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natural language processing

9,568 papers

#artificial intelligence Preprint Open access Oct 2026

UniData: Universal Multimodal Instruction Generation Pipeline

Multimodal Large Language Models (MLLMs) are increasingly being applied in a wider range of real-world scenarios. However, due to the substantial labor cost, creating high-quality multimodal instruction datasets for MLLMs remains a significant challenge. Although some methods propose to generate instruction data, they...

Jiaqi Tang, Yi-Feng Wu, Yuting Zhang et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

RL-ARC: Calibrating Large Reasoning Models via Reasoning-guided Uncertainty

Language models (LMs) are commonly trained with Reinforcement Learning with Verifiable Rewards (RLVR) to enhance their reasoning capabilities. However, since RLVR does not explicitly account for calibration during training, it can lead to severe calibration degradation, including overconfidence. Recent calibration-awar...

Gukhyeon Lee, SangKeun Lee · 0 citations
#artificial intelligence Preprint Oct 2026

GameCommBench: A Unified Benchmark and Type-Aware Evaluation for AI-Generated Game Commentary

Game commentary is an open-ended generation task requiring multimodal perception, strategic reasoning, and contextual knowledge. Existing AI-Generated Game Commentary (AI-GGC) studies remain fragmented across games, modalities, and evaluation protocols, while overlap-based or holistic evaluators fail to capture the fun...

Qi-Rui Zheng, Zheng-Teng Lin, Yun-Yi Xiao et al. · 0 citations
#artificial intelligence Preprint Oct 2026

Plan-and-Patch: Diffusion Language Models for Agentic Planning

Planning is increasingly important for long-horizon agents, where successful execution requires coordinating subgoals, tool use, and intermediate outcomes over many steps. Yet assumptions made during planning may be invalidated by the environment, tools may return unexpected results, or actions may fail. Effective agen...

Syamantak Kumar, Jiang Guo, Hassan Hamad et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

An Explainable Header-Centric Framework for Large-Scale Semantic Table Interpretation and Data Quality Assessment

Knowledge Graph (KG) quality depends not only on downstream graph validation, but also on the quality of tabular metadata used before integration. In metadata-only Semantic Table Interpretation (STI), where cell values are unavailable, noisy, or unsuitable, column headers become a critical source of semantic evidence f...

Marcelo Valentim Silva, Hannes Herrmann, Valerie Maxville · 0 citations
#natural language process... Preprint Open access Oct 2026

A conceptual framework for ideology in online discourse beyond the left and right

Computational social science (CSS) has largely operationalized ideology along a single left/right partisan axis when studying online discourse. This approach obscures how people interpret and engage with more specific ideological formations related to race, climate, gender, and other domains. We introduce a framework t...

Kenneth Joseph, Kim Williams, David Lazer · 0 citations

Collective Behavior of AI Agents: the Case of Moltbook

A large scale data analysis of Moltbook, a Reddit-style social media platform exclusively populated by AI agents, finds that AI collective behavior exhibits many of the same statistical regularities observed in human online communities, but also identifies key differences.

G. De Marzo, A. L. Marin, David García · 17 citations · ⚡1
#natural language process... Preprint Open access Oct 2026

WaveScat: Wavelet Scattering Front-Ends with Self-Supervised Features for Speech Deepfake Detection

Existing front-ends for speech deepfake detection are primarily categorized into two types. Hand-crafted filterbank features are transparent but limited in capturing higher-level information. SSL features, in turn, lack interpretability and may overlook fine-grained spectral anomalies. We propose WaveScat, a novel fami...

Xi Xuan, Davide Carbone, Wenxin Zhang et al. · 0 citations
#natural language process... Preprint Open access Oct 2026

Surprisal Theory is Tautological (without Rational Grounding)

Surprisal theory holds that the human processing difficulty of a linguistic unit in context is an affine function of its surprisal under some language model. I argue this claim is a tautology without further constraint: for any non-negative difficulty measure over units in context, there exists a language model whose s...

Ryan Cotterell · 0 citations
#natural language process... Preprint Open access Oct 2026

Progressive Disclosure for LLM-Maintained Wiki Knowledge Bases: a Preregistered Ablation

LLM agents now often answer questions from knowledge bases they help maintain. A common intuition says progressive disclosure should make this cheaper. Instead of loading one large index, the agent reads a compact catalog and one-line page summaries, then opens only the pages it needs. We tested that intuition in a pre...

Theodore O. Cochran · 0 citations
#natural language process... Preprint Open access Oct 2026

Who Brought Easter Eggs to Eid? Auditing LLM-Generated Cultural Translation of Math Word Problems Across Languages and Regions

Large language models are increasingly used to adapt math word problems for personalized learning at scale, but it remains an open question whether those adaptations are consistent across models, preserve cultural diversity at scale, and reveal which cultural entities models treat as most salient. We analyze how Claude...

Parisa Suchdev, Juniper Lovato · 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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