FraLLM is a novel LLM fine-tuning framework that seamlessly internalizes transaction-oriented knowledge for FRA and introduces the Memory Token Mechanism, which recurrently aggregates historical text prototypes into a compact, continuously updated memory token that allows LLMs to effectively synthesize long-term transaction history while ensuring cost-efficiency.
Siwei Zhang, Yun Xiong, Xi Chen et al.· Proceedings of the 32nd ACM...· 0 citations
Deep research is a retrieval-intensive task that requires iteratively retrieving evidence, reading across sources, and synthesizing source-grounded outputs. In practice, real-world deep research applications are of high workloads that require generating massive reports or conducting large-scale literature surveys. Such applications increasingly require batch processing capabilities, which are missing from traditional chat-oriented agents, limiting throughput when processing large volumes of structurally similar jobs. We introduce C3Flow (Concurrent Claude Code Workflow), a framework that transforms Claude Code from an interactive assistant into a SIMD-style (Single Instruction, Multiple Data) concurrent compute engine. C³Flow treats each agent instance as an isolated, schedulable unit capable of handling declarative multi-step tasks, multi-model routing, and comprehensive trajectory logging. On BrowseComp-zh, C³Flow improves pass@1 from 48.44% to 61.59% and pass@3 from 70.24% to 77.51% compared to standard function calling, while reducing average latency. For multi-hop fact verification, C3Flow achieves a 5.9 speedup over human annotators while maintaining 87.5% accuracy, demonstrating its effectiveness for production-scale deep research pipelines. Code is available at~ https://github.com/RAGenius/C3Flow.
Yunfan Gao, Xinyi Huang, Yijie Zhong et al.· Annual International ACM SIG...· 0 citations
Financial risk assessment (FRA) seeks to forecast the likelihood of user default based on transaction sequences, which are inherently sequential and enriched with textual attributes. Despite the remarkable potential of large language models (LLMs) in sequential modeling and semantic understanding, developing LLM-based FRA solutions still presents unique challenges due to the temporal dynamics of transaction patterns and the need to capture long-term dependencies across prolonged behavioral horizons. To tackle these issues, in this paper, we propose FraLLM, a novel LLM fine-tuning framework that seamlessly internalizes transaction-oriented knowledge for FRA. FraLLM is designed from an interesting idea of Think-like-LSTM, enabling dynamic-aware LLMs to reason over long-range transaction trajectories. Specifically, we propose a Dynamic Fine-tuning Paradigm, which reprograms the transaction sequences into timestamped text prototypes and fine-tunes LLMs via a next-text prediction task. It dynamically offers linguistic gradients across time steps, facilitating transaction dynamics. Meanwhile, we introduce the Memory Token Mechanism that recurrently aggregates historical text prototypes into a compact, continuously updated memory token. This token allows LLMs to effectively synthesize long-term transaction history while ensuring cost-efficiency. Extensive experiments on three real-world industrial transaction datasets show that FraLLM outperforms all baselines, achieving substantial gains in AUC (+11.2%), Recall@10 (+9.1%), and KS (+14.8%). Moreover, our proposed FraLLM yields significant improvements in online testing within the WeChat Pay financial system, demonstrating its effectiveness and application value for industrial deployment.
Siwei Zhang, Yun Xiong, Xi Chen et al.· Proceedings of the 32nd ACM...· 0 citations
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