goalbench measures small "decider" models that answer typed choice questions (Laya, Strands Decider) on customer-goal classification in Brazilian Portuguese, inside a two-stage pipeline: an embedding shortlist of 10 goals followed by the decider, with a calibrated combination fitted by cross-validation. Runs log every...
Fulvio Jorge da Silva· Zenodo (CERN European Organi...· 0 citations
This dataset repository contains all raw data files, processed matrices, spatial interpolation grids, and reproducible R scripts associated with the ecological, sedimentological, and geochemical assessment of estuarine beaches in Cajueiro and Carimã (Northern Coast of Brazil), sampled across different seasonal periods...
Gerson dos Santos Protazio, Wallacy Borges Teixeira Silva, Veronica R. L. Oliveira et al.· Mendeley Data· 0 citations
v4.5.0 adds Google Cloud integration: Gemini through Vertex AI in the user's own project with their gcloud login, keys read from Google Secret Manager instead of a plaintext file, and CI on Google Cloud Build; it also includes NCLM-1 workflow and verify-loop changes. See RELEASE_NOTES_v4.5.0.md. OSIRIS is a local-first...
Devin Phillip Davis· Zenodo (CERN European Organi...· 0 citations
EL-RAKHAWI BEACON: EXECUTIVE SUMMARY The El-Rakhawi Beacon is a symbiotic convergence system integrating four life-sustaining functions into a single solar-powered unit: atmospheric water generation (8-40 L/day via MOF sorbents), hybrid energy storage (LFP battery 5kWh), edge AI with mesh networking, and environmental-...
mohamed kamal arafa el-rakhawi· Zenodo (CERN European Organi...· 0 citations
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Language models can write research hypotheses much faster than anyone can test them. Earlier studies have judged such hypotheses by expert ratings, benchmarks with known answers or laboratory experiments. We report a small, fully documented case from a different angle: what happens to a handful of model-generated predi...
Mariusz Kulma· Zenodo (CERN European Organi...· 0 citations
In the 1980s, Daniel Gile thought of the potential benefit for translation and interpreting students of simple conceptual building blocks that could help them make sense of their classroom experience and of their instructors’ advice, especially as regards recurring challenges. The first edition of Basic Concepts and Mo...
Daniel Gile· Benjamins translation librar...· 0 citations
This record provides the evaluation data accompanying the paper “A Safety-First Two-Stage Mental Health SupportChatbot: Robust Crisis Routing andRetrieval-Grounded Response Generation” (SU26 DAP Group 4, FPT University, Can Tho Campus). The data is designed to stress-test the robustness of a two-stage mental-health sup...
Vinh Hung Ho· Zenodo (CERN European Organi...· 0 citations
Overview In this Zenodo upload, we introduce both the code for SCARSE (scarse.zip) and the reproducability code (SCARSE-repro.zip). How SCARSE works SCARSE is designed for peptide property prediction in low-data regimes by combining protein language model embeddings with classical machine learning methods. The workflow...
Leo Andrekson, Robin Rydbergh, Rocío Mercado et al.· Zenodo (CERN European Organi...· 0 citations
Supporting data, adapters, predictions and code for the article *Low-Cost LoRA Fine-Tuning of Small Language Models for Multi-Step Arithmetic Reasoning* by Jake O'Grady, Asena Isik Gürhan, Chee Fong Ting and Effirul Ramlan (University of Galway). We generated 20,000 GSM8K-derived arithmetic problems with step-by-step s...
O'Grady, Jake, Gürhan, Asena Isik, Chee, Fong Ting et al.· Zenodo (CERN European Organi...· 465 citations
Skill-SLM is proposed, a framework that reformulates SLM-driven robot operation as a task-decomposition and skill-composition problem, and employs a progressive skill orchestration strategy to improve the reliability of skill implementation and overall robot operation.
Listen-to-Reason (L2R), an interpretable-by-design pipeline that passes audio to the LLM through an explicit, human-readable tree that outperforms all LALMs the authors compare against on SAKURA and trails them by 6-12 points on MMAU and MMAR, is proposed.
Pooneh Mousavi, M. Ravanelli, Cem Subakan· 0 citations
Large language models (LLMs) exhibit us-vs.-them bias: A behavioral asymmetry in which prompts framed around an ingroup (``we''/``us'') receive systematically more positive continuations than matched prompts framed around an outgroup (``they''/``them''). Using Edge Attribution Patching (EAP), we localize this behavior...
Tabia Tanzin Prama, J. Zimmerman, C. Danforth et al.· 0 citations
