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 AI

Jul 1

Hierarchical Global Attention (HGA)

arXiv:2606. 30709v1 Announce Type: cross Abstract: Hierarchical Global Attention (HGA) is a drop-in replacement for dense causal attention in pretrained long-context transformers.

By Woernle Frank, Fedosov Vladimir, Grinenko Artemiy

llms efficiency

arXiv AI

Jul 1

CLOUDADV: Decision-Aligned Instance Sizing with Zero-Shot Foundation Models under Drift

arXiv:2606. 31470v1 Announce Type: new Abstract: Cloud virtual machines are often overprovisioned, creating avoidable cost and operational inefficiency.

By Jack Bell, Giacomo Carfi, Gerlando Gramaglia, Andrea Simioni, Daniele Fontani, Vincenzo Lomonaco

llms safety

arXiv AI

Jul 1

ALM2Vec: Learning Audio Embeddings for Universal Audio Retrieval with Large Audio-Language Models

arXiv:2606. 30682v1 Announce Type: cross Abstract: Recent advances in language--audio retrieval have been largely driven by contrastive dual-encoder architectures that align audio and text in a shared embedding space.

By Fengjie Lu, Chenang Jiang, Jiarui Hai, Helin Wang, Aaron Yee

llms rag nlp multimodal benchmarks

arXiv AI

Jul 1

Introspective Coupling: Self-Explanation Training Tracks Behavioral Change Despite Fixed Supervision

arXiv:2606. 32038v1 Announce Type: cross Abstract: When does training language models (LMs) to generate explanations of their predictions yield faithful introspection, rather than superficial imitation?

By Zifan Carl Guo, Laura Ruis, Jacob Andreas, Belinda Z. Li

llms

arXiv AI

Jul 1

GR2 Technical Report

arXiv:2606. 31984v1 Announce Type: cross Abstract: Industrial recommendation systems serve billions of users through a multi-stage funnel -- retrieval, early-stage ranking, and re-ranking -- where the final re-ranking step disproportionately shapes user engagement and downstream performance, particularly for carousel and grid display formats.

By Yufei Li (Yongkang), Zaiwei Zhang (Yongkang), Mingfu Liang (Yongkang), Kavosh Asadi (Yongkang), Jay Xu (Yongkang), Jimmy Kim (Yongkang), Chongyang Bai (Yongkang), Jieyi Zhang (Yongkang), Hongye Xie (Yongkang), Prachi Agrawal (Yongkang), Dian Yu (Yongkang), Tianyi Chen (Yongkang), Jean-Pascal Billaud (Yongkang), Garret Buell (Yongkang), YK (Yongkang), Zhu (Yang), Sachin Patil (Yang), Brooke Bian (Yang), Zhou Fang (Yang), Kevin Huang (Yang), Shiva Sudanagunta (Yang), Yuzhen Huang (Yang), Emma Lu (Yang), Chris O'Brien (Yang), Yang Song (Yang), Lihong Li (Yang), Jacob Tao (Yang), Zhicheng Zhu (Yang), Chao Li (Yang), Gaoxiang Liu (Yang), Neil Wu (Yang), Zhongyin Hu (Yang), Li Han (Yang), Loki Chen (Yang), Ming Lei (Yang), Greg Rehm (Yang), Siyuan Song (Yang), Tianwei Zhang (Yang), Li Li (Yang), Ketan Singh (Yang), Yavuz Yetim (Yang), Ilyas Atishev (Yang), Satendra Gera (Yang), Ashkan Sadeghi (Yang), Rachel Yan (Yang), Nikko Mizutani (Yang), Shuaiwen Wang (Yang), Song Yang (Yang), Zhijing Li (Yang), Jiang Liu (Yang), Mengying Sun (Yang), Fei Tian (Yang), Xiaohan Wei (Yang), Chonglin Sun (Yang), Parish Aggarwal (Yang), Kaushik Rangadurai (Yang), Zhi Hua (Yang), Frank Shyu (Yang), Ruchit Sharma (Yang), Liyuan Li (Yang), Shike Mei (Yang), Wenlin Chen (Yang), Santanu Kolay (Yang), Ben Schulte (Yang), Deepak Chandra (Yang), Adam (Yang), Song, Sandeep Pandey, Xi Liu, Hamed Firooz, Luke Simon

