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

Affinity-Aware Sharding for Delayed Tensor Parallelism

arXiv:2609.13846v1 Announce Type: new Abstract: Delayed Tensor Parallelism (DTP) removes the blocking all-reduce of tensor-parallel Transformer inference. Every device adds its own partial output to...

By Eloi de Reynal

llms efficiency

arXiv Machine Learning

Sep 15

Minibatch persistency, eight years later: what batch reuse costs in steps and joules, and what it saves in data

arXiv:2609.13922v1 Announce Type: new Abstract: Minibatch persistency reuses data instead of reading it: rather than drawing a fresh minibatch at every optimizer step, it takes K consecutive steps on...

By Matteo Fischetti

llms

arXiv Machine Learning

Sep 15

Multi-Modal Tumor Survival Prediction via Graph-Guided Mixture of Experts

arXiv:2609.14072v1 Announce Type: new Abstract: Large Language Models (LLMs) have displayed impressive capabilities in handling tasks that require few demonstration examples, making them effective fe...

By H Mathavan, H Liu

llms multimodal

arXiv Machine Learning

Sep 15

AgentKV: Phase-Aware KV Eviction for Agentic LLMs

arXiv:2609.14872v1 Announce Type: new Abstract: Agentic serving can consume orders of magnitude more tokens than chatbot workloads, stressing both KV-cache capacity and decode-time bandwidth. Most KV...

By Taowen Tony Liu, Jeffrey T. H. Wong, Can Xiao, Bowen Yang, Hao Mark Chen, Yiren Zhao

llms agents efficiency

arXiv Machine Learning

Sep 15

What Does an LLM Learn from Reinforcement Learning? A Mechanistic Interpretability Perspective with Fixed-SAE Track

arXiv:2609.15064v1 Announce Type: new Abstract: Reinforcement learning (RL) is widely utilized in large language model training to improve targeted capabilities, yet how RL reshapes a model remains p...

By Lingheng Du, Yiming Tang, Xufeng Duan, Dianbo Liu

llms reinforcement-learning benchmarks safety

arXiv Machine Learning

Sep 15

Multi-source Transfer Learning of Time Series with a Shapelet-based Distance Measure

arXiv:2609.15148v1 Announce Type: new Abstract: Transfer learning is an effective technique for addressing data scarcity in deep learning for time series classification, but its success depends on th...

By Jiseok Lee, Brian Kenji Iwana

llms fine-tuning

arXiv Machine Learning

Sep 15

Temporal Self-Distillation: Faster Inference in Discrete Diffusion Language Models

arXiv:2609.15177v1 Announce Type: new Abstract: Diffusion language models (dLLMs) promise fast inference by generating multiple tokens in parallel, but suffer severe performance degradation when para...

By Shijian Xu, Andrea Miele, Metod Jazbec, Volker Roth, Eric Nalisnick, Ilija Bogunovic

llms diffusion efficiency benchmarks

arXiv Machine Learning

Sep 15

Rethinking Correctness for Uncertainty Estimation in Clinical Prediction with Vision-Language Models

arXiv:2609.15180v1 Announce Type: new Abstract: Vision-language models are increasingly explored for clinical prediction from electronic health records and medical images, where identifying unreliabl...

By Mingcheng Zhu, Jinning Liang, Tingting Zhu

llms multimodal

arXiv Machine Learning

Sep 15

Data-driven Prediction of Satellite-observed Avalanche Activity from Snowpack Simulations

arXiv:2609.15485v1 Announce Type: new Abstract: Avalanche forecasting requires knowledge of snowpack conditions and recent avalanche activity, but field observations are sparse across large mountain...

By Jakob Grah, Filippo Maria Bianchi, Bert Kruyt, Karsten M\"uller

llms

arXiv Machine Learning

Sep 15

The Token Before the Value Is the Key: How Hybrid Architectures Organize Induction Circuits

arXiv:2609.15545v1 Announce Type: new Abstract: Hybrid language models can improve capability as well as efficiency, raising the question of how architectural complementarity becomes learned computat...

By Ke Cheng, Xin Xu, Yixiao Chen, Lei Xin, Jianbo Zhao, Fanhu Zeng, Yue Liu, Jun Zhang, Jie Jiang

llms

arXiv Machine Learning

Sep 15

Principal-timestep Restricted Init via Sparse Matrix-decomposition in Flow-matching

arXiv:2609.15643v1 Announce Type: new Abstract: Flow-matching diffusion models have recently emerged as a strong paradigm for high-fidelity visual generation. However, their prohibitively high fine-t...

