Hallucination has emerged as a pivotal challenge of Large Language Models (LLMs) that generate plausible yet non‑factual content, significantly impeding the trustworthy AI applications in real-world scenarios like medical diagnosis and autonomous driving. Editing the internal activations of LLMs during inference has shown promising effectiveness in mitigating hallucinations with minimal cost. However, previous editing approaches neglect the query‑specific inference pathways that require tailored truthful steering vectors, resulting in suboptimal hallucination mitigation. To address these issues, we propose the Query-Routed Activation Editing (QRAE) framework, which comprises Divergence-sensitive Head Routing (DHR) and Truth-hierarchical Preference Steering (TPS), to fully leverage query-specific semantics for adaptive activation editing. Specifically, DHR is proposed to establish a query-aware head selection criterion, thereby dynamically routing to truth-critical attention heads. Subsequently, TPS introduces a query-specific steering vector calibration policy with the guidance of progressive truth-preferred optimization, enabling precise and adaptive editing for each distinct query. Extensive experiments on the widely recognized TruthfulQA benchmark demonstrate that QRAE outperforms SOTA methods by up to 13.2% in MC1. Meanwhile, QRAE demonstrates strong generalization to out-of-distribution TriviaQA and Natural Questions benchmarks.

Large Language Models (LLMs) inevitably encounter factual hallucinations and knowledge obsolescence, necessitating lifelong knowledge editing to sustain reliability and factual advancement. While mainstream lifelong editing paradigms aim to alleviate knowledge forgetting through allocating and updating isolated parameter subspaces, they often overlook conflict assessment among distinct editing processes, leading to unjustified subspace allocation and indiscriminate neuron tuning. To address these issues, we propose the Conflict-Assessed Sensitive Editing (CASE) framework, which integrates a Conflict-Assessed Editing Allocation (CAA) module and a Knowledge-sensitive Neuron Tuning (KNT) strategy. The CAA module quantitatively assesses editing conflicts to enable justified subspace allocation, thereby reducing globally significant conflicts and routing errors. The KNT strategy adaptively identifies and tunes knowledge-sensitive neurons through a calibrated sensitivity threshold, effectively eliminating local conflicts and enhancing subspace stability. Extensive experiments on standard lifelong editing benchmarks demonstrate that CASE achieves state-of-the-art performance, improving average editing accuracy by nearly 10% after 1,000 sequential edits. Overall, CASE substantially mitigates editing conflicts and enhances knowledge retention, offering a scalable and conflict-resilient solution for lifelong model editing.

Zhange Zhang, Yuqing Ma, Yulong Wang, Shan He, Tianbo Wang, Siqi He, Jiakai Wang, Xianglong Liu. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Intervening the internal activations of large language models (LLMs) provides an effective inference-time alignment approach to mitigate undesirable behaviors, such as generating erroneous or harmful content, thereby ensuring safe and reliable applications of LLMs.However, previous methods neglect the misalignment discrepancy among varied tokens, resulting in deviant alignment direction and inflexible editing strength.To address these issues, we propose a token-aware editing (TAE) approach to fully utilize token-level alignment information in the activation space, therefore realizing superior post-intervention performance.Specifically, a Mutual Information-guided Graph Aggregation (MIG) module first develops an MI-guided graph to exploit the tokens' informative interaction for activation enrichment, thus improving alignment probing and facilitating intervention.Subsequently, Misalignment-aware Adaptive Intervention (MAI) comprehensively perceives the token-level misalignment degree from token representation and prediction to guide the adaptive adjustment of editing strength, thereby enhancing final alignment performance.Extensive experiments on three alignment capabilities demonstrate the efficacy of TAE, notably surpassing baseline by 25.8% on the primary metric of truthfulness with minimal cost. 1 MHSA

Large Language Models (LLMs) have demonstrated broad applications but suffer from issues like hallucinations, erroneous outputs and outdated knowledge. Model editing emerges as an effective solution to refine knowledge in LLMs, yet existing methods typically depend on structured knowledge representations. However, real-world knowledge is primarily embedded within complex, unstructured text. Existing structured knowledge editing approaches face significant challenges when handling the entangled and intricate knowledge present in unstructured text, resulting in issues such as representation ambiguity and editing conflicts. To address these challenges, we propose a Conflict-Aware Knowledge Editing in the Wild (CAKE) framework, the first framework explicitly designed for editing knowledge extracted from wild unstructured text. CAKE comprises two core components: a Semantic-augmented Graph Representation module and a Conflict-aware Knowledge Editing strategy. The Semantic-augmented Graph Representation module enhances knowledge encoding through structural disambiguation, relational enrichment, and semantic diversification. Meanwhile, the Conflict-aware Knowledge Editing strategy utilizes a graph-theoretic coloring algorithm to disentangle conflicted edits by allocating them to orthogonal parameter subspaces, thereby effectively mitigating editing conflicts. Experimental results on the AKEW benchmark demonstrate that CAKE significantly outperforms existing methods, achieving a 15.43\% improvement in accuracy on llama3 editing tasks. Our framework successfully bridges the gap between unstructured textual knowledge and reliable model editing, enabling more robust and scalable updates for practical LLM applications.

Open world object detection (OWOD) has attracted extensive attention due to its practicability in the real world. Previous OWOD works manually designed unknown-discover strategies to select unknown proposals from the background, suffering from uncertainties without appropriate priors. In this paper, we claim the learning of object detection could be seen as an object-level feature-entanglement process, where unknown traits are propagated to the known proposals through convolutional operations and could be distilled to benefit unknown recognition without manual selection. Therefore, we propose a simple yet effective Annealing-based Label-Transfer framework, which sufficiently explores the known proposals to alleviate the uncertainties. Specifically, a Label-Transfer Learning paradigm is introduced to decouple the known and unknown features, while a Sawtooth Annealing Scheduling strategy is further employed to rebuild the decision boundaries of the known and unknown classes, thus promoting both known and unknown recognition. Moreover, previous OWOD works neglected the trade-off of known and unknown performance, and we thus introduce a metric called Equilibrium Index to comprehensively evaluate the effectiveness of the OWOD models. To the best of our knowledge, this is the first OWOD work without manual unknown selection. Extensive experiments conducted on the common-used benchmark validate that our model achieves superior detection performance (200% unknown mAP improvement with the even higher known detection performance) compared to other state-of-the-art methods. Our code is available at https://github.com/DIG-Beihang/ALLOW.git.