llms nlp reinforcement-learning fine-tuning efficiency safety

arXiv AI

Jul 1

Token-Sparse Medical Multimodal Reasoning via Dual-Stream Reinforcement Learning

arXiv:2606. 31599v1 Announce Type: cross Abstract: Vision-language models (VLMs) combining reinforcement learning (RL) ignite remarkable progress in multimodal reasoning, yet still struggle with medical images, which typically exhibit extremely sparse visual evidence to inform clinical decision-making.

By Kaitao Chen, Weiqian Zhao, Jiamin Wu, Qihao Zheng, Shangquan Sun, Chunfeng Song, Xiaosong Wang, Mu Zhou, Mianxin Liu

llms nlp reinforcement-learning efficiency multimodal benchmarks

arXiv Machine Learning

Jul 1

TabPATE: Differentially Private Tabular In-Context Learning Without Public Data

arXiv:2606. 31474v1 Announce Type: new Abstract: Tabular foundation models enable accurate in-context learning (ICL) from small labeled datasets, but the private records placed in context can leak through model predictions.

By Dariush Wahdany, Matthew Jagielski, Jesse C. Cresswell, Adam Dziedzic, Franziska Boenisch

llms benchmarks safety

arXiv AI

Jul 1

Towards Inclusive Mobility Modeling: Characterizing and Evaluating Elderly Trajectory Patterns in Urban Systems

arXiv:2606. 31207v1 Announce Type: new Abstract: The rapid advance of smart cities increasingly depends on trajectory data mining, yet underrepresented demographic groups, particularly the elderly, are often sparsely represented in public mobility datasets.

By Zhengxuan Wang, Haohan He, Mengying Zhou

llms fine-tuning safety

arXiv AI

Jul 1

Verify when Uncertain: Beyond Self-Consistency in Black Box Hallucination Detection

arXiv:2502. 15845v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) often hallucinate, limiting their reliability in sensitive applications.

By Yihao Xue, Kristjan Greenewald, Youssef Mroueh, Baharan Mirzasoleiman

llms rag benchmarks safety

arXiv Machine Learning

Jul 1

On Optimizing Multimodal Jailbreaks for Spoken Language Models

arXiv:2603. 19127v2 Announce Type: replace Abstract: As Spoken Language Models (SLMs) integrate speech and text modalities, they inherit the safety vulnerabilities of their LLM backbone while introducing an expanded attack surface.

By Aravind Krishnan, Karolina Sta\'nczak, Dietrich Klakow

llms multimodal benchmarks safety

arXiv Machine Learning

Jul 1

HSAP: A Hierarchical Sequence-aware Parallelism for Hybrid-Context Generative Models

arXiv:2606. 30460v2 Announce Type: replace Abstract: In this paper, we aim to combine the advantages of existing sequence parallelism paradigms and overcomes their drawbacks, the most serious of which is the incapability to correctly compute causal attention on the hybrid-context packed sequences, in a stronger sequence parallelism framework.

By Songxin Zhang, Zejian Xie, Zhuoyang Song, Cong lin, Junyu Lu, Jiaxing Zhang, Bingyi Jing

llms fine-tuning

arXiv AI

Jul 1

MECoBench: A Systematic Study of Multimodal Agent Collaboration in Embodied Environments

arXiv:2606. 31966v1 Announce Type: cross Abstract: Recent multimodal large language models (MLLMs) have strong potential as embodied agents, but their ability to collaborate in visually grounded environments remains underexplored.

