Model checkpoints used by the artifact's second claim: the fine-tuned Qwen 0.5B checkpoint (wave 1, seed 42) and its AWQ 4-bit quantization. With these, a reviewer replays the claim instead of retraining. Data component of the research artifact of the paper Not All 4-bit Quantizers Are Equal: Deployment-Time Mitigation...
Cristhian Kapelinski, Diego Kreutz· Zenodo (CERN European Organi...· 0 citations
Research artifact of the paper Not All 4-bit Quantizers Are Equal: Deployment-Time Mitigation of PII Leakage in Fine-Tuned Small Language Models, published at SBSeg 2026 Main Track (full paper). Artifact evaluation (CTA/SBSeg 2026): this artifact was awarded all four seals: Available Artifacts (SeloD), Functional Artif...
Cristhian Kapelinski, Diego Kreutz· Zenodo (CERN European Organi...· 0 citations
Curriculum is a fundamental component of the education system that determines the direction, content, and process of learning. This study analyzes the various approaches used in Arabic language curriculum development and identifies the strengths and weaknesses of each approach within the context of Arabic language lear...
This paper explores the philosophical mystery hidden within the relationship between the Elephant and the Ant. What appears vast is not necessarily complete, and what appears subtle is not necessarily powerless. Through this simple contrast, the study questions human assumptions about magnitude, power, fear, knowledge...
Vedanta2.0 Agyat Agyani· Zenodo (CERN European Organi...· 0 citations
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We present a computational framework for multi‑dimensional value assessment, designed to address a structural problem shared by historical evaluation and AI value alignment: how to adjudicate among incommensurable values without collapsing them into a single score. Mainstream approaches to value aggregation seek a corr...
Wei Wan· Zenodo (CERN European Organi...· 0 citations
We introduce Grouped-Query Latent Sparse Attention (GQLSA), a novel attention mechanism that integrates latent compression, grouped-query sharing, and block-sparse attention into a unified hardware-native architecture. GQLSA achieves linear O(T) complexity in sequence length, a 3.8x speedup over multi-head attention at...
Fardin Sabid· Zenodo (CERN European Organi...· 0 citations
This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/22662611. Review of "Training Large Language Models for Small-Molecule Design with Synthetic Task Scaling" Summary This preprint asks whether LLMs can be post-trained for expensive,...
Sneh Pankajbhai Vora· Zenodo (CERN European Organi...· 0 citations
Retrieval-Augmented Generation (RAG) combines information retrieval with language generation to improve the grounding of generated answers in a source document, addressing a central weakness of purely parametric large language models: the tendency to produce fluent but unsupported statements. This study designs, implem...
Dileep Choudhary· Zenodo (CERN European Organi...· 0 citations
A method for digital analysis of threatening letters written in the Croatian language using machine learning algorithms, built upon the autoencoder architecture in combination with two text style vectorization methods.
L. Golubić, Ian Marković, A. Ledić et al.· Inventions· 0 citations
Retrieval-augmented generation (RAG) grounds a language model in retrieved documents, which reduces hallucination but creates a new attack surface: if retrieved text is tampered with, the model may repeat the falsehood. We study how much a small quantized model, Llama 3.1 8B, degrades when a fraction of its retrieved c...
Iliano Fasolino· Zenodo (CERN European Organi...· 0 citations
FAVE (Foveated Adaptive Visual Encoding), a lightweight variable-resolution ViT that encodes externally selected regions at high acuity while preserving native geometry, is introduced and integrated as a complementary local branch in FastVLM.
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Microsoft Research Blog· microsoft.comOct 15, 2026
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
