arXiv subjects

Hong Shen

Publications and source records attributed to Hong Shen.

At least 19 recordsLinked to original sources

arXiv2026

Misgendering as Breakdown in Human-Machine Communication: How AI Companion Chatbot Users Experience and Repair Misgendering

In recent years, large language model-based AI companion and role play chatbots have grown increasingly popular. People turn to these chatbots for emotional support and to engage in romantic and erotic role play. Although prior research suggests that digital role play can help people explore their gender and sexuality, LLM based technologies are also replete with gender and sexuality biases. In this study, we examine one way that AI chatbots can harm users: misgendering. In order to study chatbot misgendering we qualitatively analyzed 326 posts mentioning misgendering that were shared in AI companion or role play subreddits. We document how chatbot misgendering takes place and how, in response, users engage in ongoing work to curate their gender presentation to prevent and repair misgendering. We discuss how researchers and designers can mitigate chatbot misgendering and consider the implications of using AI chatbots for identity exploration.

cs.HC↗

arXiv2026

Beyond the Personal Assistant: How Expectations for Enterprise AI in Teamwork Diverged as Generative AI Took Shape, 2023-2025

HCI often draws on users' articulated needs and expectations to explore design opportunities for emerging technologies. Yet, these accounts are shaped by how technologies take form over time. We examine this dynamic through a two-phase interview study of enterprise AI in a project-based software development organization. We interviewed 15 practitioners in 2023 and re-interviewed 10 in 2025. In 2023, participants expected enterprise AI built around teams: systems integrating signals across workplace tools, detecting coordination problems, and intervening in team processes. By 2025, no such team-focused systems had entered their work, while personal AI assistants were prevalent. These expectations diverged from one another: some expectations were deferred, some rejected as undesirable or infeasible, and some no longer articulated as expectations they had held. These trajectories show how design possibilities can remain open-ended, narrow, or disappear as technologies take shape, with implications for designing enterprise AI and interpreting present-day user accounts.

cs.HC↗

arXiv2026

Exploring K-12 Teachers' Perceptions of Students' Relationships with AI Companions: Boundaries, Intervention Strategies, and Design Implications

K-12 students increasingly form relationships with AI companions. Schools face growing expectations to teach AI literacy, yet existing frameworks treat AI as a tool rather than a relationship, and little is known about how teachers understand and act on students' relational use of AI. We conducted scenario-based interviews with 33 US K-12 teachers. Teachers welcomed academic companions but worried that intimate companions remove the developmental friction through which students learn to sustain human relationships. Teachers drew the boundaries of their jurisdiction by setting and observable wellbeing: within it they taught, talked, and watched; beyond it they positioned themselves as the adults best placed to notice and connect students with support. They envisioned AI companion literacy as shared work across the jurisdictions of counselors, parents, platforms, and policymakers, spiraling across grade levels. We introduce AI companion literacy as an extension of AI literacy and discuss implications for K-12 AI education.

cs.HC↗

arXiv2026

The Addictive Intimacy of AI: Understanding User Disengagement from AI Companions and Why Some Relationships with AI Become Difficult to Leave

AI chatbots are increasingly used as sources of emotional support, on dedicated companion apps and general-purpose assistants alike, yet little is known about what happens when users try to leave. Combining a content analysis of Reddit posts about quitting or reducing use (N=2,782) with interviews with users who found leaving difficult (N=16), we show that disengagement sometimes is not a single decision but a recursive trajectory: triggers prompt users to question the relationship, attempts to leave collide with barriers, and some users cycle through quitting and returning. We propose the notion of the addictive intimacy of AI, a configuration in which the qualities that make a companion emotionally valuable are the same ones that make it harder for users to limit their use and leave, so that intimacy and disengagement risk cannot be treated as independent design problems. We close with design implications for responsible offboarding.

cs.HC↗

arXiv2026

Unveiling Hidden Threats: Using Fractal Triggers to Boost Stealthiness of Distributed Backdoor Attacks in Federated Learning

