Jul 2026· 2026 3rd World Conference on Computer and Information Security (WCCIS)· pp. 11-18· 0 citations· 30 references
Abstract
With the capability enhancement of Large Language Models (LLMs), LLM-based agent systems have demonstrated extraordinary potential in complex task reasoning. However, challenges such as context limitations and knowledge hallucinations persist in practical engineering applications. This paper focuses on the “Design of ReAct Agent Framework Fused with RAG” and proposes a reusable engineering framework. The proposed framework utilizes the ReAct structure as the core for reasoning execution, employs strictly constrained prompts to achieve reliable tool calls (Prompt as Code), integrates Retrieval-Augmented Generation (RAG) as a domain knowledge tool, and enhances system stability through multi-turn context management and error feedback self-correction mechanisms. Based on a general-purpose ReAct project and a specific school policy QA project, this paper establishes a unified experimental setup and conducts main experiments, ablation studies, and case analyses using an author-constructed QA set based on publicly available university policy documents from a specific university. Results on 50 sampled questions show a 100% tool-call success rate for the tool-enabled variants. The ablation results further indicate that context-window management and early stopping reduce the average number of reasoning steps. However, accuracy gains remain inconclusive under the current EM/F1 evaluation and require further validation. This paper provides an engineering pathway reference for designing LLM Agent systems oriented toward real-world business scenarios.
In the current artificial intelligence-driven innovation era, the pace of knowledge growth is accelerating, and is hard to keep up with. While generative models are increasingly used to synthesize content, they often lack in information grounding. To address these peculiarities of our time, we propose Wyvern, a multi-agent framework for the automated generation of grounded, multimodal technical reports. Wyvern allows for the generation of multimodal outputs, integrating images, tables, and text with supporting references in a unified report. Additionally, a particular focus is placed on the grounding of the content, with the implementation of a claims auto-revision stage. We conduct a human evaluation study to assess the quality of our proposed framework. The results show that the figures'informativeness is perceived as superior to that of a recent baseline in 87% of cases. Furthermore, Wyvern's reports are rated as more useful than those produced by three alternative methods in 63% to 100% of instances. We also carry out automatic evaluations showing that Wyvern gains up to 2.3$\times$ in citation recall and 1.6$\times$ in citation precision with respect to the baselines.
Beatrice Alessandra Motetti, Emilien Guandalino, D. J. Pagliari et al.· 0 citations
Retrieval-Augmented Generation (RAG) has emerged as the dominant strategy to ground LLM outputs in retrieved evidence to reduce hallucinations and increase factuality, yet RAG itself introduces new challenges: noisy retrieval, knowledge conflicts, and wasted computation from unnecessary retrieval. This tutorial provides an in-depth treatment of modern RAG based on AI-facilitated systematic analysis of ~2000 recent papers (2020--2026). We trace the RAG pipeline from its modular foundations through graph-enhanced reasoning to the latest RL-driven agentic architectures, covering each stage---triggering, query rewriting, retrieval, post-processing, generation, and knowledge-graph integration---with running examples, method comparisons, and benchmark results. Attendees will leave with a structured roadmap, evidence-backed practical recommendations, and a clear map of open research opportunities.
X. Dong, Sanat Sharma, Kai Sun et al.· Proceedings of the 32nd ACM...· 0 citations
but struggle when confronted with multi-step, complex task planning that requires interaction with external environments. This paper investigates the architecture, implementation, and efficacy of tool-augmented language agents enhanced with iterative self-critique mechanisms. By integrating external application programming interfaces, structured databases, and computational engines, these agents transcend isolated text generation, evolving into active systems capable of executing concrete actions. However, naive tool utilization often results in cascading errors during prolonged execution trajectories. To mitigate this, we introduce an iterative self-critique framework where the agent continuously evaluates its own outputs, identifies logical fallacies or execution failures, and dynamically recalibrates its plan. This research details a comprehensive methodological framework, formalizing the probabilistic decision-making and critique generation processes. Empirical evaluations across simulated complex environments demonstrate that the proposed architecture significantly improves task success rates, minimizes superfluous tool invocations, and enhances error recovery. The findings indicate that integrating reflective cognition paradigms with modular toolsets is essential for deploying autonomous language agents in high-stakes, real-world applications.
Mabel Kwok· International journal of inf...· 0 citations
This article presents a novel approach to Intelligent Tutoring Systems (ITS) by integrating Retrieval-Augmented Generation (RAG) with Large Language Models (LLMs) to enable dynamic personalization in educational contexts. The system addresses limitations in traditional ITS that rely on static, rule-based approaches by implementing a three-layered architecture combining semantic retrieval mechanisms with generative AI capabilities. Using GPT-4 as the core LLM enhanced with a custom RAG framework, the system demonstrates improvements in response accuracy (93%), inference speed (2.1 seconds per prompt), and computational efficiency compared to a standard GPT-4 baseline, a traditional rule-based ITS, and an LLM with keyword-based retrieval. The research employs both ASSISTments (fine-grained interaction data) and EdNet (large-scale longitudinal data) datasets for evaluation. Results show that the RAG-enhanced system achieves 40% better contextual relevance compared to standard LLM implementations. The framework incorporates adaptive prompting strategies, real-time knowledge base updates, and multi-level personalization algorithms to create a dynamic educational environment.
Kuyoro Afolashade, N. Uchenna, Akinwunmi Damilare· British journal of computer,...· 0 citations
Digital Adoption Platforms (DAPs) are embedded overlays widely used on web systems to guide users through operations inside a page, helping them get started with unfamiliar interfaces quickly. Completing a real task, however, rarely means clicking a few buttons on a single page: it takes a sequence of actions that unfolds across changing page states. Prior studies have also treated automated web agent actions and guide text generation as two separate problems, and most of them feed models textual page representations such as the DOM or accessibility trees rather than the rendered screens that humans actually operate on. In this work we introduce MAG, the first benchmark that unifies task execution and guide writing into a single Multimodal Action and Guide task, with two grounding schemes over screenshots: Set-of-Mark element selection and raw pixel coordinates. We further build a complete harness for this compound task, covering annotation with LLM assistance and human verification, training, evaluation in live environments, and joint metrics for actions and guides. With this harness we evaluate frontier API models and open multimodal models, and report detailed analyses. Finally, we design a GRPO training method augmented with expert trajectories, which nearly doubles the success rate of a supervised 9B agent (from 6.9% to 13.2%) and improves guide quality at the same time. Even the strongest model completes fewer than 40% of the tasks, leaving ample room for future research.
Chengguang Gan, Hanjun Wei, Yunhao Liang et al.· 0 citations
Large Language Models (LLMs) have demonstrated strong capabilities in knowledge-based Visual Question Answering (VQA). However, existing prompt construction methods are often rigid and fail to fully exploit the reasoning potential of LLMs. To address this limitation, we propose the Intelligent Prompt Construction Framework (IPCF), which equips an autonomous agent with the ability to dynamically generate task-specific prompts. IPCF consists of a planner and a toolbox: the planner, powered by an LLM, enables autonomous decision-making, while the toolbox provides three tools—the vanilla VQA model for inspiration, the LLM for knowledge injection, and a knowledge base for information retrieval. This architecture allows the agent to flexibly determine when and how to invoke each tool and to construct adaptive prompts accordingly. Experimental results show that IPCF achieves performance gains of 2.6 and 1.9 points over existing baselines on the OK-VQA and A-OKVQA datasets, respectively.
Zhongjian Hu, Peng Yang, Dongmei Yang et al.· ACM Transactions on Multimed...· 0 citations