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

Jun 16

Unifying Acoustic Features and Text with Multimodal LLMs for Neurodegenerative Screening

arXiv:2606. 14788v1 Announce Type: cross Abstract: Voice-based screening offers a scalable and non-invasive way to assess neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD), but their staging remains challenging due to the difficulty of integrating heterogeneous data.

By Qingfeng Zhang, Yuanxiong Guo, Yanmin Gong

llms rag multimodal

arXiv AI

Jun 16

XFlow: An Executable Protocol Programming System for Reliable Multi-Agent Workflows

arXiv:2606. 14790v1 Announce Type: cross Abstract: LLM-based multi-agent systems increasingly coordinate planning, reasoning, tool use, and human interaction, yet their reliability remains limited.

By Hanqi Li, Jing Peng, Zijian Wang, Lu Chen, Kai Yu

llms agents

arXiv AI

Jun 16

Bayesian Inference and Decision Audits for Public Archives of Frontier AI Evaluations

arXiv:2606. 17005v1 Announce Type: new Abstract: Public AI evaluations are often read as terminal leaderboards, yet the underlying evidence is a selective time series shaped by reporting rules, benchmark revisions, and missingness.

By Yanan Long

llms agents benchmarks

arXiv Machine Learning

Jun 16

A Spatio-Temporal Expert Prefetching Framework for Efficient MoE-based LLM Inference

arXiv:2606. 15453v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) based large language models (LLMs), such as Qwen and DeepSeek, have recently emerged as an effective approach to improving model capacity without proportionally increasing computational cost.

By Yingnan Zhao, Razvan Bunescu, Ahmed Louri, Avinash Karanth, Ke Wang

llms

arXiv AI

Jun 16

A Self Consistency Based Reranking for Narrative Question Answering

arXiv:2606. 15741v1 Announce Type: cross Abstract: Narrative question answering (NQA) is a challenging task in natural language processing that requires models to understand long textual contexts, capture relationships across events, and generate coherent responses.

By Molham Mohamed, Ali Hamdi

llms rag nlp fine-tuning

arXiv AI

Jun 16

Resilient Consensus in Agentic AI

arXiv:2606. 15024v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly deployed in multi-agent systems where they must coordinate and agree on shared decisions.

By Sribalaji C. Anand, George J. Pappas

llms agents safety

arXiv AI

Jun 16

Controlled Dynamics Attractor Transformer

arXiv:2606. 15207v1 Announce Type: cross Abstract: Transformer architectures have dramatically advanced representation learning and inference in deep models through self-attention mechanisms.

By Cheng Zhang, Minnan Luo, Zesheng Yang, Ming Li, Yong-Jin Liu, Qinghua Zheng

llms benchmarks

arXiv AI

Jun 16

BRITE: A Benchmark for Reliable and Interpretable T2V Evaluation on Implausible Scenarios

arXiv:2605. 00873v2 Announce Type: replace-cross Abstract: The rapid advancement of photorealistic Text-to-Video (T2V) generation brings in an urgent need for up-to-date evaluation methods.

By Advait Tilak, Jiwon Choi, Nazifa Mouli, Wei Le

llms diffusion multimodal benchmarks safety

arXiv AI

Jun 16

LLM Judges Have Dark Current: A Psychometric Datasheet for LLM-as-a-Judge Evaluation

arXiv:2606. 15610v1 Announce Type: cross Abstract: LLM-as-a-judge systems are now routinely used for open-ended model evaluation, where human preference annotation is costly, slow, and difficult to reproduce.

By Hiroyasu Usami, Keisuke Hara, Ayato Tsuboi, Naohiko Matsuda

llms safety

arXiv AI

Jun 16

Post-Launch Capability Expansion of Vision-Language Models via Prompting for On-Orbit Spacecraft Inspection

arXiv:2606. 15427v1 Announce Type: cross Abstract: Spaceborne inspection systems often deploy perception models prior to launch, after which updating model weights or expanding fixed label sets becomes operationally impractical.

By Nicholas A. Welsh, Lennon J. Shikhman, Monty Nehru Attazs, Seemanthini K. Putane, Van Minh Nguyen, Ryan T. White

llms computer-vision multimodal

arXiv AI

Jun 16

Let LLMs Judge Each Other: Multi-Agent Peer-Reviewed Reasoning for Medical Question Answering

arXiv:2606. 15419v1 Announce Type: cross Abstract: Objective: To enhance the accuracy, interpretability, and robustness of large language models (LLMs) in medical question answering (MedQA).