Traditional distributed backdoor attacks (DBA) in federated learning improve stealthiness by decomposing global triggers into sub-triggers, which however requires more poisoned data to maintian the attck strength and hence increases the exposure risk. To overcome this defect, This paper proposes a novel method, namely Fractal-Triggerred Distributed Backdoor Attack (FTDBA), which leverages the self-similarity of fractals to enhance the feature strength of sub-triggers and hence significantly reduce the required poisoning volume for the same attack strength. To address the detectability of fractal structures in the frequency and gradient domains, we introduce a dynamic angular perturbation mechanism that adaptively adjusts perturbation intensity across the training phases to balance efficiency and stealthiness. Experiments show that FTDBA achieves a 92.3% attack success rate with only 62.4% of the poisoning volume required by traditional DBA methods, while reducing the detection rate by 22.8% and KL divergence by 41.2%. This study presents a low-exposure, high-efficiency paradigm for federated backdoor attacks and expands the application of fractal features in adversarial sample generation.

cs.CR↗

arXiv2026

Fine-grained Distributed Backdoor Attacks in Federated Learning

Federated learning, as a privacy-preserving distributed machine learning paradigm, faces significant threats from backdoor attacks. Compared to centralized attacks, distributed backdoor attacks are more harmful but require more poisoned samples to compensate for the loss of trigger strength due to decomposition. Fixed trigger patterns are also easily detected by robust aggregation algorithms, increasing the risk of attack exposure. To address these challenges, we propose a fine-grained distributed backdoor attack framework (FDBA). This framework uses dynamic trigger generation and embedding vector optimization to perform attacks with fewer poisoned samples. First, we design a dynamic trigger generation method based on image edge structures using the Canny algorithm to extract edge features, which are then injected with Laplacian noise. RGB channel decomposition is applied for covert adaptation of the distributed trigger, reducing detection chances. Second, we introduce an embedding vector contrastive learning strategy that forces poisoned samples to approach the target class center in the feature space, enhancing attack effectiveness. On CIFAR-10, piecewise-linear estimates for target ASRs between 70\% and 90\% show that FDBA reduces the required poisoning ratio by 37.4\%--48.4\% compared with DBA. In non-independent and identically distributed (Non-IID) scenarios, FDBA retains 84.7\% of its IID attack performance under extreme heterogeneity, whereas DBA drops to 73.5\%, and the framework successfully bypasses mainstream defense mechanisms. This study offers new insights into federated learning security and emphasizes the potential threats and defense challenges posed by fine-grained distributed attacks.

cs.LG↗

arXiv2026

Robust Decentralized Federated Distillation via Multi-Modality Knowledge Collaboration

This paper propose a robust decentralized federated distillation method that enables clients with heterogeneous models to collaborate through predictions on shared unlabeled public data. In the proposed method, each client first evaluates the received predictions in three modalities of class prediction, boundary decision, and prediction correlation. It then filters unreliable clients, assigns reliability-based weights to the retained clients, and constructs a teacher for each type of knowledge. Finally, the corresponding distillation gradients are validated using a supervised gradient computed from private data. Conflicting prediction and boundary gradients are removed, and conflicting relation gradients are suppressed before the final model update. We prove the convergence of the proposed method by showing stable local optimization for honest clients under Byzantine distillation. Particularly, we show that our method ensures a bounded Byzantine influence on both distillation gradients and individual client private gradients after cross-modality fusion, thereby enabling stable local optimization for honest clienunder Byzantine distillation. Extensive experiments on CIFAR-10 and CIFAR-100 demonstrate that the proposed method improves the prediction accuracy of heterogeneous models of clients under non-IID data and Byzantine attacks. As the booming demands of federated learning in decentralized environments such as edge computing and mission-oriented UAV collaborations, our method has a great potential for adoption of DFL in unreliable real-world scenarios where clients are exposed to receiver-specific Byzantine messages of malicious predictions.

cs.LG↗

arXiv2026

Robust Decentralized Personalized Federated Learning via Prediction-Constrained Neighborhood Collaboration

This paper proposes a robust decentralized personalized federated learning method R-DPFL, that enables clients to reduce the impact of Byzantine attacks via robust neighborhood direction estimation and history-based update trend prediction, rather than purely aggregating client models as in the existing work. In R-DPFL, each client first computes the current-round model update by aggregating the received neighborhood update vectors. It then predicts what this update should be based on its historical values and local model changes. Finally, R-DPFL computes the difference between these two quantities, adaptively clips this difference, and adds it to the local update. We prove convergence of the learning process through rigorous analysis and show that honest clients maintain stable personalized descent dynamics under Byzantine neighbor perturbations without requiring consensus among neighboring models. Extensive experiments on CIFAR-10 demonstrate that RDPFL consistently outperforms state-of-the-art decentralized and personalized federated learning baselines under heterogeneous and adversarial settings.

