Recently, large language models (LLMs) have demonstrated impressive performance but still suffer from hallucinations. Model editing has been proposed as a means to correct factual inaccuracies. A challenging scenario is sequential model editing (SME), which aims to rectify errors continuously, rather than a one-time task. During SME, the general capabilities of LLMs can be negatively affected due to the introduction of new parameters. In this paper, we propose a queue-based self-correction framework, QueueEDIT, that not only enhances SME performance by addressing long-sequence dependencies but also mitigates the impact of parameter bias on the general capabilities of LLMs. Specifically, we first introduce a structural mapping editing loss to map editing triplets to knowledge-sensitive neurons within the Transformer layers. We then store the located parameters for each piece of edited knowledge in a queue and dynamically align previously edited parameters. At each edit, we select parameters in the queue that are most relevant to currently located parameters to determine whether knowledge associated with previous edits requires realignment. Irrelevant parameters in the queue are frozen, and we update the parameters at the queue head into the LLM to ensure they do not harm general capabilities. Experiments show that QueueEDIT significantly outperforms strong baselines across various SME settings, while maintaining competitive performance in single-turn editing. Resulting LLMs also preserve high performance on general NLP tasks throughout the SME process.

Recent advancements in LLM-based multi-agent systems have demonstrated remarkable collaborative capabilities across complex tasks. To enhance the overall efficiency, existing methods often rely on aggressive graph topology evolution for agents (e.g., node or edge pruning), which risks prematurely discarding valuable agents due to transient issues such as hallucinations or temporary knowledge gaps. However, such hard pruning overlooks the potential for "zombie" agents to recover and contribute in subsequent discussion rounds. In this paper, we propose AgentRevive, a Markov state-aware framework for resilient multi-agent evolution. Our approach dynamically manages agent collaboration through soft state transitions, implemented via two key components: (1) State-Aware Policy Learning: Agent states are divided into "Active", "Standby", and "Terminated", selectively propagating messages based on agent memory. The policy employs a risk estimator to optimize agent state transitions by assessing hallucination risk, minimizing the influence of unreliable nodes while safeguarding valuable ones. (2) State-Aware Edge Optimization: Subgraph edges are pruned according to states learned from the policy, permanently removing "Terminated" nodes and retaining "Standby" nodes for subsequent rounds to observe potential future contributions. Extensive experiments on general reasoning, domain-specific, and hallucination challenge tasks show that our method consistently outperforms strong baselines and significantly reduces token consumption through state-aware agent scheduling.

Despite substantial advances in large language models (LLMs), producing factually consistent responses for knowledge-intensive question answering remains challenging. These difficulties are primarily due to hallucination and the limitations of LLMs in bridging long-tail knowledge gaps. To address this, we propose AMATA, an Adaptive Multi-Agent Trajectory Alignment framework that dynamically integrates external knowledge to improve response interpretability and factual grounding. Our architecture leverages six specialized agents that collaboratively perform structured actions for complex question reasoning. We formalize multi-agent collaboration with external tools as a trajectory preference alignment problem, incorporating question-aware agent customization and inter-agent preference harmonization. AMATA introduces two principal innovations: (1) Intra-Trajectory Preference Learning, which learns objective-oriented preferences to prioritize critical agents, and (2) Inter-Agent Dependency Learning, which captures cross-agent tool dependencies through a novel dependency-aware direct preference optimization technique. Empirical results show that AMATA consistently outperforms baseline approaches, knowledge-augmented frameworks, and LLM-based trajectory systems on five established knowledge-intensive QA benchmarks. Further analysis demonstrates the efficiency of our method in reducing token consumption.