Large Language Model (LLM)-based agents have achieved promising performance in web task planning. However, they primarily focus on task completion rather than preference modeling, leading to imprecise personalization. This can be attributed to two facts: (1) Contextual scarcity. Vague instructions and insufficient background of users’ inputs make it difficult to accurately identify users’ intents. (2) Preference unpredictability. It’s hard for a single response path to handle users’ sudden intent changes. To address these issues, we propose SAPA, a Situation-Augmented Personalized web Agent. Specifically, a Situational Intent Grounding module is proposed to enrich the contexts of users’ inputs by incorporating historical records and candidate constraints, effectively alleviating the contextual scarcity. Then, we propose a Task-Driven Semantic Expansion module to explore diverse response paths and reduce preference unpredictability in turn, which is achieved by employing a test-time expansion strategy. We construct SAPA with representative LLMs and conduct extensive experiments on PersonalWAB and Amazon Reviews datasets. On PersonalWAB, SAPA achieves new state-of-the-art performance with improvements of +14.0%, +8.8%, and +1.3% in Overall score on personalized recommendation, search, and question answering, respectively. We further evaluate on three single-category scenarios from Amazon Reviews (Office Products, Beauty and Personal Care, Electronics), where SAPA consistently improves Overall scores by 16%–18% with gains across all task types. Ablation studies and case analyses further confirm that the gains stem from jointly addressing contextual scarcity and preference unpredictability.

Small Language Models (SLMs) have been widely deployed in edge computing and mobile devices. Generally, these models need to be continuously updated to adapt to dynamic environments and evolving user needs. Therefore, the performance of SLMs in continual learning has become a critical research focus. Compared to LLMs with rich redundant representation spaces, SLMs are more likely to overwrite previous representations during new task learning due to their fewer attention heads and layers. To address this issue, we propose LightLoRA, a sparse fine-tuning method specifically designed for SLMs. This method constructs a conflict-aware importance mechanism by combining raw importance from SVD decomposition with task conflict degrees based on gradient direction consistency. By selecting and updating heads that provide high contributions and low conflicts, LightLoRA successfully maintains sufficient space for knowledge preservation. Experimental results show that the proposed method reaches state-of-the-art performance across multiple benchmarks without adding extra parameters. Code is released on https://anonymous.4open.science/r/LightLoRA-077C.

Large language models (LLMs) generate human-aligned content under certain safety constraints. However, the current known technique “jailbreak prompt” can circumvent safety-aligned measures and induce LLMs to output malicious content. Research on Jailbreaking can help identify vulnerabilities in LLMs and guide the development of robust security frameworks. To circumvent the issue of attack templates becoming obsolete as models evolve, existing methods adopt iterative mutation and dynamic optimization to facilitate more automated jailbreak attacks. However, these methods face two challenges: inefficiency and repetitive optimization, as they overlook the value of past attack experiences. To better integrate past attack experiences to assist current jailbreak attempts, we propose the JailExpert, an automated jailbreak framework, which is the first to achieve a formal representation of experience structure, group experiences based on semantic drift, and support the dynamic updating of the experience pool. Extensive experiments demonstrate that JailExpert significantly improves both attack effectiveness and efficiency. Compared to the current state-of-the-art black-box jailbreak method, JailExpert achieves an average increase of 24% in attack success rate and 2.7 times improvement in attack efficiency.