By Qingyun Liu, Jiwen Zhang, Jingyi Hu, Siyuan Wang, Zhongyu Wei

llms agents robotics multimodal benchmarks

arXiv AI

Jul 1

Are Video Reasoning Models Ready to Go Outside?

arXiv:2603. 10652v3 Announce Type: replace-cross Abstract: In real-world deployment, vision-language models often encounter disturbances such as weather, occlusion, and camera motion.

By Yangfan He, Changgyu Boo, Jaehong Yoon

llms robotics multimodal benchmarks

arXiv AI

Jul 1

TSHA: A Benchmark for Visual Language Models in Trustworthy Safety Hazard Assessment Scenarios

arXiv:2603. 29759v2 Announce Type: replace-cross Abstract: Recent advances in vision-language models (VLMs) have accelerated their application to indoor safety hazards assessment.

By Qiucheng Yu, Ruijie Xu, Mingang Chen Jianfeng Dong, Xin Tan

llms multimodal benchmarks safety

arXiv AI

Jul 1

CoReLIN: Constraint-based Reasoning for Zero-shot Lifelong Interactive Navigation

arXiv:2602. 20055v2 Announce Type: replace-cross Abstract: Robot navigation typically assumes an obstacle-free path exists between start and goal.

By Apoorva Vashisth (Purdue University), Manav Kulshrestha (Purdue University), Pranav Bakshi (IIT Kharagpur), Damon Conover (DEVCOM Army Research Lab), Guillaume Sartoretti (National University of Singapore), Aniket Bera (Purdue University)

llms robotics

arXiv AI

Jul 1

QVal: Cheaply Evaluating Dense Supervision Signals for Long-Horizon LLM Agents

arXiv:2606. 32034v1 Announce Type: cross Abstract: LLM agents increasingly act over long horizons, where a single trajectory can contain hundreds or thousands of actions.

By Sergio Hern\'andez-Guti\'errez, Matteo Merler, Ilze Amanda Auzina, Joschka Str\"uber, Ameya Prabhu, Matthias Bethge

llms rag agents efficiency benchmarks

arXiv AI

Jul 1

ADAPT: Attention Dynamics Alignment with Preference Tuning for Faithful MLLMs

arXiv:2606. 31054v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) are critically hampered by hallucination, generating content inconsistent with the provided image.

By Zhiyuan Yao, Zheren Fu, Zhixiao Zheng, Jiajun Li, Yi Tu, Zhendong Mao

llms diffusion reinforcement-learning multimodal benchmarks safety

arXiv AI

Jul 1

Disentangling Reasoning Logic to Resolve Explicit Knowledge Conflicts

arXiv:2508. 01273v3 Announce Type: replace Abstract: Explicit knowledge conflicts, occurring when retrieved contexts contain contradictory information, pose a fundamental challenge for Large Language Models (LLMs) as they integrate increasingly diverse data sources.

By Xianda Zheng, Zijian Huang, Meng-Fen Chiang, Jiamou Liu, Yuan Fang, Michael Witbrock, Kaiqi Zhao

llms reinforcement-learning

arXiv AI

Jul 1

An Efficient Heterogeneous Co-Design for Fine-Tuning on a Single GPU

arXiv:2603. 16428v2 Announce Type: replace-cross Abstract: Fine-tuning Large Language Models (LLMs) has become essential for domain adaptation, but its memory-intensive property exceeds the capabilities of most GPUs.

By Ruijia Yang, Zeyi Wen

llms fine-tuning efficiency

arXiv AI

Jul 1

RigorBench: Benchmarking Engineering Process Discipline in Autonomous AI Coding Agents

arXiv:2606. 22678v2 Announce Type: replace-cross Abstract: Agentic coding harnesses - such as Agent-Skills, Superpowers, and Agent-Rigor - are increasingly deployed to augment underlying LLMs for real-world software engineering tasks.

By Meher Bhaskar Madiraju, Meher Sai Preetam Madiraju

llms agents benchmarks

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