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

TERMS-Bench: Diagnosing LLM Negotiation Agents Beyond Deal Rate

arXiv:2605. 13909v2 Announce Type: replace-cross Abstract: Negotiation is a central mechanism of economic exchange, shaping markets, procurement, labor agreements, and resource allocation.

By Erica Zhang, Fangzhao Zhang, Aneesh Pappu, Batu El, Jose Blanchet, Susan Athey, Jiashuo Liu, James Zou

llms agents benchmarks

arXiv Machine Learning

Jun 16

Semantic DLM+: Improving Diffusion Language Models through Bias-variance Trade-off in Transition Kernel Design

arXiv:2606. 15327v1 Announce Type: new Abstract: Diffusion Language Models (DLMs) have demonstrated strong scaling capacity as alternatives to autoregressive language models.

By Keyue Jiang, Yuxiang Wang, Yanan Zhao, Xiang Yu, Qifang Zhao, Bohan Tang, Baojian Zhou, Yanghua Xiao, Lin Qu, Xiaoxiao Xu

llms diffusion safety

arXiv AI

Jun 16

Beyond Case Law: Evaluating Structure-Aware Retrieval and Safety in Statute-Centric Legal QA

arXiv:2604. 06173v2 Announce Type: replace-cross Abstract: Legal QA benchmarks have predominantly focused on case law, overlooking the unique challenges of statute-centric regulatory reasoning.

By Kyubyung Chae, Jewon Yeom, Jeongjae Park, Seunghyun Bae, Ijun Jang, Hyunbin Jin, Jinkwan Jang, Taesup Kim

llms benchmarks safety

arXiv AI

Jun 16

Attention, not scale, drives human-AI alignment in multimodal language prediction

arXiv:2308. 06035v4 Announce Type: replace Abstract: Humans routinely draw on visual context to predict upcoming words.

By Viktor Kewenig, Andrew Lampinen, Samuel A. Nastase, Christopher Edwards, Quitterie Lacome D'Elascombe, Akilles Rechardt, Jeremy I Skipper, Gabriella Vigliocco

llms multimodal benchmarks safety

arXiv AI

Jun 16

Metacognitive Myopia in Large Language Models

arXiv:2408. 05568v2 Announce Type: replace Abstract: Large Language Models (LLMs) exhibit potentially harmful biases that reinforce culturally embedded stereotypes, influence moral judgments, or amplify positive evaluations of majority groups.

By Florian Scholten, Tobias R. Rebholz, Mandy H\"utter

llms rag agents

arXiv Machine Learning

Jun 16

Federated Foundation Language Model Post-Training Should Focus on Open-Source Models

arXiv:2505. 23593v4 Announce Type: replace Abstract: Post-training of foundation language models has emerged as a promising research domain in federated learning (FL) with the goal to enable privacy-preserving model improvements and adaptations to user's downstream tasks.

By Nikita Agrawal, Ruben Mayer

llms safety

arXiv Machine Learning

Jun 16

DYNA-PRUNER: Input-Adaptive Data-Model Co-Pruning for Efficient and Scalable Spatio-Temporal Media Prediction

arXiv:2606. 15346v1 Announce Type: cross Abstract: Spatio-temporal prediction supports radar/satellite nowcasting and city-scale traffic monitoring, but modern models are often too expensive for real-time deployment.

By Fuyan Zhang, Yuqi Li, Yingli Tian, Edmond S. L. Ho

llms efficiency

arXiv AI

Jun 16

Parallel Test-Time Scaling with Multi-Sequence Verifiers

arXiv:2603. 03417v2 Announce Type: replace-cross Abstract: Parallel test-time scaling, which generates multiple candidate solutions for a single problem, is a powerful technique for improving large language model performance.

By Yegon Kim, Seungyoo Lee, Chaeyun Jang, Hyungi Lee, Juho Lee

llms efficiency benchmarks

arXiv AI

Jun 16

Cross-modal Identity Mapping: Minimizing Information Loss in Modality Conversion via Reinforcement Learning

arXiv:2603. 01696v2 Announce Type: replace-cross Abstract: Large Vision-Language Models (LVLMs) often omit or misrepresent critical visual content in generated image captions.

By Haonan Jia, Shichao Dong, Xin Dong, Zenghui Sun, Jin Wang, Jinsong Lan, Xiaoyong Zhu, Bo Zheng, Kaifu Zhang

llms reinforcement-learning fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 16

Escaping the Cognitive Well: Efficient Competition Math with Off-the-Shelf Models

arXiv:2602. 16793v2 Announce Type: replace Abstract: In the past year, custom and unreleased math reasoning models reached gold medal performance on the International Mathematical Olympiad (IMO).

