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natural language processing

9,568 papers

#artificial intelligence Preprint Open access Oct 2026

Think Twice: The Metis-1.6 Technical Report

Metis is a bet that better data, better training methods and new architectures can buy what a bigger parameter count cannot, at a fraction of the cost. Metis-1.6 is its latest step: two language models built from scratch by a single researcher, Praxis (2.94B parameters, 0.46B active per pass) and Logos (10.23B paramete...

Giulianno Vollmer · 0 citations
#artificial intelligence Preprint Oct 2026

Locating Sycophancy Across Training Stages in Open Language Models

Language model assistants sometimes change a correct answer when the user suggests a wrong one. The behavior, called sycophancy, is usually attributed to preference-based post-training, yet early measurements found it in pretrained models. We ask where it comes from by measuring it at every available stage of training:...

Jiarui Yao, Shweta Parihar, Nghia Bui et al. · 0 citations
#artificial intelligence Preprint Oct 2026

DuQUAD: A Dual-View Framework with Quality-Aware Evidence Pruning for Multi-Document Question Answering

DuQUAD is proposed, a multi-document QA framework that mitigates positional bias through dual-view reasoning and quality-aware evidence pruning, and substantially reducing computational cost compared to existing multi-agent frameworks.

Yeonjun Choi, Seyeong Jeong, Jong Wook Kim et al. · 0 citations
#artificial intelligence Preprint Oct 2026

Bouba Beats Kiki, and Other Regularities in the Subjective Preferences of LLMs

Users engage with large language models on tasks that are purely objective ("When was Netflix founded?"), purely subjective ("What should I name my child?") or anywhere in-between. We have rich tools for evaluating model accuracy with respect to objective criteria. However, when it comes to subjective aspects of a task...

Katy Blumer, Jon M. Kleinberg, Ravi Kumar et al. · 0 citations
#artificial intelligence Preprint Oct 2026

One Confidence Threshold, Unequal Coverage: Language Gaps in Typed Decision Interfaces and How Translating Inputs Reduces Them

Deployed classifiers often act on a prediction only when its confidence clears a threshold and pass the rest to a person. We ask whether one threshold serves all languages equally on typed decision interfaces, APIs that return a probability for each option of a fixed multiple-choice question. In a confirmatory study wi...

Jun-Wei Yao, Yong-Xin Zhou, Zi-Ke Yang et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

From Geometry to Behavior: How Instruction Exposure Unlocks Latent Rhetorical Directions in LLMs

Language models do not merely memorize facts; they absorb rhetorical strategies from pretraining data, including the human tendency to reason by analogy. We ask: is this absorbed capability geometrically accessible before any instruction tuning, and what determines whether it can be activated at inference time? We ex...

Shengjie Xu · 0 citations
#artificial intelligence Preprint Open access Oct 2026

An Introduction to Large Language Models for Scientists and Professionals

Large Language Models (LLMs) have rapidly become important tools for science, education, medicine, and professional work. Despite their apparent simplicity from the user's perspective, effective and responsible use requires an understanding of how these systems work, what their limitations are, and how their outputs sh...

Umberto Michelucci · 0 citations
#artificial intelligence Preprint Oct 2026

Prompt-agnostic Language Models

Despite their strong performance, large language models remain highly sensitive to prompt formulation. This issue is typically addressed at inference-time through manual prompt optimization, which introduces overhead and lacks scalability. Conversely, we focus on train-time approaches that build prompt robustness into...

Frederic Sadrieh · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Rank-Aware Differentially Private Release of Listwise Preferences for LLM Alignment

Listwise preference feedback offers richer supervision for large language model (LLMs) alignment than pairwise comparisons, but a full ranking also reveals more sensitive user preferences. Existing privacy-preserving alignment methods focus mainly on pairwise feedback, while listwise alignment under local differential...

Junwei Chen, Manjiang Yu, Pengpeng Qiao et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Supervised Fine-Tuning for Reasoning Large Language Models: A Survey

Large reasoning models have recently demonstrated remarkable capabilities in solving complex tasks, where supervised fine-tuning (SFT) on long chain-of-thought data serves as a crucial foundation for eliciting and enhancing their reasoning abilities. Despite rapid progress in both improving and analyzing reasoning-orie...

Yongcan Yu, Kuangpu Guo, Jian Liang et al. · 0 citations

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Microsoft Research Blog Oct 22, 2026

Flint-interactive: A language for creating interactive data visualizations

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.

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