cs.LG↗

arXiv2026

Quantum information in neutron-proton scattering from the $M$ matrix

We study quantum-information aspects of neutron--proton scattering in the spin-space $M$-matrix framework. Four representative classes of input states are considered, namely diagonal mixed states, separable pure states, general two-qubit pure states, and a special Schmidt-like entangled subclass. For each class, ensemble-averaged output mutual information, reduced-state linear entropy, negativity, and geometric quantum discord are calculated in the relative momentum-- scattering angle plane. The results show that the outgoing spin correlations are governed jointly by scattering kinematics and by the structure of the incoming quantum ensemble. Input states with stronger intrinsic coherence or entanglement give larger maxima and higher minima in the mutual information, negativity, and geometric quantum discord. The enhanced regions of the mutual information and geometric discord depend on the input states, while the negativity maximum remains concentrated in the high-momentum backward-scattering region. These results extend earlier studies based on product-state entanglement power and provide an ensemble-based description of how spin correlations in neutron--proton scattering arise from the interplay between input-state structure and scattering dynamics.

nucl-th↗

arXiv2026

AEGIS: Attention-Embedding Gradient Isolation Shield - Triple-Channel Gradient Masking for Privacy-Preserving Federated LLM Fine-Tuning

Gradient inversion attacks recover private training text from gradients shared in federated learning, posing a serious threat to collaborative model training. Through our analysis of transformer gradient structure, we identify three channels through which private token information leaks: the attention output projection gradient exposes a low-rank subspace that encodes input embeddings (Channel 1), the embedding gradient's row-norm sparsity directly reveals which tokens are present (Channel 2), and the MLP expansion gradient carries a recoverable subspace signal analogous to Channel 1 (Channel 3). State-of-the-art attacks exploit these channels analytically to achieve near-exact token recovery in seconds. Existing defences address at most one channel and either degrade model utility or leave the remaining structural signals intact. We introduce AEGIS (Attention-Embedding Gradient Isolation Shield), a lightweight defence that closes all three analytical channels with three backward-path operations requiring no architectural changes: freezing attention projection parameters eliminates Channel 1 by construction, calibrated noise injection into the embedding gradient destroys Channel 2's token-presence signal, and analogous per-block noise injection into the MLP expansion gradient masks Channel 3. The same masked gradient drives both the local optimiser step and the server export, so no clean signal is retained on either side. Evaluated across 11 models and six datasets, AEGIS reduces token recovery rates to near zero against a range of gradient inversion attacks, both analytical and optimisation-based, while preserving or improving model utility. We provide formal guarantees for Channels 1 and 2 and validate the full defence empirically against adaptive adversaries with complete knowledge of the mechanism.

cs.CR↗

arXiv2026

Role of the $δ$ Meson in Softening the Symmetry Energy within the DDRHF Model

We investigate the effects of the isovector-scalar $δ$ meson on the density dependence of the symmetry energy within the density-dependent relativistic Hartree--Fock (DDRHF) framework. As a baseline, we generate $1006$ accepted DDRHF parametrizations including the $σ$, $ω$, $ρ$, and $π$ mesons by imposing empirical constraints on the saturation properties of nuclear matter. The resulting symmetry-energy slope parameters are confined to relatively large values, $L\simeq65$--$110~\mathrm{MeV}$. Two representative parametrizations, denoted RHF-NK1 and RHF-NK2, are randomly selected from this ensemble. Starting from these two parametrizations, we introduce the $δ$ meson and readjust the meson--nucleon couplings under the same saturation-property constraints. The numerical optimization shows that small values of $L$ are obtained most efficiently when the $δ$ coupling is taken to be constant. In this case, $L$ is reduced from approximately $73$ to $32~\mathrm{MeV}$, while the binding energy per nucleon, saturation density, symmetry energy, and incompressibility coefficient remain nearly unchanged. A channel-by-channel decomposition shows that the softening is not caused by the direct $δ$-meson contribution alone, but by a redistribution among the $δ$, $ρ$, and $π$ mesons together with the isoscalar Fock contributions. The resulting neutron-star mass--radius relations shift toward smaller radii, indicating that the $δ$ meson provides an efficient additional degree of freedom for controlling the isovector properties of DDRHF models.

nucl-th↗

arXiv2026

Influence of effective mass of the relativistic mean field theory on core collapse supernovae and compact objects

