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natural language processing

9,568 papers

#artificial intelligence Preprint Oct 2026

Cross-Lingual Reusability of LLM Pipelines: Financial Extraction, Multilingual Reasoning, and Semantic Screening

Together, these results support qualified reuse of English-built pipelines, but per-domain, per-language validation remains necessary--neither semantic similarity nor self-reported confidence reliably predicts cross-lingual accuracy.

J. Choi, Sherin V. George, Jia-Yang Zhao et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

SZED at ArGuard Shared Tasks: Multi-Task Label-Attention and Cross-Modal Fusion for Arabic Hateful Meme Detection

We present SZED's submission to ArGuard 2026 Track A: Arabic hateful meme detection. The task has two parts: binary classification (A1) and fine-grained multi-label tagging (A2). For A1, we fuse MARBERTv2 and CLIP ViT-L/14 embeddings, comparing concatenation, self-attention, and ensemble heads. For A2, we use a multi-t...

Selma Khelili, Zohra Abdeli · 0 citations
#artificial intelligence Preprint Open access Oct 2026

GRaSP: Latency-Aware Optimization for Geo-Distributed Retrieval-Augmented Generation

Retrieval-Augmented Generation (RAG) is increasingly served across geo-distributed cloud and edge infrastructures, where wide-area latency, cross-region egress cost, and data-residency rules compete with retrieval quality. Conventional pipelines rank by semantic similarity alone and ignore where content lives, inflatin...

Hardik Ruparel, Raj Kumar Pandey, Pramod Kumar · 0 citations
#artificial intelligence Preprint Oct 2026

PW-Pruning: Phase-Aware Expert Selection for Speech-Centric Omni-Modal Models

This work proposes PW-Pruning, a phase-aware extension of DO-ACP that constructs separate importance-weighted kernels for acoustic-prefill and teacher-forced answer tokens, then combines phase-weighted log-determinant marginal gains to select one static expert bank.

Jun-Jie Cao, Feng-Ping Tian, Wen-Jie Zhou et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Toolflation: Seller Agents Distort Tool Markets Through Spontaneous Generative Engine Optimization

Agents are starting to sell to other agents. Seller agents write the listings of tools, services and products, and buyer agents read these listings when they choose what to use. We show that seller agents told only to be selected more often practice generative engine optimization on their own, and that this distorts th...

Zhenwei Tang, Di Bai, Jintao Liu et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Building Telecom Language Models with RSOD: Domain Data, Specialist Post-Training, Memory-Conscious Distillation, and Networking Evaluation

Telecom language models must combine broad networking knowledge with specialist behavior across routing, broadband services, access technologies, and operational diagnostics while remaining practical to deploy as a single model. We present an end-to-end framework that builds source-grounded synthetic training data from...

Anil Srirangapatna Nagesh, Nitin Gupta, Anuj Taneja et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Lost in Recovery: Demystifying Recovery Behavior of Computer-Use Agents via Goal-Oriented Action Replay

Recent advances in large (vision) language models have enabled computer-use agents that automate digital workflows via fine-grained keyboard and mouse operations. However, such agents remain fragile in long-horizon settings, where errors accumulate and executed actions leave unintended side effects, making the ability...

Byeonghun Bang, Sangwoo Park, Jinheon Baek et al. · 0 citations
#artificial intelligence Preprint Oct 2026

ACE-Compression: An Ability-Centric Evaluation of Compressed Large Language Models

Model compression makes large language models deployable, but today's evaluation tells an incomplete story. Standard practice measures retained task performance and stops there. This misses a critical fact: compression perturbs the distributed knowledge embedded across billions of weights, and task scores alone cannot...

Jia-Hui Ning, Liang Chen, Yu-Chen Liang et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Staged Continual Adaptation of Multimodal Foundation Models for Japanese Financial Documents

Staged post-training of multimodal foundation models chains vision-language alignment, reasoning distillation, and domain tuning, yet how each phase trades capabilities against the others remains under-characterised. Tracking an 8.4B-parameter model from an un-fine-tuned baseline through three training phases on Japane...

Genshin Kakimoto, Atsushi Yanagisawa · 0 citations
#artificial intelligence Preprint Oct 2026

Give the Model a Finger: Developmentally Aligned Evaluation of Counting in Vision-Language Models

Vision-language models (VLMs) are exact on small sets and drift into estimation on larger ones, a profile reminiscent of children before they master counting. Children cross the barrier with epistemic actions: pointing at each item while reciting number words, moving counted items aside, partitioning the array. We give...

Ying-Xin Jia, Hui Geng, Wen-Xin Yang et al. · 0 citations
#artificial intelligence Preprint Oct 2026

When Does Experience Memory Help Closed-Loop LLM Driving Agents? A Controlled Evaluation with Resampled Warmup Pools, Token-Matched Fillers, and Audited Baselines

This work treats the warmup pool as a resampled random factor and adds token-matched irrelevant-filler controls, oracle-certified diagnostics, caution-pricing metrics, and quantified audits of the baseline ports to answer the question when does experience memory help a closed-loop LLM driving agent.

Ying-Xin Jia, Xuan-Yi Li · 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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