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natural language processing

9,568 papers

#machine learning Preprint Oct 2026

Quantize by Drift: Label-Free Mixed-Precision Post-Training Quantization for Text Embedders

Output drift is a robust coarse sensitivity signal, not a universally optimal allocation objective: it avoids the catastrophic failures of the transferred signed-geometry adaptation and can remain usable at stressed budgets where uniform collapses, but fine-grained redistribution around a strong uniform operating point...

HyoJung Han, Jongmin Kim, S. Jeon · 0 citations
#machine learning Preprint Oct 2026

Same Text, Different Prediction: Serving-Context Nondeterminism in Text Classifiers

Deterministic inference is essential for reliable and trustworthy machine learning. Prior studies of text generation have shown that changing factors such as batch size, batch composition, hardware, or inference engine can alter the generated text, even when the prompt, model parameters, and sampling randomness are fix...

Santhosh Kumar Kasa, Siva Rajesh Kasa, Sumit Negi · 0 citations
#machine learning Preprint Oct 2026

CARE: Certifying Acceleration for Vision-Language-Action Inference

While vision-language-action (VLA) models have advanced rapidly, running them at every control step remains expensive. Prior work accelerates VLA inference using techniques like action chunking and visual-token pruning, typically evaluating based on latency and average task success. However, acceleration may discard in...

Rui Liu, Tong Zheng, Jin-Dong Gu et al. · 0 citations
#machine learning Open access Sep 2026

CoDR: Training-Free Confidence-Drift Remasking for Diffusion Language Models

CoDR (Confidence Drift Remasking), a training-free and sampler-agnostic refinement pass that estimates drift for all committed positions in only k forward passes via k-partition probing, and regenerates only the tokens the model no longer endorses.

Yue Wu, Qing-He Zheng, Yu Zhang et al. · 0 citations
#machine learning Preprint Open access Oct 2026

Which Rollout Taught It That? BehaviorTrace and the Limits of Training-Data Attribution in Online RL

When reinforcement learning teaches a language model a new behavior, can we find the training rollouts that taught it? And when an attribution method says it can, how do we know the answer is real? We study both questions on online RL fine-tuning with GRPO, using a planted behavior with a known cause. We release Behavi...

Amit Nautiyal · 0 citations
#machine learning Preprint Oct 2026

Input-Blind Controls Produce Substantial Oracle Headroom for Layer Programs in Multiple-Choice Evaluation

Adaptive computation aims to improve language-model inference by tailoring execution to each input. For layer programs, oracle evaluations use known answers to estimate the potential gain from this flexibility, before a practical selector is available. However, a gain from selection does not by itself explain why the c...

Yi-Bei Guo, Rui Liu · 0 citations
#machine learning Preprint Oct 2026

PatchBench: Measuring Collateral Damage in Activation Patching

PatchBench, a benchmark of empirically observed model-specific jailbreak failures inducing actionable harmful answers, and PatchBench-Local, an evaluation protocol testing whether a patch is behaviourally precise, provide a more precise basis for developing and comparing jailbreak repair methods.

Alexi Canesse, Mathis Le Bail, Maël Jenny et al. · 0 citations
#machine learning Preprint Open access Oct 2026

YANchor-4B: Effective Long-Horizon Reasoning in O(N) Time with O(1) Memory

Long-horizon reasoning demands access to earlier information at a manageable generation cost. Full-history attention incurs growing storage and computation, while recurrent compression can lose precise details. Therefore, we present YANchor-4B, a general-purpose recurrent model that preserves crucial memory as ANchors...

Huishan Ji, Hua Xu, Weiming Zhang et al. · 0 citations
#machine learning Preprint Oct 2026

When Rank Rises as LLMs Degrade

It is shown that direction is a property of the regime-statistic pair and cannot be fixed by recalibration alone, and two often-conflated statistics: RankMe normalises singular values, whereas covariance effective rank normalises eigenvalues.

Zhaohui Geoffrey Wang · 0 citations
#machine learning Preprint Open access Oct 2026

CHASE: Channel-Aligned Structure Exploitation for Geometry-Aware Model Engineering

Geometric and Spectral Alignment (GSA) characterizes trained networks through spectral concentration, physical-channel alignment, support structure, and changes in singular bases. In this paper, we propose CHASE (Channel-Aligned Structure Exploitation) to use these structures in practical model design. CHASE covers six...

Wei Wang, Wei Jiang, Ziran Liu · 0 citations
#machine learning Preprint Open access Oct 2026

Noise Your Prompt: Noising Conditioning Tokens in Continuous Diffusion Language Models

We revisit a standard accepted practice in the continuous diffusion language model literature of fixing conditioning prompt tokens clean during training. We make a very simple modification: also noise the conditioning prompt tokens during training. We demonstrate that under this modified training objective, we ac...

Justin Jung · 0 citations
#machine learning Review Oct 2026

Multi-Label Topic Assignment via LLM Distillation: A Comparative Analysis of Generative vs. Discriminative Student Models

Multi-label topic assignment for user-generated content (UGC) -- including product reviews and buyer-seller conversations -- poses unique scalability challenges in large-scale e-commerce due to informal language, extreme label sparsity, and rapidly evolving taxonomies. While utilizing Large Language Models (LLMs) as la...

Sourabh Kasliwal, Shubhranshu Singh · 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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