Federated learning lets multiple parties train a shared model without pooling their data, but a client with far less data than the others can end up poorly served even when the group's average accuracy looks fine. We ask whether the choice of pretrained backbone affects this under LoRA fine-tuning, and whether per-word...
Kiran Naseer, Samreen Azhar, Umar Shoaib et al.· 0 citations
A novel Reliability-Aware framework for MMEA (RA-MMEA), which assesses visual reliability and adaptively improves unreliable visual representations for robust entity alignment and enables more reliable visual representations for modality fusion, thereby improving performance.
Chen-Xiao Li, Yun-He Feng, Dongfang Liu et al.· 0 citations
Findings show that many peer-review evaluation metrics partially conflate review quality with linguistic presentation, and indicate that robustness to meaning-preserving rewriting should be validated before such metrics are used to compare human-written, AI-assisted, and AI-generated reviews.
Shakiba Amirshahi, Sajad Ebrahimi, Hai-Son Le et al.· 0 citations
This work organises the field of automated fact-checking by what is being formalised instead, which gives five levels: the claim, the reasoning, the system doing the checking, the ecosystem the claim spreads through, and the regulatory obligation.
Nikolaos Kekatos, Theodoros Nestoridis, Charalampos Bratsas et al.· 0 citations
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A controlled diagnostic evaluation framework for CLIR over industrial technical text is established and some multilingual embedding models that perform strongly in monolingual settings exhibit sharp declines when queries and documents are in different languages, providing practical insights for model selection in cross...
Mahdi Astaraki, Mohammad Khodadad, Reza Namazi et al.· Proceedings of the 2026 Conf...· 0 citations
Euston is presented, an 8B mathematical claim-verification model trained to resist the all-false composition of the official evaluation sets and the low precision implied at realistic error prevalence, which is principally the all-false composition of the official evaluation sets.
This work proposes to enhance acoustic representation trough a cross-modal transfer knowledge approach, based on heterogeneous graph neural networks (HGNNs), where acoustic and linguistic entities are modeled as distinct node types within a unified graph.
Y. Y. Nzeuhang, Paulin Melatagia Yonta, Marie Tahon· 0 citations
This work proposes a data-efficient space alignment method based on Heterogeneous Graph Neural Networks and link prediction that yields results comparable to those of SAMU-XLSR, a state-of-the-art method, and even surpasses it for the Yemba language in the task of word retrieval, while using far fewer resources.
Y. Y. Nzeuhang, Marie Tahon, Paulin Melatagia Yonta· 0 citations
Language models are appealing tools for research on the past. But to trust the evidence a model provides, researchers need to know whether its responses fit the period represented. Validation is challenging, because this is not a task living people ordinarily perform, and because many questions have multiple correct an...
Ted Underwood, Zi-Liang Qiu, Sarah Griebel et al.· 0 citations
The value of curated, capability-balanced supervision for adapting general language models to educational tasks spanning problem solving, curriculum understanding, and instructional support is demonstrated.
Hao Liang, Qi-Han Lin, Mei-Yi Qiang et al.· 0 citations
An analysis of model internals revealed a low-dimensional space with three interpretable dimensions matching a formal pre-specified framework for the geometry of legal concepts, showing how legal thought in LLMs may be organized and directed.
Peng Qian, Andrew Li, Sam S. Chen et al.· 0 citations
Agentic retrieval-augmented generation (RAG) enables language models to adapt retrieval based on previously retrieved evidence, but it remains unclear whether such adaptive orchestration consistently outperforms well-designed static pipelines. We conduct a controlled comparison of agentic and static RAG for Taiwanese h...
Kai-Hsin Chen, Wei-Yuan Chen, Xuan-Bo Chen et al.· 0 citations
Flint-interactive gives AI agents a language for creating interactive visualizations. Click a chart, filter data, highlight related content, and explore beyond the view the author originally chose. The post Flint-interactive: A language for creating interactive data visualizations appeared first on Microsoft Research.
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.
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