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natural language processing

9,568 papers

#artificial intelligence Preprint Oct 2026

Hybrid Latent Attention for Looped Language Models

Looped language models apply the same stack of layers T times to each token, which deepens the model without adding parameters but multiplies its key-value (KV) cache by T. The larger cache limits how many sequences a GPU can decode at once and slows each decoding step, which reads the whole cache. We propose Hybrid La...

Yu-Han Chen, Si-Yuan Zhang, Nan Wang et al. · 2 citations
#natural language process... Preprint Open access Oct 2026

Leveraging a four-quadrant approach for evaluating Redpine Science

Redpine Science gives models and agents a single access point to a wide range of peer-reviewed literature, queried directly through the Model Context Protocol (MCP) and an API. This report evaluates Redpine Science on two levels: the relevance of the retrieved chunks, and a model's answer when it has access to Redpine...

Filip Dorm, Leonora Vesterbacka · 0 citations
#natural language process... Preprint Oct 2026

Pseudowords as probes: Large Language Models show little of the sublexical sensitivity that governs human pseudoword processing

Systematicity, the probabilistic mapping of form to meaning, permeates language at all levels, and sublexical cues have been shown to govern human pseudoword processing. Yet whether LLMs exhibit comparable sensitivity to these cues remains unclear. We tested five LLMs on two Italian two-alternative forced-choice pseudo...

Jing Chen, Giulia Loca, Simona Amenta et al. · 0 citations
#natural language process... Preprint Open access Oct 2026

ARIA: Audio-Driven Melody-Tone Relation Modeling for Cantonese Lyric Authoring

Cantonese lyric writing requires close alignment between lexical tones and melodic pitch. Existing melody-guided lyric generation methods typically rely on symbolic melody to generate lyrics. However, in real songwriting scenarios, melodies are often expressed as raw singing audio or hummed recordings, where pitch is i...

Shengyu Li, Jinting Wang, Li Liu · 0 citations
#artificial intelligence Preprint Oct 2026

Visual Abstention in Unified Multimodal Models

Unified multimodal models (UMMs) integrate understanding and generation, yet their generative behavior is rarely governed by what they understand about the task. We formalize visual abstention: when a requested visual transformation is impossible under the task's rules, the model should recognize that no valid solution...

Chu-Fan Shi, Cheng Yang, Tian-Nuo Yang et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

Dynamic Positional Attention Modulation for Parameter-Efficient Fine-Tuning of Large Language Models

Parameter-efficient fine-tuning (PEFT) has become a standard approach for adapting large language models to downstream tasks. However, most existing PEFT methods rely on uniform and static adaptations, without accounting for the structured heterogeneity of attention across dimensions, heads, layers, and input tokens. I...

Dayan Pan, Jingyuan Wang, Xie Yu · 0 citations
#natural language process... Preprint Open access Oct 2026

OMIT the Action: Measuring Framing-Invariant Omission Bias under Philosophical Disagreement

As LLMs increasingly assist in moral reasoning, omission bias, the tendency to prefer inaction even when equivalent framings reverse substantive outcomes, poses a significant risk of skewed decision-making. Yet omission bias remains underexplored in LLM evaluation, with the few existing studies limited in scale and foc...

Sihyeon Lee, Jihun Song, Chanwoo Kim et al. · 0 citations
#natural language process... Preprint Oct 2026

Nucleus Speculative Decoding: Plausibility-Aware Verification Beyond Exact Distribution

Speculative decoding accelerates autoregressive generation by using a lightweight draft model to propose multiple tokens that are verified by a target model in parallel. However, the standard acceptance rule focuses on exact distribution correction and rejects tokens that remain highly plausible under the target model...

Shu-Hao Li, Fang-Hua Ye, Wan-Yu Lin et al. · 0 citations
#artificial intelligence Preprint Oct 2026

One Step at a Time: Trading LLM Autonomy for Process Predictability

Organizations automating operational processes need more than a correct outcome: they need to predict how a process will run, know which one actually ran, and inspect it step by step. When an agent is the executor that predictability is normally lost: the prescribed procedure goes into the system prompt, and only a fin...

Hans Schabert, Christoph Peters · 0 citations
#natural language process... Preprint Oct 2026

Reading, Not Manipulating: Leveraging Router Logits for Multimodal Safety in MoE Vision-Language Models

Vision-language models (VLMs) face compositional safety risks where harmful intent emerges from the interaction between visual and textual inputs. As mixture-of-experts (MoE) VLMs become increasingly common, recent work has explored various safety interventions, including prompting, supervised fine-tuning, and routing-...

Zi-Yuan Yang, Wen-Xuan Ding, Shang-Bin Feng et al. · 0 citations
#artificial intelligence Preprint Open access Oct 2026

No Transformer Beats Six Covariates: Long-Horizon Prediction of Depressive Symptoms from Childhood Essays

Natural language processing (NLP) models can detect depression-related language in text written near the time symptoms are measured, but whether pretrained transformers can predict depressive symptoms from text written twelve years earlier is largely untested. In the National Child Development Study, a British birth co...

Daniel Kua, Emrul Hasan, John-Jose Nunez et al. · 0 citations
#artificial intelligence Preprint Oct 2026

From Evidence to Action: How Tool-Using Agents Fail

Tool-using agents make consequential changes to external state, yet correct outcomes do not guarantee that their actions were supported by evidence established beforehand. We study where this evidence-to-action chain breaks as agents move from deciding whether to act to executing single actions and dependent workflows....

Hongzhan Lin, Shi-Dong Cao, Ziyang Luo 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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