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Works15 from public data
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- MGLEP: Multimodal Graph Learning for Modeling Emerging Pandemics with Big Data9
A novel framework that integrates temporal graph neural networks and multi-modal data for learning and forecasting, and incorporates big data sources, including social media content, by utilizing specific pre-trained language models and discovering the underlying graph structure among users is proposed.
- Reasoning Transfer for an Extremely Low-Resource and Endangered Language: Bridging Languages Through Sample-Efficient Language Understanding7
This work first investigates CoT techniques in extremely low-resource scenarios through previous prompting, model editing, and fine-tuning approaches, and introduces English-Pivoted CoT Training, leveraging the insight that LLMs internally operate in a latent space aligned toward the dominant language.
- NeuProNet: neural profiling networks for sound classification7
This work proposes novel neural profiling networks (NeuProNet) capable of learning and extracting high-level unique profile representations from sounds, and introduces an in-batch profile grouping mechanism based on profile awareness and attention pooling to produce reliable and robust features with contrastive learning.
- IRLBench: A Multi-modal, Culturally Grounded, Parallel Irish-English Benchmark for Open-Ended LLM Reasoning Evaluation6
This work exposes systemic weaknesses in today's multilingual LLMs and provides a rigorous benchmark for evaluating true multilingual capabilities, and releases IRLBench and an accompanying evaluation codebase to enable future research on robust, culturally aware multilingual AI development.
- ToolBrain: A Flexible Reinforcement Learning Framework for Agentic Tools3
This work presents ToolBrain, an open-source platform that addresses the challenge of training language-enabled agents to reliably use tools through the Coach–Athlete paradigm, an architectural abstraction that simplifies the application of reinforcement learning to agentic workflows.
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- Personalization for Robust Voice Pathology Detection in Sound Waves–
A non-invasive framework that contrastively learns personalization from sound waves as a pre-train and predicts latent-spaced profile features through semi-supervised learning is proposed, which allows all subjects from various distributions to benefit from personalized predictions for robust voice pathology in a privacy-fulfilled manner.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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