By Zaifu Zhan, Shuang Zhou, Rui Zhang

llms agents nlp benchmarks safety

arXiv Machine Learning

Jun 16

HiRo: A Compact Four-Directional Hierarchical Reservoir Token-Mixer for Efficient Image Classification

arXiv:2606. 15151v1 Announce Type: cross Abstract: Recent image classification models must balance local feature modeling, cross-window interaction, and parameter efficiency.

By Md Farhadul Islam, Ishan Thakkar, J. Todd Hastings

llms computer-vision

arXiv Machine Learning

Jun 16

Spatial Priors via Space Filling Curves for Small and Limited Data Vision Transformers

arXiv:2606. 14757v1 Announce Type: cross Abstract: Though Vision Transformers (ViTs) have become the dominant backbone in many computer vision tasks, due to permutation equivariance, their attention mechanism lacks explicit spatial inductive biases.

By Leyla Naz Candogan, Arshia Afzal, Pol Puigdemont, Volkan Cevher

llms computer-vision fine-tuning safety

arXiv AI

Jun 16

Domain-Guided Prompting of the Segment Anything Model for Seismic Interpretation: The Role of Attributes, Visualization, and Hybrid Prompts

arXiv:2606. 15786v1 Announce Type: cross Abstract: The advent of large pretrained foundation models for computer vision has significantly improved the efficiency of visual data interpretation.

By Aniq Ahmad, Heather Bedle, Ahmad Mustafa

llms computer-vision fine-tuning

arXiv AI

Jun 16

Deep Residual Injection for Full-Spectrum Forensic Signal Perception in Multimodal Large Language Models

arXiv:2606. 15880v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have been increasingly adopted in forensics for their robust semantic understanding.

By Kaiqing Lin, Zhiyuan Yan, Ruoxin Chen, Ke-Yue Zhang, Yue Zhou, Caiyong Piao, Bin Li, Taiping Yao, Bo Wang, Youchang Xiao, Shouhong Ding

llms fine-tuning multimodal benchmarks

arXiv AI

Jun 16

Orchestrated Reality: From Role-Play to Living, Playable Game Worlds -- LLM-Driven World Simulation as a Parameterized-Action POMDP

arXiv:2606. 16014v1 Announce Type: cross Abstract: Many games rely on storytelling combined with systems that track levelling, NPC behaviour, and consequence simulation; bridging tightly-authored narrative with deeply-simulated worlds -- most acute in sandbox and open-world settings -- has been prohibitively expensive.

By Yuhang Huang, Chenmiao Li, Chaowei Fang

llms agents reinforcement-learning

arXiv AI

Jun 16

State-Grounded Multi-Agent Synthetic Data Generation for Tool-Augmented LLMs

arXiv:2606. 16307v1 Announce Type: new Abstract: Training tool-augmented LLM agents requires large corpora of multi-turn, tool-grounded conversational data that is expensive to annotate, privacy-constrained in production settings, and largely absent from public datasets.

By Rahul Khedar, Eshita, Sneha Teja Sree Reddy Thondapu, Mayank Malhotra, Arup Das, Jitesh Chandra, Yun-Shiuan Chuang, Chaitanya Kulkarni, Arun Menon, Linsey Pang, Avinash Karn, Mouli V, Prakhar Mehrotra

llms agents safety

arXiv AI

Jun 16

Long-Context Modeling via GSS-Transformer Hybrid Architecture with Learnable Mixing

arXiv:2606. 16093v1 Announce Type: cross Abstract: Modeling long-range dependencies remains a central challenge in natural language processing.

By Kuzey Torlak, H\"useyin Arda Arslan, An{\i}l Dervi\c{s}o\u{g}lu, Beyza Nur Deniz, Onur Boyar

llms nlp efficiency

arXiv AI

Jun 16

Who Should Lead Decoding Now? Tracking Reliable Trajectories for Ensembling Masked Diffusion Language Models

arXiv:2606. 16281v1 Announce Type: cross Abstract: Masked Diffusion Language Models (MDLMs) have emerged as a distinct paradigm for sequence generation.

By Heecheol Yun, Joonhyung Park, Joowon Kim, Eunho Yang

llms diffusion

arXiv AI

Jun 16

Sycophancy as Material Failure under Pushback Loading: A Multi-Axis Characterization Across Three Loading Cases and up to Seventeen Material Charges

arXiv:2606. 16617v1 Announce Type: cross Abstract: Sycophancy in LLMs is documented across 70+ papers, but expert agreement on construct boundaries remains low (ICC=.

By Ferdinand M. Schessl

llms benchmarks

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