Multi-Hop Question Answering (MHQA) tasks permeate real-world applications, posing challenges in orchestrating multi-step reasoning across diverse knowledge domains. While existing approaches have been improved with iterative retrieval, they still struggle to identify and organize dynamic knowledge. To address this, we propose DualRAG, a synergistic dual-process framework that seamlessly integrates reasoning and retrieval. DualRAG operates through two tightly coupled processes: Reasoning-augmented Querying (RaQ) and progressive Knowledge Aggregation (pKA). They work in concert: as RaQ navigates the reasoning path and generates targeted queries, pKA ensures that newly acquired knowledge is systematically integrated to support coherent reasoning. This creates a virtuous cycle of knowledge enrichment and reasoning refinement. Through targeted fine-tuning, DualRAG preserves its sophisticated reasoning and retrieval capabilities even in smaller-scale models, demonstrating its versatility and core advantages across different scales. Extensive experiments demonstrate that this dual-process approach substantially improves answer accuracy and coherence, approaching, and in some cases surpassing, the performance achieved with oracle knowledge access. These results establish DualRAG as a robust and efficient solution for complex multi-hop reasoning tasks.

Large Language Models (LLMs) have demonstrated proficiency in addressing tasks that necessitate a combination of task planning and the usage of external tools, such as weather and calculator APIs. However, real-world industrial systems present prevalent challenges in task planning and tool usage: numerous APIs in the real system make it intricate to invoke the appropriate one, while the inherent limitations of LLMs pose challenges in orchestrating an accurate sub-task sequence and API-calling order. This paper introduces a comprehensive framework aimed at enhancing the Task Planning and Tool Usage (TPTU) abilities of LLM-based agents in industry. Our framework comprises three key components designed to address these challenges: (1) the API Retriever selects the most pertinent APIs among the extensive API set; (2) the Demo Selector retrieves task-level demonstrations, which is further used for in-context learning to aid LLMs in accurately decomposing subtasks and effectively invoking hard-to-distinguish APIs; (3) LLM Finetuner tunes a base LLM to enhance its capability for task planning and API calling. We validate our methods using a real-world industry system and an open-sourced academic dataset, demonstrating the efficacy of each individual component as well as the integrated framework. The code is available at here.

Accurate prediction of user attributes from social media is valuable for both social science analysis and consumer targeting. In this paper, we propose a systematic method to leverage user online social media content for predicting offline restaurant consumption level. We utilize the social login as a bridge and construct a dataset of 8,844 users who have been linked across Dianping (similar to Yelp) and Sina Weibo. More specifically, we construct consumption level ground truth based on user self report spending. We build predictive models using both raw features and, especially, latent features, such as topic distributions and celebrities clusters. The employed methods demonstrate that online social media content has strong predictive power for offline spending. Finally, combined with qualitative feature analysis, we present the differences in words usage, topic interests and following behavior between different consumption level groups.