N Lee

Jinsu Kim, Jihoon Tack, Noah Lee et al.

Language Models (LMs) have shown remarkable potential as role-playing chatbots, delivering consistent, stylized interactions when given a specification of a character or user persona. However, applying these capabilities to real-world applications (e.g., ecosystems with numerous NPCs interacting simultaneously) exposes a critical inefficiency due to the excessive computational cost. In this paper, we question the necessity of dedicating a full, generalist model to a single persona, hypothesizing that a specific character identity relies on only a fraction of the model's total capacity. We observe that naively pruning LMs often severely degrades the role-playing performance for a specific persona; it does not distinguish between redundant knowledge and essential character traits. We propose Persona-Pruner, a framework that sculpts a lightweight role-playing model by isolating persona-specific sub-networks from a single description. Our experiments consistently show that Persona-Pruner preserves role-playing performance substantially more effectively than existing state-of-the-art LLM pruning techniques, reducing the performance drop from the dense model by up to 93.8% over the strongest baseline on RoleBench in LLM-as-a-judge score, while still maintaining general LLM capabilities. Code is available at https://github.com/jsu-kim/Persona-Pruner.

Juha Park, Hyungseok Ko, Haejung Kim et al.

Low Earth Orbit (LEO) satellite communications (SATCOM) has emerged as a key enabler of global connectivity for 6G networks. To overcome the significant path loss of space-to-ground links, high-gain directional beamforming (BF) is indispensable. As LEO systems evolve toward wider bandwidths to support data-intensive applications, however, they encounter a fundamental physical limitation known as the beam-squint effect, which induces frequency-dependent beam misalignment. Conventionally, the beam-squint effect has been treated as a critical performance impairment that must be mitigated. This article introduces a paradigm shift in wideband LEO satellite systems by redefining beam-squint as a valuable source of frequency-spatial diversity and presents the principles of rainbow BF. Rather than mitigating beam squint, rainbow BF deliberately exploits it to generate frequency-dependent beams, enabling different frequency components to illuminate distinct spatial directions using only a single or a small number of radio frequency chains. By supporting dynamic frequency-spatial beam allocation, rainbow BF offers enhanced flexibility and scalability for wideband LEO SATCOM. We further illustrate the benefits of rainbow BF through three representative LEO SATCOM applications: i) massive multiple access to overcome the latency and throughput bottlenecks of conventional beam hopping; ii) integrated sensing and communications for simultaneous target detection and data transmission; and iii) rapid satellite acquisition to reduce search overhead and improve link reliability. Finally, we discuss key implementation challenges and outline promising future research directions for rainbow BF in wideband LEO SATCOM.

Seonghwan Park, Hyunji Jung, Dongyeop Lee et al.

Detecting out-of-distribution (OOD) data is crucial for reliable machine learning deployment. Among detection strategies, post-hoc methods are particularly attractive due to their efficiency, as they operate directly on pre-trained networks without requiring retraining. Within this paradigm, one promising direction exploits loss-landscape curvature to estimate model uncertainty; however, such methods incur substantial computational cost and rely on implicit assumptions about how landscape flatness differs between in-distribution (ID) and OOD data. In this work, we provide the first systematic investigation of this curvature discrepancy and show that OOD inputs exhibit larger Hessian curvature than ID data, with the gap widening under stronger distributional shifts. Motivated by these observations, we propose Fold, a lightweight flatness-modulated OOD detector that leverages the feature Hessian and partial feature normalization to improve ID-OOD separability while avoiding costly parameter-space curvature approximations. To optimally adapt this normalization across diverse datasets, we further introduce AutoFold, a self-supervised tuning scheme that synthesizes pseudo-OOD samples via ID logit masking for automatic calibration without requiring external data. Experiments on OOD benchmarks show that Fold outperforms prior methods, improving the average AUROC by 1.63% and reducing FPR95 by 2.30%, while maintaining computational efficiency comparable to a standard forward pass. Supported by theoretical analysis and extensive ablations, Fold provides a principled and practical solution for robust real-world deployment.

Chaewon Park, Soyoon Lee, Naeun Lee et al.

Real image editing enables precise manipulation of visual content, yet existing methods often fail in complex multi-object scenarios, causing semantic blending, object duplication, or incomplete edits. We attribute these failures to attention leakage, where signals across spatial regions and text tokens become entangled during the denoising process. Specifically, we identify two distinct forms of leakage: Edit-Token Leakage, where ambiguous token-region alignment leads to object blending, and Source Dominance Leakage, where tokens of unchanged source objects overwhelm the attention intended for target entities. To resolve these leakages, we propose \textbf{BindEdit}, which enforces attention-level constraints within a single diffusion trajectory. To suppress Edit-Token Leakage, BindEdit jointly regularizes cross- and self-attention so that each target token group is bound to its corresponding spatial region while maintaining instance-level separation. To suppress Source Dominance Leakage, a cross-attention re-balancing mechanism amplifies target token influence and attenuates residual source semantics within editable regions. Moreover, a region fidelity term ensures that each target concept is expressed coherently across the entire editing mask. Additionally, we propose a comprehensive multi-object benchmark encompassing diverse object counts and categories. Extensive experiments demonstrate that BindEdit consistently outperforms existing methods within a single diffusion trajectory, maintaining robust performance across both single- and multi-object editing scenarios.