Agentic AI systems powered by large language models are increasingly expected to solve capability-intensive tasks by using reusable external skills, which package instructions, tool-use procedures, executable code, and auxiliary resources. However, mainstream skill-based systems typically assume that available skills can be directly placed in the prompt. As skill libraries grow, this assumption becomes impractical: large corpora cannot be fully exposed to the model, while excessive candidate exposure can distract the agent and degrade skill selection. Therefore, scalable skill augmentation requires retrieving a small set of task-relevant skills from a large corpus before incorporating them into the agent’s context. We formulate this paradigm as Skill Retrieval Augmentation (SRA), where agents dynamically retrieve, incorporate, and apply reusable skills from large external corpora on demand. To systematically study SRA, we introduce SRA-Bench, which enables decomposed evaluation across three stages: skill retrieval, skill incorporation, and skill application. SRA-Bench contains 5,400 capability-intensive instances from six domains, 636 manually constructed gold skills, and a corpus of 26,262 skills. Experiments across multiple LLM and skill-use strategies show that SRA can consistently improve agent performance, but current agents struggle to realize this promise in practical pipelines. Specifically, they remain sensitive to distractor skills, lack need-aware skill loading, and often fail to translate relevant skills into correct task execution. Together, these results establish SRA as a distinct research problem and provide a foundation for developing scalable skill-augmented agents.

Knowledge editing enables efficient updates to Large Language Models (LLMs) by modifying specific knowledge without full-model retraining. Among knowledge editing approaches, in-context editing (ICE) stands out for its ability to inject knowledge without modifying the model’s parameters. However, existing ICE approaches directly edit model context without isolating target knowledge from the reasoning path of model inference, resulting in unreliable and low-quality outputs, particularly in multi-hop tasks. To investigate this issue, we analyze the interaction between reasoning path planning and knowledge injection, showing that the reasoning ability of a LLM is usually coupled with its original knowledge, and directly replacing old knowledge with new one could simultaneously hurt the LLM’s performance in task reasoning. Based on these findings, we propose DecKER, a novel ICE framework that separates model reasoning from knowledge editing. Extensive experiments show that DecKER significantly improves multi-hop reasoning performance by mitigating knowledge conflicts and preserving reasoning integrity.

Knowledge Editing is a technique that updates large language models (LLMs) with new information to maintain their world knowledge. This approach avoids the need to rebuild the model from scratch, thereby addressing the high costs associated with frequent retraining. Among these, the in-context editing paradigm stands out for its effectiveness in integrating new knowledge while preserving the model’s original capabilities. Despite its potential, existing in-context knowledge editing methods are often task-specific, focusing primarily on multi-hop QA tasks using structured knowledge triples. Moreover, their reliance on few-shot prompting for task decomposition makes them unstable and less effective in generalizing across diverse tasks. In response to these limitations, we propose EditCoT, a novel knowledge editing framework that flexibly and efficiently updates LLMs across various tasks without retraining. EditCoT works by generating a chain-of-thought (CoT) for a given input and then iteratively refining this CoT process using a CoT editor based on updated knowledge. We evaluate EditCoT across a diverse range of benchmarks, covering multiple languages and tasks. The results demonstrate that our approach achieves state-of-the-art performance while offering superior generalization, effectiveness, and stability compared to existing methods, marking a significant advancement in the field of knowledge updating.

Hallucinations in large language models (LLMs) refer to the phenomenon of LLMs producing responses that are coherent yet factually inaccurate. This issue undermines the effectiveness of LLMs in practical applications, necessitating research into detecting and mitigating hallucinations of LLMs. Previous studies have mainly concentrated on post-processing techniques for hallucination detection, which tend to be computationally intensive and limited in effectiveness due to their separation from the LLM’s inference process. To overcome these limitations, we introduce MIND, an unsupervised training framework that leverages the internal states of LLMs for real-time hallucination detection without requiring manual annotations. Additionally, we present HELM, a new benchmark for evaluating hallucination detection across multiple LLMs, featuring diverse LLM outputs and the internal states of LLMs during their inference process. Our experiments demonstrate that MIND outperforms existing state-of-the-art methods in hallucination detection.