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

Jun 16

Mean-Field Parallel Decoding for Discrete Diffusion Language Models

arXiv:2606. 15805v1 Announce Type: new Abstract: Discrete diffusion language models enable parallel token generation, offering a pathway to low-latency decoding.

By Tamim Zoabi, Ameen Ali, Liran Ringel, Lior Wolf

llms diffusion benchmarks

arXiv Machine Learning

Jun 16

ExpRL: Exploratory RL for LLM Mid-Training

arXiv:2606. 17024v1 Announce Type: new Abstract: Sparse reward reinforcement learning (RL) has become a standard tool for improving LLM reasoning, but its success depends critically on the coverage present in the base model.

By Violet Xiang, Amrith Setlur, Chase Blagden, Nick Haber, Aviral Kumar

llms reinforcement-learning fine-tuning efficiency

arXiv Machine Learning

Jun 16

GD$^2$PO: Mitigating Multi-Reward Conflicts via Group-Dynamic reward-Decoupled Policy Optimization

arXiv:2606. 16771v1 Announce Type: new Abstract: As LLMs advance, post-training reinforcement learning (RL) increasingly relies on multi-dimensional rewards to cultivate comprehensive capabilities.

By Haotian Liu, Yihao Liu, Jingwei Ni, Siyuan Huang, Xinpeng Liu, Pengyu Cheng, Jiajun Song, Ruijin Ding, Junfeng Li, Zhechao Yu, Mengyu Zhou, Hongteng Xu, Xiaoxi Jiang, Guanjun Jiang

llms agents reinforcement-learning safety

arXiv AI

Jun 16

Follow the Latent Roadmap: Navigating Revocable Decoding for Diffusion LLMs with Anchor Tokens

arXiv:2606. 16847v1 Announce Type: cross Abstract: Diffusion Large Language Models (dLLMs) offer a promising avenue for parallel generation but face a trade-off between decoding speed and quality.

By Yizhen Yao, Qinglin Zhu, Runcong Zhao, Xiangxiang Dai, Yanzheng Xiang, Yulan He, Lin Gui

llms rag diffusion efficiency benchmarks

arXiv AI

Jun 16

daVinci-kernel: Co-Evolving Skill Selection, Summarization, and Utilization via RL for GPU Kernel Optimization

arXiv:2606. 16497v1 Announce Type: cross Abstract: GPU kernel optimization represents a paradigm where functional correctness is assumed and execution efficiency is the objective.

By Dayuan Fu, Mohan Jiang, Tongyu Wang, Dian Yang, Jiarui Hu, Liming Liu, Jinlong Hou, Pengfei Li

llms rag agents nlp reinforcement-learning fine-tuning

arXiv Machine Learning

Jun 16

LoLA: Low-Rank Linear Attention With Sparse Caching

arXiv:2505. 23666v3 Announce Type: replace-cross Abstract: The per-token cost of transformer inference scales with context length, preventing its application to lifelong in-context learning.

By Luke McDermott, Robert W. Heath Jr., Rahul Parhi

llms efficiency

arXiv Machine Learning

Jun 16

Your "Pro" LLM Subscription May Actually Be "Free": Exposing Fingerprint Spoofing Risks in LLM Inference Services

arXiv:2606. 16100v1 Announce Type: cross Abstract: As Large Language Model (LLM) APIs become ubiquitous, users increasingly rely on black-box fingerprinting to verify that providers are serving the advertised premium models.

By Jiahao Zhang, Xiuyu Li, Suhang Wang

llms fine-tuning efficiency safety

arXiv AI

Jun 16

AgenticRec: A Recommendation-Oriented Agentic Framework with Progressive Tool-Integrated Reasoning Optimization

arXiv:2603. 21613v2 Announce Type: replace-cross Abstract: Recommender agents built on Large Language Models offer a promising paradigm for personalized recommendation.

By Tianyi Li, Zixuan Wang, Guidong Lei, Xiaodong Li, Hui Li

llms agents

arXiv AI

Jun 16

AC-ODM: Actor--Critic Online Data Mixing for Sample-Efficient LLM Pretraining

arXiv:2505. 23878v2 Announce Type: replace-cross Abstract: Optimizing pretraining data composition is pivotal for LLM generalization.

By Jing Ma, Chenhao Dang, Mingjie Liao

llms reinforcement-learning benchmarks

arXiv AI

Jun 16

The Proxy Knows Too Much: Sealing LLM API Routers with Attested TEEs

arXiv:2606. 16358v1 Announce Type: cross Abstract: Agents increasingly access large language models (LLMs) through API routers.

