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natural language processing

9,568 papers

#artificial intelligence Preprint Oct 2026

Recurrent Looped Transformer

State tracking requires an update at every input, but the depth a Transformer applies to each token is fixed regardless of sequence length. We introduce the Recurrent Looped Transformer (RLT), which splits its layers between a parallel causal encoder and a recurrent decoder. At each token, the decoder merges the encode...

Yi-Fan Zhang, Ji-Chen Feng, Shi-Han Qin · 0 citations
#machine learning Preprint Oct 2026

Two Vectors Replace In-Context Demos: Structured Task Adaptation via Embeddings

In-context learning (ICL) adapts frozen large multimodal models (LMMs) to new tasks from a few demonstrations (demos), but re-encodes them at every query, where each demo image adds up to hundreds of visual tokens. Demo-free methods remove this cost with a compact task state. However, they add it at locations searched...

Xiong-Bin Ding, Nai-Chen Shi, Jia-Wei Zhang · 0 citations
#machine learning Preprint Open access Oct 2026

AccentCL: Robust Accent Classification with Incremental Expansion

Accent classifiers are typically trained with a fixed label inventory and cannot accommodate new accent categories as new data becomes available. Moreover, accented speech corpora often exhibit substantial class imbalance and/or domain shift due to differences in recording conditions across corpora. We present AccentCL...

Mu-Ruei Tseng, Waris Quamer, Ghady Nasrallah et al. · 0 citations
#artificial intelligence Preprint Oct 2026

Logbook: Extremely Long-form Audio Event Understanding

Audio benchmarks are built around short, pre-segmented clips, limiting model design to brief inputs or fixed vocabularies. To close this gap, we introduce Logbook, a benchmark for hour-scale audio understanding, with recordings ranging from ten minutes to six days. Given a continuous audio recording and an event label...

Kwanghee Choi, Suwon Shon, Dmitriy Serdyuk et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Lineage-Aware Memory Governance: A Derivation-Gated Framework for Privacy-Preserving Column-Level Access Control in Enterprise AI Agents

Enterprise AI agents that share a memory store face two unaddressed risks: sensitive data can leak through legitimately computed results the requester could not derive, and departments can silently compute a same-named key performance indicator (KPI) through conflicting logic. Existing agent-memory systems (e.g., MemGP...

Venkata M Sangaraju, Sudhir Vissa · 0 citations
#artificial intelligence Preprint Oct 2026

CroissantMiner: Automated Extraction and Validation of Croissant Metadata for ML Datasets

Croissant has emerged as a standard for machine-readable dataset metadata, yet populating its fields remains labor-intensive and requires careful reading of accompanying dataset documentation. We present the first benchmark enabling end-to-end evaluation of metadata extraction aligned with a community-standard schema....

Berke Arda, Ahmetcan Yavuz, Paul Gerry et al. · 0 citations
#artificial intelligence Preprint Oct 2026

Jailbreaking Open-Weight LLMs via Random Embedding Perturbations

While open-weight models have enjoyed steady progress in capabilities and wide adoption across multiple domains, their safety remains an important concern. One key feature is the ability to refuse or deflect harmful, malicious, or insensitive prompts. In this paper, we expose safety vulnerabilities across six common op...

A. Dubey, Scott Sirri, Vaggos Chatziafratis et al. · 0 citations
#artificial intelligence Preprint Oct 2026

Investigating Model Compression for Neural Machine Translation in the Biomedical Domain

Large-scale pretrained transformer models have achieved state-of-the-art performance across diverse machine translation tasks, including multilingual settings. Knowledge distillation has emerged as a sustainable approach for model compression, transferring knowledge from large teacher models to smaller, more efficient...

Maria Zafar, Souhail Bakkali, Rejwanul Haque · 0 citations
#machine learning Review Oct 2026

Calibrated Evidence Inference With a Small, Permissively Licensed Open Model

Systematic reviewers who use a model to read randomized trials need each answer and a measure of how far to trust it. If the model is calibrated, so that its stated probabilities match how often it is right, a reviewer can accept its confident answers and check the rest by hand. This paper fine-tunes Qwen3-4B-Base with...

Johann Li · 0 citations
#artificial intelligence Preprint Sep 2026

Component and Dimension Sparsity in Transformer Refusal Mechanisms

Together these findings show that refusal is not diffusely encoded across a transformer but assembled by a structured, identifiable mechanism, providing a foundation for mechanistic understanding of how refusal behaviors are represented and steered.

Vincent Siu, Glenn Grant-Richards, Vlad Pavlovich et al. · 0 citations
#artificial intelligence Preprint Sep 2026

Zero-Shot Visualization: Exploring Text Corpora with User-Prompted Axes

We study the application of large language models (LLMs) to the visual exploration of textual corpora. We introduce zero-shot visualization (ZSV), a task in which users specify concepts in natural language and documents are mapped onto the corresponding concept axes for visualization. Building a ZSV system of practical...

Arnau Bueno Tricas, J. A. Rodríguez-Serrano · 0 citations
#machine learning Preprint Oct 2026

A Systematic Study of Small Language Models on Abstract Reasoning Tasks

Endpoint accuracy on abstract-reasoning benchmarks does not reveal whether a language model has acquired a transferable rule or fit distribution-specific regularities. We study this distinction in small language models on the ARC-TGI benchmark, which organizes abstract grid transformations into controllable task famili...

Nur A. Zarin Nishat, Jens Lehmann, Andrei C. Aioanei 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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