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 21

Zero Hallucination, by Construction: Hallucination-Aware Layered Oversight for Trustworthy Enterprise AI

arXiv:2607. 17883v1 Announce Type: cross Abstract: Enterprises will not deploy AI agents they cannot trust, and the most-cited reason for distrust is hallucination: confident, fluent output that is simply not true.

By Bogdan Raduta, Horia Velicu, Alexandru Preda, Serban Chiricescu

llms agents safety

arXiv AI

Jul 21

SelectInfer: Selective Neuron Loading and Computation for On-Device LLMs

arXiv:2607. 18081v1 Announce Type: cross Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities across a range of Natural Language Processing (NLP) tasks, but their high computational and memory demands pose significant challenges for deployment on resource-constrained edge devices.

By Huzaifa Shaaban Kabakibo, Eric Schniedermeyer, Artem Burchanow, Lin Wang

llms nlp fine-tuning efficiency

arXiv AI

Jul 21

Parallel Decoder Transformer: Planner-Conditioned Latent Coordination for Model-Intrinsic Parallel Generation

arXiv:2512. 10054v3 Announce Type: replace Abstract: Autoregressive language models expose one causal token frontier, even when the requested document contains sections that could be developed concurrently.

By Logan Robbins

llms efficiency benchmarks

arXiv AI

Jul 21

Deterministic Hallucination Detection in Medical VQA via Confidence-Evidence Bayesian Gain

arXiv:2603. 21693v2 Announce Type: replace Abstract: Multimodal large language models (MLLMs) have shown strong potential for medical Visual Question Answering (VQA), yet they remain prone to hallucinations, defined as generating responses that contradict the input image, posing serious risks in clinical settings.

By Mohammad Asadi, Tahoura Nedaee, Jack W. O'Sullivan, Euan Ashley, Ehsan Adeli

llms nlp multimodal benchmarks safety

arXiv AI

Jul 21

When Direct Prediction Fails: Evidence from LLM-Based Misinformation Risk Evaluation

arXiv:2604. 06820v2 Announce Type: replace Abstract: LLMs make it increasingly easy to generate deceptive content at scale, creating a need for scalable misinformation risk evaluation based on whether readers find such content credible and are willing to share it.

By Zonghuan Xu, Xiang Zheng, Yutao Wu, Xingjun Ma

llms

arXiv AI

Jul 21

STAC: When Innocent Tools Form Dangerous Chains for LLM Agents

arXiv:2509. 25624v3 Announce Type: replace-cross Abstract: As LLMs advance into autonomous agents with tool-use capabilities, they introduce security challenges that extend beyond traditional content-based LLM safety concerns.

By Jing-Jing Li, Jianfeng He, Chao Shang, Devang Kulshreshtha, Xun Xian, Yi Zhang, Hang Su, Sandesh Swamy, Yanjun Qi

llms agents multimodal benchmarks safety

arXiv AI

Jul 21

Probing the Difficulty Perception Mechanism of Large Language Models

arXiv:2510. 05969v3 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly deployed on complex reasoning tasks, yet little is known about their ability to internally evaluate problem difficulty, which is an essential capability for adaptive reasoning and efficient resource allocation.

By Sunbowen Lee, Qingyu Yin, Chak Tou Leong, Jialiang Zhang, Yicheng Gong, Shiwen Ni, Min Yang, Xiaoyu Shen

llms benchmarks

arXiv AI

Jul 21

From Evidence to Trajectory: Abductive Reasoning Path Synthesis for Retrieval-Augmented Generation Agents Development

arXiv:2509. 23071v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) agent development is hindered by the lack of executable ground-truth agent-environment interaction trajectories.

By Muzhi Li, Jinhu Qi, Yihong Wu, Minghao Zhao, Liheng Ma, Yifan Li, Xinyu Wang, Zhenghan Tai, Zixing Song, Yingxue Zhang, Ho-fung Leung, Irwin King

llms rag agents nlp reinforcement-learning fine-tuning benchmarks

arXiv AI

Jul 21

ClawBench: Can AI Agents Complete Everyday Online Tasks?

arXiv:2604. 08523v2 Announce Type: replace-cross Abstract: AI agents may be able to assist with emails and documents, but can they reliably complete everyday online workflows on real websites?

By Yuxuan Zhang, Yubo Wang, Yipeng Zhu, Penghui Du, Junwen Miao, Xuan Lu, Zhuofeng Li, Xingwei Qu, Zhengkang Guo, Yuanzhe Shen, Dingjie Song, Han Zhou, Tuney Zheng, Xian Wu, Hao Yu, Songcheng Cai, Yi Lu, Yunzhuo Hao, Minyi Lei, Liang Chen, Kai Zou, Huifeng Yin, Wendong Xu, Dongfu Jiang, Ping Nie, Jiaheng Liu, Wenhu Chen, Kelsey R. Allen

llms agents benchmarks

arXiv AI

Jul 21

BERT-as-a-Judge: A Robust Alternative to Lexical Methods for Efficient Reference-Based LLM Evaluation

arXiv:2604. 09497v2 Announce Type: replace-cross Abstract: Accurate evaluation is central to the large language model (LLM) ecosystem, guiding model selection and downstream adoption across diverse use cases.