By Sipeng Xie, Qianhong Wu, Hengrun Lu, Ziliang Sun, Qi Wu, Bo Qin, Qin Wang

llms agents

arXiv AI

Jun 16

Communication-Efficient Verifiable Attention for LLM Inference

arXiv:2606. 16352v1 Announce Type: cross Abstract: Computation integrity of remote large language model (LLM) serving can be questionable.

By Ziqun Chen, Ming Wu, Michael Heinrich, Jason Zeng, Huiying Lan, Tianwei Zhang, Rui Tan

llms

arXiv Machine Learning

Jun 16

How Post-Training Shapes Biological Reasoning Models

arXiv:2606. 16517v1 Announce Type: new Abstract: Scientific reasoning models for biology combine language models with foundation models trained on multimodal biological data, including DNA, RNA, and proteins.

By Lukas Fesser, Hanlin Zhang, Michelle M. Li, Eric Wang, Bryan Perozzi, Shekoofeh Azizi, Sham M. Kakade, Marinka Zitnik

llms reinforcement-learning fine-tuning multimodal

arXiv Machine Learning

Jun 16

CT-VDETR: Semi-supervised 3D Trauma Detection in Computed Tomography (CT) scans using Dense Vertex Relative Position Encoding

arXiv:2603. 12514v2 Announce Type: replace-cross Abstract: Accurate detection and localization of traumatic injuries in abdominal CT remain challenging because voxel-level annotations are limited and expensive to obtain.

By Shivam Chaudhary, Sheethal Bhat, Andreas Maier

llms benchmarks

arXiv Machine Learning

Jun 16

Privacy from Symmetry: Orthogonally Equivariant Transformers for LLM Inference

arXiv:2606. 16461v1 Announce Type: new Abstract: Running large language models locally is often impractical, pushing inference on sensitive text to third-party providers.

By Alexander Yukhimchuk, Andrey Shulga, Mladen Kolar, Martin Tak\'a\v{c}

llms rag fine-tuning safety

arXiv AI

Jun 16

Mind-Studio: Executable World Models with Lookahead Evaluation for Partially Observable Games

arXiv:2606. 16070v1 Announce Type: new Abstract: World-model synthesis aims to turn interaction experience into an internal model of environment dynamics.

By Yifei Dong (Hong Kong University of Science and Technology), Mingen Zheng (Hong Kong University of Science and Technology), Linquan Wu (City University of Hong Kong), Jeff Z. Pan (University of Edinburgh), Jiaxin Bai (Hong Kong Baptist University)

llms

arXiv AI

Jun 16

SMEPilot: Characterizing and Optimizing LLM Inference with Scalable Matrix Extensions

arXiv:2606. 16332v1 Announce Type: cross Abstract: Modern CPUs increasingly integrate matrix extensions, such as Arm Scalable Matrix Extension (SME), that provide high-throughput matrix execution within the CPU.

By Feiyang Chen, Haibo Chen

llms efficiency

arXiv AI

Jun 16

XAI-Grounded Explanation Generation for Speech Deepfake Detection with Training-Free Multimodal Large Language Models

arXiv:2606. 16137v1 Announce Type: cross Abstract: Speech deepfake detection (SDD) systems require trustworthy explanations for reliable decision-making.

By Yupei Li, Qiyang Sun, Xiaoliang Wu, Chenxi Wang, Berrak Sisman, Bj\"orn W. Schuller

llms multimodal

arXiv AI

Jun 16

Service-Induced Congestion in Memory-Constrained LLM Serving

arXiv:2606. 15555v1 Announce Type: cross Abstract: In large language model (LLM) serving, each request accumulates persistent graphics processing unit (GPU) memory during service as its key-value cache grows with every generated token.

By Ruicheng Ao, Jing Dong, Gan Luo, David Simchi-Levi

llms efficiency

arXiv AI

Jun 16

TimeVista: Exploring and Exploiting Vision-Language Models as Judges for Time Series Forecasting

arXiv:2606. 16173v1 Announce Type: new Abstract: High-quality time series forecasting is pivotal for real-world decision-making.

By Zhi Chen, Yuxuan Wang, Jialong Wu, Yong Liu, Haoran Zhang, Xingjian Su, Jianmin Wang, Mingsheng Long

llms multimodal benchmarks

arXiv Machine Learning

Jun 16

Branching Flows: Discrete, Continuous, and Manifold Flow Matching with Splits and Deletions

arXiv:2511. 09465v4 Announce Type: replace-cross Abstract: Diffusion and flow matching approaches to generative modeling have shown promise in domains where the state space is continuous, such as image generation or protein folding & design, and discrete, exemplified by diffusion large language models.

By Lukas Billera, Hedwig Nora Nordlinder, Jack Collier Ryder, Anton Oresten, Aron St{\aa}lmarck, Theodor Mosetti Bj\"ork, Ben Murrell

llms diffusion multimodal

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