DySem is proposed, a novel training-free framework that investigates more semantic-related internal components of LLMs via multilingual consensus, and shifts away from static representation spaces in favor of dynamic, sample-specific semantic dimensions by constructing text-dependent joint semantic set and computes sim...
Kaijie Zheng, Wei-Qin Wang, Yi-Le Wang et al.· arXiv.org· 0 citations
CorVer (Corpus Verify) is a lightweight training-time approach to process supervision that derives sentence-level rewards from corpus co-occurrence statistics and outperforms the unmodified models in all standard factual-QA settings.
Shi-Chen Fan, Hao-Chang Hao, De-Hai Min et al.· 5 citations
ResearchMath-14k is introduced, a set of research-level mathematical problems curated from academic sources via a multi-agent pipeline that ranks above existing math datasets on knowledge, novelty, and procedural difficulty and suggests that research-level mathematical content and greater reasoning diversity may help e...
Experimental results across a broad range of domains show that MCPO alleviates performance degradation caused by mixed-domain training and outperforms single-domain training in most cases.
Zong-Ji Yu, Wen-Shui Luo, Yi-Liu Sun et al.· arXiv.org· 3 citations
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This work introduces HiMed, a Hindi reasoning medical corpus and benchmark suite covering both Western and Indian medicine and proposes HiMed-8B, a Hindi-form medical reasoning LLM, through the design of decaying scaffolding reward.
Dingfeng Jiang, Handi Yan, Chen Ma et al.· arXiv.org· 0 citations
A search-based memory framework called BOOKMARKS for active grounding, which retains access to the full preceding storyline and collects task-relevant information on demand, and improves next-action fidelity in a five-dimensional evaluation.
As Large Language Models (LLMs) are increasingly deployed in cross-linguistic contexts, ensuring safety across diverse regulatory and cultural environments has become a critical challenge. However, existing multilingual benchmarks largely rely on general risk taxonomies and machine-translated data, limiting evaluation...
Yunhan Zhao, Zhaorun Chen, Xingjun Ma et al.· 0 citations
We identify and systematically characterize a class of task-structural alignment failures in large language models (LLMs): even when the harmful intent remains unchanged, changing the task presentation and output constraints can substantially alter model safety behavior. Specifically, when a harmful request is reformul...
Yuheng Chen, Zhiyu Wu, Bowen Cheng et al.· 0 citations
Structured extraction of evidence from clinical trial publications underpins systematic reviews and clinical guidelines, yet large language models are adopted for it only hesitantly: their outputs are difficult to verify, their use commonly requires transmitting documents to proprietary services, and they do not improv...
Naman Ahuja, Abhijit Chakraborty, Muhammad Ali Khan et al.· 0 citations
Findings indicate that stylistic and structural properties of text provide a robust basis for distinguishing AI-generated misinformation from human-written fake news.
Samuel Jaeger, Calvin Ibeneye, Aya Vera-Jimenez et al.· arXiv.org· 2 citations
Active learning (AL) is a training paradigm for selecting unlabeled samples for annotation to improve model performance on a test set, which is useful when only a limited number of samples can be annotated. These algorithms often work by optimizing for the informativeness and diversity of the training data to be annota...
Lorenzo Jaime Yu Flores, Cesare Spinoso di-Piano, Ori Ernst et al.· 0 citations
SQLStructEval is introduced, a framework that analyzes this behavior through canonical abstract syntax tree representations and adopts a pipeline that first generates structured intermediate representations and then deterministically compiles them into SQL, improving execution accuracy and structural agreement among co...
Yi-Xi Zhou, Fan Zhang, Zhiyu Guo et al.· arXiv.org· 1 citation
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.
MIT News · Artificial Intelligence· news.mit.eduSep 24, 2026
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
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