, but also on what to avoid. These synthesized insights serve as supervision to train a lightweight insight model that generates task-specific guidance. While this supervision stabilizes insight generation, it does not guarantee the effectiveness of the insights due to distribution shifts and execution discrepancies across agents. Therefore, we further optimize the insight model via reinforcement learning using execution feedback from heterogeneous agents in real environments, ensuring that the generated insights effectively improve agent behavior. Experiments on AlfWorld and SciWorld show that R-IDEAL substantially outperforms existing methods, demonstrating strong robustness in out-of-distribution scenarios and serving as a lightweight, plug-and-play module for both closed-source and large-scale models.

Long-context LLMs and Retrieval-Augmented Generation (RAG) systems expand accessible context, but still defer state tracking and evidence consolidation to query time. This passive paradigm is brittle in streaming settings where facts are updated and answers depend on latent states rather than explicit spans. We introduce Unified Memory Agent (UMA), an end-to-end reinforcement-learning framework that trains a single policy to maintain external memory and answer questions. UMA updates a structured memory bank via CRUD operations and combines it with raw-context retrieval for QA. To jointly optimize memory maintenance and question answering, we propose Task-Stratified GRPO, which normalizes heterogeneous trajectories separately for more effective credit assignment. We also introduce Ledger-QA, a controlled benchmark for long-horizon state tracking over accumulated updates. Experiments on Ledger-QA and 13 TTL/AR benchmarks show that UMA improves long-horizon tracking, achieves the best average TTL/AR score among compared methods, and remains competitive on retrieval-centric benchmarks. Our results highlight learned proactive memory management as a complement to longer context windows and query-time retrieval.

Prompt engineering is pivotal for harnessing the capabilities of large language models (LLMs) across diverse applications. While existing prompt optimization methods improve prompt effectiveness, they often lead to prompt drifting, wherein newly generated prompts canadversely impact previously successful cases while addressing failures. Furthermore, these methods tend to rely heavily on LLMs’ intrinsic capabilities for prompt optimization tasks. In this paper, we introduce STRAGO (StrategicGuided Optimization), a novel approach designed to mitigate prompt drifting by leveraging insights from both successful and failed cases to identify critical factors for achieving optimization objectives. STRAGO employs a how-to-do methodology, integrating in-context learning to formulate specific, actionable strategies that provide detailed, step-by-step guidance for prompt optimization. Extensive experiments conducted across a range of tasks, including reasoning, natural language understanding, domain-specific knowledge, and industrial applications, demonstrate STRAGO’s superior performance. It establishes a new stateof-the-art in prompt optimization, showcasing its ability to deliver stable and effective prompt improvements.

Prompt engineering is very important to enhance the performance of large language models (LLMs). When dealing with complex issues, prompt engineers tend to distill multiple patterns from examples and inject relevant solutions to optimize the prompts, achieving satisfying results. However, existing automatic prompt optimization techniques are only limited to producing single flow instructions, struggling with handling diverse patterns. In this paper, we present AMPO, an automatic prompt optimization method that can iteratively develop a multi-branched prompt using failure cases as feedback. Our goal is to explore a novel way of structuring prompts with multi-branches to better handle multiple patterns in complex tasks, for which we introduce three modules: Pattern Recognition, Branch Adjustment, and Branch Pruning. In experiments across five tasks, AMPO consistently achieves the best results. Additionally, our approach demonstrates significant optimization efficiency due to our adoption of a minimal search strategy.