By Hippolyte Gisserot-Boukhlef, Nicolas Boizard, Emmanuel Malherbe, C\'eline Hudelot, Pierre Colombo

llms

arXiv AI

Jul 21

MedLVR: Latent Visual Reasoning for Reliable Medical Visual Question Answering

arXiv:2604. 09757v2 Announce Type: replace-cross Abstract: Medical vision--language models (VLMs) have shown strong potential for medical visual question answering (VQA), yet their reasoning remains largely text-centric: images are encoded once as static context, and subsequent inference is dominated by language.

By Suyang Xi, Songtao Hu, Yuxiang Lai, Wangyun Dan, Yaqi Liu, Shansong Wang, Xiaofeng Yang

llms rag nlp fine-tuning benchmarks

arXiv Machine Learning

Jul 21

PsiLogic: Chaos-Aware Active Cancellation for Adam with a Fair Cross-Domain Benchmark

arXiv:2607. 16268v1 Announce Type: new Abstract: Adaptive optimizers such as Adam and AdamW apply the same update rule regardless of whether training is in a chaotic early phase or near convergence.

By Ali Sultonov

llms diffusion nlp benchmarks

arXiv Machine Learning

Jul 21

HyBDM: Multi-Scale Hybrid Experts for Time Series Forecasting with Bidirectional Dependency Modeling

arXiv:2607. 16882v1 Announce Type: new Abstract: Time series forecasting (TSF) is vital to many applications, yet existing models often struggle to capture the heterogeneous long-range global patterns and short-range local variations in multivariate time series.

By Wenqiang Ma, Chen Cheng, Xue Cheng, Jiarui Ye

llms benchmarks

arXiv Machine Learning

Jul 21

The Geometry of Semantic Space: A Continuous Geometric Framework for the Transformer Architecture

arXiv:2607. 17146v1 Announce Type: cross Abstract: We present a continuous geometric framework that models the discrete algebraic operations of the Transformer architecture as an integro-differential equation (IDE) on a semantic fiber bundle $\calE = \calM \times \R^d$.

By Zhihua Liang

llms diffusion benchmarks

arXiv Machine Learning

Jul 21

An Adjoint-Sensitivity Framework for Lost-in-the-Middle Phenomena in Causal Residual Transformers

arXiv:2607. 17696v1 Announce Type: cross Abstract: We develop an adjoint-sensitivity framework for positional influence in causal residual Transformers and separate unconditional analytic results from conditional boundary-shape conclusions.

By Cheng Huan, Hongwei Yuan

llms safety

arXiv Machine Learning

Jul 21

SciForma: Structure-Faithful Generation of Scientific Diagrams

arXiv:2607. 18091v1 Announce Type: cross Abstract: Structural fidelity is essential to scientific methodology diagrams.

By Yuxuan Luo, Peng Zhang, Xinjie Zhang, Xun Guo, Zhouhui Lian, Yan Lu

llms reinforcement-learning fine-tuning

arXiv Machine Learning

Jul 21

When Benchmarks Lie: Evaluating Malicious Prompt Classifiers Under True Distribution Shift

arXiv:2602. 14161v2 Announce Type: replace Abstract: Detecting prompt injection, jailbreak attacks, and harmful requests is critical for deploying LLM-based agents safely, yet current evaluation practices in this literature overestimate generalization.

By Max Fomin

llms agents benchmarks safety

arXiv Machine Learning

Jul 21

SkillRouter: Skill Routing for LLM Agents at Scale

arXiv:2603. 22455v5 Announce Type: replace Abstract: Reusable skills let LLM agents package task-specific procedures, tool affordances, and execution guidance into modular building blocks.

By YanZhao Zheng, ZhenTao Zhang, Chao Ma, YuanQiang Yu, JiHuai Zhu, Yong Wu, Tianze Xu, Baohua Dong, Hangcheng Zhu, Ruohui Huang, Gang Yu

llms rag agents benchmarks

arXiv Machine Learning

Jul 21

When Can You Debias an LLM Judge? Identifiability Limits, a Test, and Designs for Top-k Ranking

arXiv:2607. 02104v2 Announce Type: replace Abstract: Large language models (LLMs) are increasingly used as cheap, scalable judges that compare candidate outputs pairwise.

By Jian Xu, Delu Zeng, John Paisley, Qibin Zhao

llms safety

arXiv Machine Learning

Jul 21

Hippasus: Effective and Efficient Automatic Feature Augmentation for Machine Learning Tasks on Relational Data

arXiv:2602. 02025v2 Announce Type: replace-cross Abstract: ML models critically depend on feature quality, yet in real-world settings, useful features are often distributed across multiple relational tables rather than a single dataset.

By Serafeim Papadias, Kostas Patroumpas, Dimitrios Skoutas

llms benchmarks

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