VeriX-Anon is a multi-layered verification framework for outsourced Target-Driven k-anonymization combining three orthogonal mechanisms: deterministic verification via Merkle-style hashing of an Authenticated Decision Tree, probabilistic verification via Boundary Sentinels and exact-duplicate Twins with cryptographic identifiers, and utility-based verification that compares SHAP value distributions before and after anonymization using the Wasserstein distance.
This work proposes a VQA-based fine-tuning strategy that trains models to answer structured questions about visual content rather than relying solely on captions or simple instructions, which encourages deeper visual grounding and reasoning.
Tanzila Rahman, Renjie Liao, Leonid Sigal· 0 citations
This work explores a proof-of-concept application of Deep Operator Networks (DeepONets) as a surrogate for the Simulating WAves Nearshore (SWAN) numerical wave model, and demonstrates consistently high accuracy in predicting the significant wave height and the x- and y- components of the radiation stress gradient.
Shukai Cai, Sourav Dutta, Mark Loveland et al.· Ocean Engineering· 0 citations
This work derives a closed-form expression for this adversarial perturbation, bypassing the iterative inner optimization of adversarial training entirely and enabling linear-time evaluation in the state dimension, and shows that this expression approximates the exact minimizer of the value function over the modeled uncertainty set with second-order accuracy.
Alex Zongo, Filippos Fotiadis, U. Topcu et al.· arXiv.org· 1 citation
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PeopleSearchBench, an open-source benchmark comprising 119 multilingual queries across four scenarios: corporate recruiting, B2B sales prospecting, expert search, and influencer discovery, finds that multi-source search agents significantly outperform single-domain systems, particularly in influencer discovery where the performance gap is largest.
Tianyu Shi, Wei Wang, Zequn Xie et al.· 0 citations
Under a manifold model with the frame bundle structure, it is shown that it can accurately recover the parallel transport with landmark-constrained diffusion from a point cloud, and hence asymptotically LA-VDM converges to the connection Laplacian.
Numerical experiments demonstrate competitive accuracy and lower computational cost than expectation-maximization across a range of multidimensional settings.
This paper replicates and extends the system used in the AuTexTification shared task for authorship attribution of machine-generated texts, and tested newer multilingual language models and added 26 document-level stylometric features, using ablation, permutation importance, and SHAP analysis to assess feature influence.
Adam Skurla, D. Macko, Jakub Simko· arXiv.org· 0 citations
This work proposes a multi-stage alignment method that teaches models to recall and apply relevant business policies during chain-of-thought reasoning at inference time, without including the full business policy in-context.
Shubhashis Roy Dipta, Daniel Bis, Kun Zhou et al.· arXiv.org· 6 citations
NanoVDR exploits query--document asymmetry by decoupling the two encoding paths: a frozen 2B VLM teacher indexes documents offline, while a distilled text-only student as small as 69M parameters encodes queries at inference, and the resulting NanoVDR-S-Multi (DistilBERT, 69M) retains 95.1% of teacher quality.
Zhu Liu, Yao Zhang, Yuntian Xiao· arXiv.org· 1 citation
This work introduces an RKHS localization method that learns a data-adaptive weight from covariates and reformulates the target conditional moment at the target point as a weighted unconditional moment, yielding a prediction-powered estimator and confidence interval that reduce variance when the predictor is informative while preserving validity regardless of predictor accuracy.
Yang Sui, Jin Zhou, Hua Zhou et al.· arXiv.org· 2 citations
This work proposes DesignAsCode, a novel framework that reimagines graphic design as a programmatic synthesis task using HTML/CSS, incorporating a Plan-Implement-Reflect pipeline, incorporating a Semantic Planner to construct dynamic, variable-depth element hierarchies and a Visual-Aware Reflection mechanism that optimizes the code to rectify rendering artifacts.
A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.
Adaptive AI agents can help make BIM data more machine-readable by navigating IFC models, interpreting inconsistent information, and mapping it to defined standards. In this blog, Alok Rawat shares findings from a real-world pilot in construction workflows. The post Adaptive AI Agents in Construction Workflows appeared first on GPT-Lab.
