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natural language processing

9,568 papers

#machine learning Preprint Oct 2026

Calibration Language Shapes Block-Influence Pruning in Multilingual Language Models

Block Influence (BI) ranks Transformer blocks for depth pruning from their input-output cosine change, but the ranking depends on the calibration distribution. We study whether English calibration transfers to Indian languages in Qwen2.5-1.5B-Instruct, Llama-3.2-1B, and Gemma-2-2B. English, Hindi, Marathi, and Tamil us...

Vedant Pandhare, Piyush Dhoka · 0 citations
#machine learning Preprint Open access Oct 2026

What Should We Remove? Evaluating the Reliability and Costs of RLVR Data Decontamination

Reinforcement learning with verifiable rewards (RLVR) math corpora are routinely screened for benchmark overlap, but the reliability and data-loss costs of cleaning policies remain underexamined. We audit sixteen open RLVR math corpora comprising 1,673,514 corpus-specific training items against six benchmarks containin...

Esther Xin · 0 citations
#machine learning Preprint Oct 2026

SHIP: Structured and Hierarchical Insight with Privacy

Extracting insights from textual data while protecting user privacy is increasingly important. Existing methods produce private insights in diverse forms for different analytical purposes, making their utility difficult to assess and compare. In this work, we formulate differentially private (DP) insights as reusable r...

Kai Chen, Gui-Si Ni, Albert Cheu et al. · 0 citations
#machine learning Preprint Oct 2026

Can LLMs Anticipate Their Cruxes?

To use language models to help us make decisions we would like to understand the rationale behind the language model's outputs. In principle, the language model provides its rationale as part of its text output. However, the sheer volume of text makes this hard to use in practice. In this paper, we are interested in ex...

David Reber, James Reber, Ari Holtzman et al. · 0 citations
#machine learning Preprint Open access Oct 2026

One Bias Score, Two Factors: How Mitigations Affect Option Bias in Multiple-Choice Evaluation

Reordering multiple-choice options can change a model's answer, an effect studied as selection bias (a preference for certain option letters) and as order sensitivity (changes in answers across option orderings). We connect these perspectives through the pooled selection-rate standard deviation (pooled SD), which measu...

Hyunjong Ok, Minjae Park, Jaeho Lee · 0 citations
#machine learning Preprint Open access Oct 2026

Does Morphological Correctness Matter for Tokenization? A Scrambled Control Study in Tagalog

Typology-aware tokenizers are motivated by the claim that segmentation which respects morpheme boundaries preserves information that frequency-driven segmentation destroys. Studies routinely establish that it helps while assuming the boundaries must be linguistically correct. We separate them. Tagalog marks voice with...

Abdul Rehman Antall, Baidya Nath Saha · 0 citations
#machine learning Preprint Open access Oct 2026

CascadeLedger: Counterfactual Auditing of Information and Resource Integrity in LLM Cascades

We introduce CascadeLedger, a counterfactual execution-audit framework for budgeted language-model cascades. The protocol enforces call-before-read semantics, binds successful reads to service and cache identities, records availability and component-wise budget transitions, and freezes the event trace before any target...

Miaobo Hu, Shuhao Hu, Xiaobo Guo et al. · 0 citations
#machine learning Preprint Open access Oct 2026

AbridgeKV: Compressing Long Reasoning into the KV Cache of Learned Pause Tokens

Long chain-of-thought decoding is memory-bound: the KV cache of the generated tokens grows linearly with the test-time compute being spent. Evicting cache entries does not fix this, because a model never trained on a cache with entries removed generates longer, often up to the length limit. We instead train the model t...

Jack Cai, Lijie Yang, Vasilis Kontonis et al. · 0 citations
#machine learning Preprint Open access Oct 2026

A Large-Scale Dataset for Multi-Class Financial News Classification in Brazilian Portuguese

The development of Financial Natural Language Processing (NLP) systems for Brazilian Portuguese is hindered by the scarcity of domain-specific datasets. To address this, we present a large-scale corpus of approximately 160,000 Brazilian Portuguese financial news articles collected from web sources. To establish initial...

Leomax Bandeira, Gabriel Aguiar, Dalton C\'ezane Gomes Valadares et al. · 0 citations
#machine learning Preprint Open access Oct 2026

DeepUnfolding_Agent: A Classical_Quantum Adaptive Control API

Adaptive optimization workflows in machine learning, federated learning, quantum machine learning, and hybrid classical quantum systems often embed controller logic directly inside task specific training scripts. This coupling limits reuse, reproducibility, safety validation, and fair comparison across execution enviro...

Shanika Iroshi Nanayakkara, Shiva Raj Pokhrel · 0 citations
#machine learning Preprint Oct 2026

CAT-alyzing Honesty: Continuous Adversarial Training for Transferable LLM Honesty

Large language models (LLMs) are becoming prevalent in scenarios where honesty is critical. However, despite alignment training, these models still exhibit dishonest behaviour, such as outputting claims that contradict some knowledge or are not supported by what is accessible in context. We introduce Continuous Adversa...

Yuan-Chen Chang, Niklas Herbster, G. Gidel et al. · 1 citation
#machine learning Preprint Open access Oct 2026

Effectiveness of Hidden-State Probes for Localizing Reasoning Failures

Verifying the final answer of a long reasoning trace may show that the trace is wrong but it does not distinguish three cases. The problem may lie beyond the model's capability, another attempt would succeed, or one step in an otherwise recoverable trace made the trace fail. Standard hidden-state probes are trained on...

Shubham Subhnil, Yannis Belkhiter, Erik Miehling 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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