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8,228 papers
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.
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.
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.
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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...
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...
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...
Findings indicate that stylistic and structural properties of text provide a robust basis for distinguishing AI-generated misinformation from human-written fake news.
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...
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...
This work reframing proposal generation as a time-sliced scientific forecasting problem, given a research question and inspiring papers available before a cutoff time, and operationalizes this objective with the Future Alignment Score (FAS), computed via retrieval and LLM-based semantic scoring against a held-out futur...
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.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
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.
A new language-processing tool could help identify the highest-risk individuals from natural language, enabling swifter interventions.
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