Large language models

Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.

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arXiv Machine Learning

Sep 15

One Size Does Not Fit All: Setting Inference Depth from the Questions a Deployment Actually Asks

arXiv:2609.14144v1 Announce Type: cross Abstract: A transformer language model is trained to respond to any prompt, but each deployment asks only a narrow range of questions: a support assistant sees...

By Jerry Kaplan

llms

arXiv AI

Sep 15

MoME: Mixture-of-Memory Embeddings for Context-Aware Sparse Lookup

arXiv:2609.15126v1 Announce Type: cross Abstract: Scaling large language models efficiently has motivated sparse capacity mechanisms such as Mixture-of-Experts and, more recently, conditional memory:...

By Muchen Li, Leonid Sigal, Renjie Liao

llms rag safety

arXiv Machine Learning

Sep 15

What Input Resolution Is Required for Bird Species Identification, and What Is Its Latency Cost on an Edge Device? A Study of 14 Input Resolutions and Six Architectures with On-Device Measurements

arXiv:2609.14247v1 Announce Type: cross Abstract: Bird-strike mitigation at wind farms requires identifying distant birds that span only tens of pixels, so the classifier's input resolution N is a de...

By Takeshi Nishikawa

llms

arXiv Machine Learning

Sep 15

CCMAN: Cognitive Instability-Aware Cross-Modal Attention Network for Interpretable Temporal Biomarkers of Verbal Fluency Speech

arXiv:2609.14764v1 Announce Type: cross Abstract: Early detection of cognitive decline from speech offers a scalable and non-invasive alternative to conventional clinical assessment. Verbal fluency t...

By Madhurananda Pahar, Caitlin Illingworth, Dorota Braun, Daniel Blackburn, Heidi Christensen

llms fine-tuning multimodal benchmarks

arXiv Machine Learning

Sep 15

Geometric Signatures of Conceptual Reorganization: A Counterfactual Embedding Framework for Detecting Scientific Revolutions

arXiv:2609.14917v1 Announce Type: cross Abstract: We introduce document embedding geometry as a quantitative observable of conceptual reorganization and develop a counterfactual ablation framework fo...

By Dimitris Ntounis, Ariel Schwartzman, Chris Chafe, Thomas A. Ryckman

llms rag benchmarks

arXiv Machine Learning

Sep 15

ABSOL: Aggregated Bayesian Subsampling Orchestrated with LLMs

arXiv:2609.15007v1 Announce Type: cross Abstract: Large language models are increasingly used as natural-language interfaces to structured data, yet they remain unreliable when answers require consis...

By Jackson Hassell, Chen Shen, Estevam Hruschka

llms benchmarks

arXiv Machine Learning

Sep 15

Temperature Fragility and the Conditional Benefits of Truncation Sampling

arXiv:2609.15476v1 Announce Type: cross Abstract: Large language models generate text by sampling each token from a predicted distribution, and a temperature parameter sets how far the draw strays fr...

By Francesco La Rosa

llms benchmarks

arXiv Machine Learning

Sep 15

Disentangling Representation Evolution in Transformers through Directional Decomposition

arXiv:2609.15975v1 Announce Type: cross Abstract: Transformer representations evolve through learned additive transformations that either preserve their current direction or redirect it. We study thi...

By Shwai He, Haichao Zhang, Shen Yan

llms robotics

arXiv Machine Learning

Sep 15

N$^2$: A Unified Python Package and Test Bench for Nearest Neighbor-Based Matrix Completion

arXiv:2506.04166v3 Announce Type: replace Abstract: Nearest neighbor (NN) methods have re-emerged as competitive tools for matrix completion, offering strong empirical performance and recent theoreti...

By Caleb Chin, Aashish Khubchandani, Harshvardhan Maskara, Kyuseong Choi, Jacob Feitelberg, Albert Gong, Manit Paul, Tathagata Sadhukhan, Dwaipayan Saha, Anish Agarwal, Raaz Dwivedi

llms benchmarks

arXiv Machine Learning

Sep 15

Data Efficient Any Transformer-to-Mamba Distillation via Attention Bridge

arXiv:2510.19266v3 Announce Type: replace Abstract: State-space models (SSMs) have emerged as promising alternatives to Transformers for sequence modeling. However, training competitive SSMs from scr...

By Penghao Wang, Yuhao Zhou, Mengxuan Wu, Panpan Zhang, Zhangyang Wang, Kai Wang

llms computer-vision efficiency safety

arXiv Machine Learning

Sep 15

Size Transferability of Graph Transformers with Convolutional Positional Encodings

arXiv:2602.15239v3 Announce Type: replace Abstract: Transformers have achieved remarkable success across domains, motivating the rise of Graph Transformers (GTs) as attention-based architectures for...

By Javier Porras-Valenzuela, Zhiyang Wang, Teresa Shang, Yusu Wang, Alejandro Ribeiro

llms benchmarks

arXiv Machine Learning

Sep 15

Non-Asymptotic Global Convergence of PPO-Clip

arXiv:2512.16565v2 Announce Type: replace-cross Abstract: Reinforcement learning has gained attention for modern Large Language Model post-training. The actor-only variants of Proximal Policy Optimiz...

By Qiming Dai, Yin Liu, Junyu Zhang, Zaiwen Wen

llms reinforcement-learning

arXiv Machine Learning

Sep 15

Multimodal Dataset Normalization and Perceptual Validation for Music-Taste Correspondences

arXiv:2604.10632v2 Announce Type: replace-cross Abstract: Music and food traditions are both intangible cultural heritage, and the links between them, how a sound can make a taste seem sweeter or mor...

By Matteo Spanio, Valentina Frezzato, Antonio Rod\`a

llms fine-tuning multimodal