Retrieval-augmented generation (RAG) has become a widely adopted paradigm for realistic financial analysis over financial documents. However, existing benchmarks fail to capture realistic financial analysis settings that involve cross-document retrieval, multi-page evidence integration, and diverse analytical tasks. To address this gap, we introduce FinMRAGBench, a comprehensive multi-modal financial RAG benchmark in which most questions require retrieving evidence scattered across multiple pages and documents, constructed from large-scale real-world annual reports and comprising 887 expert-verified QA pairs spanning five representative financial analysis tasks. Moreover, we introduce FinMRAGAgent, an agent trained on high-quality agentic trajectories following the reasoning-and-acting (ReAct) paradigm, capable of dynamic tool invocation and multi-step financial analysis. Our extensive experiments show that current multi-modal RAG systems still struggle with incomplete retrieval and complex financial reasoning. In contrast, FinMRAGAgent achieves the strongest overall performance across all models, demonstrating that our structured reasoning approach significantly enhances multi-modal RAG in realistic financial scenarios. The code and data are available at https://github.com/sqyangit/FinMRAGBench.

Recent advances in persona-centric memory demonstrate the promise of multi-agent systems for managing persona memory, especially in conversational scenarios. However, their complex pipelines often incur information loss and poor cross-scenario robustness, leading to suboptimal performance. In this paper, we propose DeltaMem, an agentic memory management system that formulates persona-centric memory management as an end-to-end task within a single-agent setting. To further improve the performance of our agentic memory manager, we draw inspiration from the evolution of human memory and synthesize a user-assistant dialogue dataset along with corresponding operation-level memory updating labels. Building on this, we introduce a novel Memory-based Levenshtein Distance to formalize the memory updating reward, and propose a tailored reinforcement learning framework to further enhance the management capabilities of DeltaMem. Extensive experiments show that both training-free and RL-trained DeltaMem outperform all product-level baselines across diverse long-term memory benchmarks, including LoCoMo, HaluMem, and PersonaMem.

Large language models (LLMs) have demonstrated impressive capabilities in reasoning with the emergence of reasoning models like OpenAI-o1 and DeepSeek-R1. Recent research focuses on integrating reasoning capabilities into the realm of retrieval-augmented generation (RAG) via outcome-supervised reinforcement learning (RL) approaches, while the correctness of intermediate think-and-search steps is usually neglected. To address this issue, we design a process-level reward module to mitigate the unawareness of intermediate reasoning steps in outcome-level supervision without additional annotation. Grounded on this, we propose Learning to Think-and-Search (LeTS), a novel framework that hybridizes stepwise process reward and outcome-based reward to current RL methods for RAG. Extensive experiments demonstrate the generalization and inference efficiency of LeTS across various RAG benchmarks. In addition, these results reveal the potential of process- and outcome-level reward hybridization in boosting LLMs’ reasoning ability via RL under other scenarios.