While Knowledge Graphs (KGs) can ground Large Language Models (LLMs) in factual knowledge, existing LLM-KG integration methods for complex reasoning are plagued by computational inefficiency and semantic inconsistency. The tight coupling of LLM inference and KG traversal leads to prohibitive costs, while spurious reasoning paths often misguide learning-based agents, causing reward hacking. To this end, we propose EAT (Exploration-and-Thinking), a novel agentic framework that synergizes LLMs with Reinforcement Learning (RL) for efficient and faithful reasoning on KGs. EAT's core innovations are twofold: (1) an adaptive retrieval-augmented generation mechanism that decouples language comprehension from structured exploration, dramatically improving efficiency; and (2) an LLM-guided reward shaping strategy that explicitly penalizes semantically inconsistent paths and promotes logically valid trajectories grounded in the KG. Extensive experiments on benchmarks like WebQSP, CWQ, and GrailQA show that EAT achieves state-of-the-art performance. Notably, it surpasses the reasoning capability of GPT-4o while utilizing a significantly smaller 8B-parameter LLM.

Stance detection, a natural language processing technique, captures user attitudes on controversial social media topics. However, the semantic ambiguity of social texts makes accurate stance determination challenging. Existing annotated data is often domain-specific, resulting in poor model generalization for unseen targets and cross-domain scenarios. To tackle this challenge, we introduce inference tasks related to stance detection as auxiliary tasks and use a multitask learning approach to improve the model’s understanding of textual semantics. Meanwhile, to alleviate the scarcity of annotated data, we use argumentation corpus with abundant resources as the source domain to train the basic stance detection model. We integrate this model with a large-scale framework to facilitate weakly supervised learning for machine annotation of unlabeled social texts, thereby boosting the model’s adaptability across different targets and domains. Experimental results on four Twitter datasets demonstrate that our method can significantly improve the model’s stance discriminative ability and generalization performance.

The symmetric Password-Authenticated Key Exchange (PAKE) protocol enables two parties sharing a low-entropy password to establish a high-entropy session key, offering advantages in simplicity and efficiency. This makes it particularly suitable for Internet of Things (IoT) devices with limited computational resources, positioning it as one of the most effective security methods for authentication and key exchange in IoT environments. In this paper, we analyze a recently proposed efficient symmetric PAKE protocol for IoT, identifying its vulnerability to offline dictionary attacks and its failure to meet the claimed security goals. Building upon the analysis of these design flaws, we present an enhanced protocol, demonstrating its security within the random oracle model. The enhanced protocol retains the protocol flow and computational operations of the original protocol to the greatest extent possible, maintains nearly the same computational efficiency, and simultaneously addresses the vulnerabilities that made the original protocol susceptible to offline dictionary attacks.