Table Question Answering (TableQA) enables natural language interaction with structured tabular data. However, existing large language model (LLM) approaches face critical limitations: context length constraints that restrict data handling capabilities, hallucination issues that compromise answer reliability, and single-agent architectures that struggle with complex reasoning scenarios involving semantic relationships and multi-hop logic. This paper introduces DataFactory, a multi-agent framework that addresses these limitations through specialized team coordination and automated knowledge transformation. The framework comprises a Data Leader employing the ReAct paradigm for reasoning orchestration, together with dedicated Database and Knowledge Graph teams, enabling the systematic decomposition of complex queries into structured and relational reasoning tasks. We formalize automated data-to-knowledge graph transformation via the mapping function T : D × S × R → G , and implement natural language-based consultation that—unlike fixed workflow multi-agent systems—enables flexible inter-agent deliberation and adaptive planning to improve coordination robustness; we also apply context engineering strategies that integrate historical patterns and domain knowledge to reduce hallucinations and improve query accuracy. Across TabFact, WikiTableQuestions, and FeTaQA, using eight LLMs from five providers, results show consistent gains. Our approach improves accuracy by 20.2% (TabFact) and 23.9% (WikiTQ) over baselines, with significant effects (Cohen’s d>1). Team coordination also outperforms single-team variants (+5.5% TabFact, +14.4% WikiTQ, +17.1% FeTaQA ROUGE-2). The framework offers design guidelines for multi-agent collaboration and a practical platform for enterprise data analysis through integrated structured querying and graph-based knowledge representation.

A new class of poisoning attacks has recently emerged targeting the client-side Domain Name System (DNS) cache. It allows users to visit fake websites unconsciously, thereby revealing their information, such as passwords. However, the current DNS defense architecture does not include DNS clients. Although relative encryption solutions can mitigate this attack, they require the cooperation of multiple parties, and the deployment speed is slow. Therefore, we propose an intelligent-driven proactive defense strategy. First, we model the offensive and defensive process as a stochastic game based on moving target defense. Second, we adopt and optimize Proximal Policy Optimization (PPO), a deep reinforcement learning method, to solve problems caused by uncertain attack strategies and unknown state transition probability. Third, we design a self-checking component in PPO to solve the uncertainty of action space caused by game state constraints based on our previous work. Thus the convergence speed and stability of PPO are improved. Finally, to the best of our knowledge, we are the first to game with intelligent attackers besides three conventional ones. Our strategy does not require any modifications to the DNS architecture. Through an extensive experimental campaign, the prototype system is proved to be effective against multiple attack modes. Its success rate is 98.5% approximately, and network round-trip time is about 55 ms. Even for random attackers, our method can achieve the theoretical maximum defensive success rate.

Many machine-learning-based intrusion detection methods have been proposed, however there is a lack of collaboration among these methods. Faced with a cascade of malicious behaviors and various running environments, coupled with the endless emergence of new malicious activities, it is difficult for us to choose an algorithm manually that is suitable for all scenarios. In addition, usually the binary detection models are applied that only “normal” or “abnormal” decision is made, and it is difficult for us to know how much confidence we have in the prediction model. In this study, we propose an intrusion collaborative detection framework (ICDF), an ICDF that allows heterogeneous detection models to effectively work together which have complementary expertise. A multialgorithm model ensemble learning method with confidence interval is adopted. In this process, each algorithm model only makes prediction judgments on its own credible probability interval and refuses to predict outside the interval. The final result is generated by voting based on the confidence of multiple models. Ten detection algorithms were tested on three different data sets. Compared with different single algorithms, ICDF could achieve high precision and recall rate, and the best F1 scores.