N-K SCIENCES INTERNATIONAL PUBLICATION ZENODO SUBMISSION — DOI: 10.5281/zenodo.22759468 --- COMPLETE METADATA & DESCRIPTION Basic Information Field ValueTitle Rough Surface Turbulence in the Noor Ocean: Complete Deterministic Theory — Why Silica Particles Tilt in Microgravity, Why Reverse Swing Works, and Why the UN Dr...
Muhammad Usman Malik· Zenodo (CERN European Organi...· 0 citations
Across this controlled multilingual corpus, aggregate transcription-frequency metrics did not reliably track the sparse severe tail of clinically consequential errors, and these data do not support its use alone as a proxy for clinical safety.
H. Bergman, V. Liu, B. Austin et al.· medRxiv· 0 citations
Rank-Consistent Set Reasoning is presented, a supervised dense-prediction framework that models a group as an unordered set rather than as a sequence of images or a semantic label, and introduces a group permutation objective and hard-distractor augmentation so that the model learns the properties of a set-level target...
Experiments across multiple LLM backbones show that SecAwareCoder consistently improves Pass@1 and security robustness over prompting and analyzer-driven baselines, narrowing the security--correctness gap in LLM code generation.
Jie-Xin Wang, Liu-Wen Cao, Xi-Tong Luo et al.· Proceedings of the ACM on So...· 54 citations· ⚡5
Reach audiences
Advertise in front of researchers, engineers, and readers.
Our team, VANGUARD, presents IROH (Insightful Ranking of Humor), a three-stage retrieval system for JOKER Task 1 English at CLEF 2026, achieving first place on the leaderboard with 0.6347 MAP. Our pipeline combines hybrid sparse-dense retrieval, cross-encoder reranking, and a LoRA-adapted Large Language Model judge ens...
A. Mocanu, Sebastian Mocanu, Ciprian-Octavian Truică et al.· 0 citations
URCHIN (Unified Recurrent Connectome with Horizontal Integrate-and-fire Neurons), which applies the Parallelized Hierarchical Connectome Spiking State-space Model (PHCSSM) to language modeling and offers a biologically plausible, efficient, and directly deployable reference point.
Multilingual Large Language Models (LLMs) traditionally rely on a single vocabulary shared by all supported languages, which can lead to uneven compression across them. Moreover, their large embedding and output matrices increase memory usage and slow inference, notably for small-scale models. It is also wasteful as mo...
Franck Signe, Hippolyte Pilchen, François Yvon et al.· 0 citations
The North Small Translate model is presented, an open-weight, LLM-based machine translation (MT) model with instruction-following capabilities built on the same foundation as Cohere's Command A Plus, a mixture-of-experts architecture with 25 billion active parameters out of 218 billion total parameters.
Tom Kocmi, Alexandre Berard, P. Blunsom et al.· 0 citations
Findings motivate variety-aware language identifiers, references, and reporting for cross-border languages or language dialects/variants for cross-border languages or language dialects/variants.
Felermino D. M. A. Ali, D. Mateus, M. Mangue· 0 citations
Large language models (LLMs) are increasingly used for emotional support and relationship advice, where a model's tendency to preserve a user's face can inadvertently reinforce harmful interpersonal behaviors. To systematically examine this risk, we developed the Romantic Relationship Advice-Seeking Prompts (RRASP) dat...
Helen Choi, Edric Castel C. Hao, K. Bautista et al.· 0 citations
This work focuses on machine translation models for Pacific creoles: Tok Pisin, Bislama, and Solomon Pijin, with particular attention to broad domain performance, and relies on a number of data collection and preparation techniques, including LLM-assisted respelling and alignment, back-translation, and distillation fro...
Raphael Merx, Nick Thieberger, Ekaterina Vylomova· 0 citations
It is shown that lightweight reference set distributional diagnostics can reveal model-specific instability patterns during sequential LoRA personalization of SLMs, including cases where task-level metrics alone hide harmful adaptation.
Thomas S. Paula, L. Kupssinskü, Rodrigo C. Barros· arXiv.org· 0 citations