By Xingyu Dang, Rohit Agarwal, Rodrigo Porto, Anirudh Goyal, Liam H Fowl, Sanjeev Arora

llms benchmarks

arXiv AI

Jun 16

SDFLoRA: Selective Decoupled Federated LoRA for Privacy-preserving Fine-tuning with Heterogeneous Clients

arXiv:2601. 11219v3 Announce Type: replace-cross Abstract: Federated learning (FL) for large language models (LLMs) has attracted increasing attention as a privacy-preserving approach for adapting models over distributed data, where parameter-efficient methods such as Low-Rank Adaptation (LoRA) are widely adopted to reduce communication and memory costs.

By Zhikang Shen, Jianrong Lu, Haiyuan Wan, Jianhai Chen

llms fine-tuning benchmarks safety

arXiv AI

Jun 16

Compositional Reasoning Depth Predicts Clinical AI Failure: Empirical Evidence Consistent with Transformer Compositionality Limits in Electronic Health Record Question Answering

arXiv:2606. 16890v1 Announce Type: cross Abstract: Aggregate accuracy benchmarks conceal a systematic structure in how large language models fail at electronic health record (EHR) question answering: questions requiring more inferential steps produce disproportionately more errors.

By Sanjay Basu

llms nlp benchmarks

arXiv AI

Jun 16

The Quality-Utility Paradox: Why High-Reward Data Impairs Small Model Mathematical Reasoning

arXiv:2606. 16152v1 Announce Type: new Abstract: Knowledge distillation from powerful reasoning models is widely used to improve Small Language Models (SLMs) on mathematical reasoning, often assuming that traces with higher reward model scores provide more useful supervision.

By Haolong Qian, Xianliang Yang, Yinuo ma, Lirong Che, Feng Lu, Ye Guo, Lei Song, Jiang Bian, Chun Yuan

llms reinforcement-learning efficiency

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

Is My Vision-Language Data in Your AI? Membership Inference Test (MINT) Demo 2

arXiv:2606. 14748v1 Announce Type: cross Abstract: We present the Membership Inference Test (MINT) Demo 2, a framework designed to improve transparency in machine learning training processes.

By Daniel DeAlcala, Gonzalo Mancera, Julian Fierrez, Aythami Morales, Ruben Tolosana, Ruben Vera-Rodriguez

llms multimodal benchmarks

arXiv AI

Jun 16

Graphical-Probabilistic Modeling of Generative Flows in LLM-Native Software Systems

arXiv:2606. 15943v1 Announce Type: cross Abstract: Engineering LLM-native software remains a challenging and immature field.

By V\'ictor A. Braberman, Flavia Bonomo-Braberman

llms

arXiv Machine Learning

Jun 16

Code as a Weapon: A Consensus-Labeled Prompt Bank for Measuring Coding-Model Compliance with Malicious-Code Requests

arXiv:2605. 28734v2 Announce Type: replace-cross Abstract: A general-purpose language model that answers a harmful question returns text; a coding model that complies with a malicious request can return a working weapon: a keylogger, ransomware, an exploit that runs as written.

By Richard J. Young, Gregory D. Moody

llms agents benchmarks safety

arXiv AI

Jun 16

Large Language Models as Optimizers: A Survey of Direct vs. Tool-Augmented Approaches and Their Performance Frontiers

arXiv:2606. 15577v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly involved in complex mathematical optimization, even if the pragmatic user who triggers them is unaware of it.

By Roko Peran, Luka Hobor, Mihael Kovac, Mario Brcic

llms benchmarks

arXiv Machine Learning

Jun 16

Prompt Disentanglement via Language Guidance and Representation Alignment for Domain Generalization

arXiv:2507. 02288v2 Announce Type: replace-cross Abstract: Domain Generalization (DG) seeks to develop a versatile model capable of performing effectively on unseen target domains.

By De Cheng, Zhipeng Xu, Xinyang Jiang, Dongsheng Li, Nannan Wang, Xinbo Gao

llms benchmarks safety

arXiv Machine Learning

Jun 16

MARS: Efficient, Adaptive Co-Scheduling for Heterogeneous Agentic Systems

arXiv:2604. 26963v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly deployed as the execution core of autonomous agents rather than as standalone text generators.

By Yifei Wang, Hancheng Ye, Yechen Xu, Cong Guo, Chiyue Wei, Qinsi Wang, Dongting Li, Tingjun Chen, Hai "Helen" Li, Danyang Zhuo, Yiran Chen

llms agents efficiency

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