We study the influence of the effective mass in the relativistic mean field (RMF) theory on the properties of the central core of collapse-driven supernovae and the formation of compact objects. Influence of the effective mass has been so far studied within the non-relativistic frameworks. In order to clarify the role of the effective mass in the relativistic frameworks, which is different from non-relativistic ones, we adopt the set of equation of state (EOS) tables using the parameterizations TM1e and TM1m, which have different effective masses but with the same saturation properties, in the RMF theory. We show that choices of the effective mass in supernova matter affect both the stiffness of the EOS through pressure and the thermodynamical behavior through temperature under the RMF frameworks. We explore differences in matter evolution with neutrino emissions by performing a set of numerical simulations of the gravitational collapse and bounce of massive stars and the cooling of the proto-neutron stars. The EOS with large effective mass leads to compact proto-neutron stars and early collapse to black holes with high densities and temperatures due to the softness. It leads to high energy neutrinos in long emission from the proto-neutron star cooling and in short burst from the black hole formation.

astro-ph.HE↗

arXiv2026

BCNet: Bronchus Classification via Structure Guided Representation Learning

CT-based bronchial tree analysis is essential for diagnosing lung and airway diseases, yet automatic bronchus classification remains challenging because bronchial topology varies substantially across individuals. We propose the Bronchus Classification Network (BCNet), a structure-guided framework that uses segment-level topological information from point clouds to improve voxel-level representation learning. BCNet contains two jointly trained branches: a Point-Voxel Graph Neural Network (PV-GNN) for segment classification and a Convolutional Neural Network (CNN) for voxel-wise labeling. The branches share a common convolutional backbone, allowing topology-aware supervision from the PV-GNN to enhance voxel-level features. During inference, only the CNN branch is required, so BCNet retains the computational efficiency of its CNN baseline. Experiments on BronAtlas demonstrate that BCNet outperforms state-of-the-art methods by more than 8.0% in F1-score for bronchus classification. We also introduce BronAtlas, an open-access benchmark for bronchial imaging analysis that contains high-quality voxel-wise annotations of anatomical and abnormal bronchial segments. BronAtlas provides a valuable resource for developing and evaluating advanced methods for bronchial tree analysis, disease diagnosis, and surgical planning.

eess.IV↗

arXiv2026

AI LEGO: Scaffolding Cross-Functional Collaboration in Industrial Responsible AI Practices during Early Design Stages

Responsible AI (RAI) efforts increasingly emphasize the importance of addressing potential harms early in the AI development lifecycle through social-technical lenses. However, in cross-functional industry teams, this work is often stalled by a persistent coordination challenge: how technical roles hand off technical intent, how teams establish shared structures for collaboration, and how non-technical roles are supported in systematically evaluating harms. Through literature review and a semi-structured interview study with 8 practitioners, we unpack how this challenge manifests---technical design choices are rarely handed off in ways that support meaningful engagement by non-technical roles; collaborative workflows lack shared, visual structures to support mutual understanding; and non-technical practitioners are left without scaffolds for systematic harm evaluation. Existing tools like JIRA or Google Docs, while useful for product tracking, are ill-suited for supporting joint harm identification across roles, often requiring significant extra effort to align understanding. To address this, we developed AI LEGO, a web-based prototype that operationalizes the boundary object theory to support cross-functional AI practitioners in effectively facilitating knowledge handoff and identifying harmful design choices in the early design stages. Technical roles use interactive blocks to draft development plans, while non-technical roles engage with those blocks through stage-specific checklists and LLM-driven persona simulations to surface potential harms. In a study with 18 cross-functional practitioners, AI LEGO increased the volume and likelihood of harms identified compared to baseline worksheets. Participants found that its modular structure and persona prompts made harm identification more accessible, fostering clearer and more collaborative RAI practices in early design.

cs.HC↗

arXiv2026

Scheduling Coflows in Multi-Core OCS Networks with Performance Guarantee

Coflow provides a key application-layer abstraction for capturing communication patterns, enabling the efficient coordination of parallel data flows to reduce job completion times in distributed systems. Modern data center networks (DCNs) are employing multiple independent optical circuit switching (OCS) cores operating concurrently to meet the massive bandwidth demands of application jobs. However, existing coflow scheduling research primarily focuses on the single-core setting, with multi-core fabrics only for EPS (electrical packet switching) networks. To address this gap, this paper studies the coflow scheduling problem in multi-core OCS networks under the \textit{not-all-stop} reconfiguration model in which one circuit's reconfiguration does not interrupt other circuits. The challenges stem from two aspects: (i) cross-core coupling induced by traffic assignment across heterogeneous cores; and (ii) per-core OCS scheduling constraints, namely \textit{port exclusivity} and \textit{reconfiguration delay}. We propose an approximation algorithm that jointly integrates cross-core flow assignment and per-core circuit scheduling to minimize the total weighted coflow completion time (CCT) and establish a provable worst-case performance guarantee. Trace-driven simulations using real Facebook workloads demonstrate that our algorithm effectively reduces weighted CCT and tail CCT.