By Jiayang Gu, Zheng Fang, Lichaun Xiang, Fanghui Liu, Xu Cai, Hongkai Wen

llms diffusion fine-tuning benchmarks safety

arXiv Machine Learning

Sep 15

Knowledge-Enriched Structured EHR Features for 30-Day Hospital Readmission Prediction on MIMIC-IV

arXiv:2609.15713v1 Announce Type: new Abstract: Recent approaches to 30-day hospital readmission prediction rely on pre-trained language models applied to discharge summaries. Although these methods...

By Mohamad Najafi, Hongyun Fu, Mathias Brochhausen, Jian Wu, Yaohang Li

llms rag safety

arXiv Machine Learning

Sep 15

Bellman Policy Optimization

arXiv:2609.15987v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) improves the reasoning capabilities of large language models (LLMs). We introduce Bellman Policy...

By Zhuoqing Song, Haotian Xu, Xikun Zhang, Lidong Bing

llms reinforcement-learning benchmarks

arXiv Machine Learning

Sep 15

Self-Indexing Attention for Compression-Compatible Sparse Long-Context LLM Inference

arXiv:2609.13205v1 Announce Type: cross Abstract: Sparse long-context inference requires efficient token retrieval in both prefill and decode. Existing methods often use different retrieval strategie...

By Xu Yang, Jiapeng Zhang, Zhangke, Changjian Chen, Yuxin Chen, Feiqiang Sun, Chengguang Xu, Feng Jin, Zhuo Tang

llms

arXiv Machine Learning

Sep 15

Part Grounding, Not Action Knowledge: Locating the Bottleneck in VLM Affordance Prediction

arXiv:2609.13225v1 Announce Type: cross Abstract: Benchmarks agree that vision-language models reason poorly about low-level manipulation, but an aggregate accuracy score does not say which step fail...

By Sarthak Sattigeri

llms robotics multimodal benchmarks

arXiv Machine Learning

Sep 15

From Masking to Merging: Rethinking SpecAugment for Efficient Audio Spectrogram Transformer

arXiv:2609.13260v1 Announce Type: cross Abstract: This paper proposes SpecAugment-Patch Merging, a simple yet effective method to accelerate Audio Spectrogram Transformer (AST) training. We first app...

By Minhee Park, Hyowon Ahn, Chanwoo Kim

llms rag efficiency

arXiv AI

Sep 15

Pick Your Poison: Learning to Select Poison Sets for Stronger LLM Backdoor Attacks

arXiv:2609.15029v1 Announce Type: cross Abstract: Backdoor poisoning attacks add poisoned examples to otherwise-clean finetuning data, pairing a trigger with a target behavior that the model learns t...

By Aashiq Muhamed, Mona T. Diab, Virginia Smith, Andrew Ilyas, Matthew Jagielski

llms agents fine-tuning

arXiv Machine Learning

Sep 15

ViFA-Council: Multi-Agent LLM Deliberation for Vietnamese Folk Art Generation

arXiv:2609.13348v1 Announce Type: cross Abstract: This paper presents ViFA-Council, a three-stage multi-agent framework that employs multiple large language models (LLMs) to tackle two culturally com...

By Hai-Dang Nguyen, Minh-Phuong Pham, Thao Thi Phuong Dao, Trong-Le Do, Vinh-Tiep Nguyen, Trung-Nghia Le

llms agents diffusion safety

arXiv Machine Learning

Sep 15

BOOST: Concurrent Access to Host Memory and HBM to Accelerate LLM Inference

arXiv:2609.13592v1 Announce Type: cross Abstract: GPU memory bandwidth and capacity limit throughput in large language model (LLM) inference. The GPU memory system consists of a primary tier of high-...

By Anish Saxena, Jae Hyung Ju, Hritvik Taneja, Po-An Tsai, Aamer Jaleel, Christos Kozyrakis, Moinuddin Qureshi

llms efficiency

arXiv Machine Learning

Sep 15

3D Gait-Based Autism Classification Using Attention-Enhanced Deep Learning with Cross-Fold Statistical Stability Analysis

arXiv:2609.14159v1 Announce Type: cross Abstract: Autism Spectrum Disorder (ASD) is a neurodevelopmental condition whose early diagnosis remains challenging because conventional clinical assessments...

By Md Nadim Mahamood, Md Arif Shahriar, Md Parvej Sikder, Md Rasul Islam, Md Shafi Ud Doula, Md Ashraful Alam, Kamrul Hasan

llms benchmarks

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