cs.DC↗

arXiv2026

Vortex Nucleons as Partial-Wave Filters in Nucleon--Nucleon Scattering

We propose vortex nucleon scattering as an angular-momentum-resolved probe of nucleon--nucleon partial waves. Using the standard $LSJ$ partial-wave $S$ matrix as input, we show that an on-axis vortex incident state with a fixed orbital angular-momentum projection $m_L=\ell$ imposes the direct selection rule $L\geq |\ell|$ on the initial nucleon--nucleon partial waves. As a result, the initial $S$ wave is excluded for $\ell=1$, while both the initial $S$ and $P$ waves are excluded for $\ell=2$. The underlying phase shifts are not modified. Instead, the vortex external state changes how the ordinary partial waves are projected into the scattering amplitude. We further analyze off-axis scattering, where the displacement of the target from the vortex axis introduces Bessel-function weights and partially relaxes the on-axis selection rule. These results suggest that vortex nucleons can provide a new experimental handle on the partial-wave content of the strong nucleon--nucleon interaction.

nucl-th↗

arXiv2026

An O(K)-Approximation Coflow Scheduling in K-Core Optical Circuit Switching Networks

Coflow has emerged as a fundamental application-layer abstraction in distributed systems, enabling collaborative management of related flows to enhance job completion efficiency. To meet the increasing bandwidth demands of modern data center networks (DCNs), optical circuit switches are widely deployed due to their high capacity and energy efficiency. Simultaneously, DCN deployments are evolving towards heterogeneous parallel architectures, where multiple independent optical circuit switching (OCS) cores operate concurrently to facilitate bandwidth expansion and incremental upgrades. However, existing research on coflow scheduling in multi-core switching fabrics primarily focuses on electrical packet switching (EPS) networks, with a few known results on OCS networks without or with a poor performance guarantee. This paper studies the coflow scheduling problem in multi-core OCS networks under the not-all-stop reconfiguration model, focusing on two major challenges of overcoming cross-core coupling for inter-core traffic allocation and satisfying the constraints of port exclusivity and reconfiguration overhead for intra-core circuit scheduling. To minimize total weighted coflow completion time (CCT), we propose an efficient algorithm by integrating LP-guided global coflow ordering, inter-core flow allocation and intra-core circuit scheduling that achieves approximation ratios of $8K$ and $\left(8K+1\right)$ for zero and arbitrary release times of coflows, respectively, where $K$ is the number of OCS cores. This framework is also applicable to $H$-core EPS networks, providing approximation guarantees of $4H$ and $\left(4H+1\right)$ for zero-time and arbitrary-time release, respectively.

cs.DC↗

arXiv2026

Beyond Symmetric Alignment: Spectral Diagnostics of Modality Imbalance in Vision-Language Models in the Medical Domain

Vision-Language Models (VLMs) struggle when applied to medical image-text data, yet the tools available to diagnose this failure remain limited. Existing representation alignment metrics are symmetric, collapsing both modalities into a single score and hiding which modality drives cross-modal degradation. We introduce the Spectral Alignment Score (SAS), an asymmetric metric that projects both modalities onto the principal eigenbasis of an anchor modality and computes eigenvalue-weighted per-eigenmode correlations, resulting in directional scores whose difference quantifies modality information imbalance. We embed SAS within a benchmarking framework evaluating 15 VLMs across natural and medical image-text datasets alongside 6 alignment metrics and bidirectional retrieval. Our experiments show that medical images retain richer structural information than their paired clinical reports, a directional asymmetry invisible to all competing metrics, and that SAS achieves the strongest zero-label correlation with retrieval performance in the medical domain, positioning it as a practical diagnostic tool for clinical deployment. Code is available at this URL: https://github.com/iamalegambetti/medical-vlms-assessment.

cs.CV↗