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

Aug 13

How Organizations Use AI: Evidence from ChatGPT

arXiv:2608. 12236v1 Announce Type: cross Abstract: We study how organizations use frontier generative AI by linking ChatGPT Enterprise account records to usage, worker roles, task classifications, and public-company financial data through March 2026.

By Aaron Chatterji, David Holtz, Neel Rakholia, Prasanna Tambe, Gawesha Weeratunga

llms safety

arXiv AI

Aug 13

OpenAg: Democratizing Agricultural Intelligence

arXiv:2506. 04571v3 Announce Type: replace Abstract: Agriculture is undergoing a major transformation driven by artificial intelligence (AI), machine learning, and knowledge representation technologies.

By Srikanth Thudumu, Jason Fisher

llms rag agents fine-tuning

arXiv AI

Aug 13

CORE-3D: Context-aware Open-vocabulary Retrieval by Embeddings in 3D

arXiv:2509. 24528v4 Announce Type: replace-cross Abstract: Object retrieval from a scene has become a new trend of research due to its numerous applications.

By Mohamad Amin Mirzaei, Pantea Amoie, Ali Ekhterachian, Matin Mirzababaei, Babak Khalaj

llms rag computer-vision multimodal benchmarks

arXiv Machine Learning

Aug 13

Draw This First

arXiv:2608. 12064v1 Announce Type: cross Abstract: We invert the typical formulation of sketch generation: instead of drawing strokes in order, we predict a 2D field that defines the order in which strokes are drawn.

By Dazhi Zhong, Rowan Bradbury, Grant Davis

llms computer-vision

arXiv AI

Aug 13

Agent Safety Should Be a Runtime Contract

arXiv:2608. 11274v1 Announce Type: cross Abstract: The dominant paradigm treats AI safety as a property to be instilled during model training via RLHF, DPO, or Constitutional AI.

By Albus W. Ng, Yi Han, Jusheng Zhang, Wenhao Wang

llms agents reinforcement-learning safety

arXiv AI

Aug 13

Methodologies for Improving the Quality of AI Tutoring in K-12 Education

arXiv:2608. 11259v1 Announce Type: cross Abstract: Many AI tutors leverage large language models (LLMs) today.

By Tushar Udeshi, Anna Khazenzon, Kabir Khan, Nick Breen, RJ Corwin, Chris DiGiano, Kodi Weatherholtz, Marek Zaluski

llms agents

arXiv Machine Learning

Aug 13

Disentangling the Expressivity of RoPE

arXiv:2608. 11909v1 Announce Type: new Abstract: Two accounts recur in explanations of the success of rotary position embeddings (RoPE).

By Selim Jerad, Anej Svete, Jiaoda Li, Ryan Cotterell

llms rag safety

arXiv AI

Aug 13

LoongReflect: Boosting Long-Horizon Reflection in Search Agents via Global Perspective Distillation

arXiv:2608. 11967v1 Announce Type: cross Abstract: Large language model agents increasingly rely on long-horizon reasoning to solve complex tasks involving planning, tool use, and memory.

By Zhixin Zhang, Xinke Jiang, Zhibang Yang, Weixuan Xu, Guohong Qiu, Xu Chu, Junfeng Zhao, Yasha Wang

llms rag agents reinforcement-learning efficiency benchmarks

arXiv AI

Aug 13

Causal Agent based on Large Language Model

arXiv:2408. 06849v3 Announce Type: replace Abstract: The large language model (LLM) has achieved significant success across various domains.

By Kairong Han, Kun Kuang, Ziyu Zhao, Junjian Ye, Fei Wu

llms agents benchmarks

arXiv Machine Learning

Aug 13

ODE-Based Transformer Decoders for Iterative Sign Language Translation

arXiv:2608. 11352v1 Announce Type: cross Abstract: Sign language translation has achieved strong results with Transformer architectures, yet recent improvements largely rely on scaling model capacity at the cost of increased computation.

By Tu\u{g}\c{c}e K{\i}z{\i}ltepe, Hacer Yalim Keles

llms benchmarks

arXiv Machine Learning

Aug 13

Robust Multi-Tier Infant-Centered Audio Understanding with Whisper via Structured Speaker Conditioning

arXiv:2608. 11587v1 Announce Type: cross Abstract: Recent advances in model design and self-supervised audio representations have improved speech and audio understanding, yet infant-centered naturalistic recordings remain challenging due to limited labeled data, low signal-to-noise ratio, and cross-family domain shifts.

By Xulin Fan, Jialu Li, Mohammad Nur Hossain Khan, Kexin Hu, Bashima Islam, Mark Hasegawa-Johnson, Nancy L. McElwain

llms fine-tuning multimodal safety

arXiv AI

Aug 13

ExRole: From Team Trajectories to Executable Roles in Multi-Agent Language Models

arXiv:2608. 11949v1 Announce Type: new Abstract: Roles provide an interpretable interface for organizing language-model agents, yet most multi-agent systems treat them as hand-written prompt labels disconnected from learned behavior and parameter updates.

By Zhou Liu, Chaoyang Han, Zewei Pan, Zeli Su, Wentao Zhang

llms agents fine-tuning benchmarks

arXiv Machine Learning

Aug 13

Task- and dataset-specific information in protein language models

arXiv:2608. 12090v1 Announce Type: new Abstract: Protein language models (PLMs) have transferred the latest advances from natural language processing to computational biology.

By Roman Joeres, Ilya Senatorov, Olga V. Kalinina

llms rag nlp

arXiv AI

Aug 13

Motion-as-Prompt: Enhancing Motion Reasoning in Multimodal Large Language Models via Motion-Guided Cross-Frame Visual Prompting

arXiv:2608. 11655v1 Announce Type: cross Abstract: Motion-centric video reasoning is fundamental to interactive applications such as robotic manipulation and autonomous navigation.

By Xikai Sun, Kebin Liu, Haotian Wang, Li Liu, Xu Wang, Yunhao Liu

llms agents robotics multimodal

arXiv AI

Aug 13

Uncertainty as a Planning Signal: Multi-Turn Decision Making for Goal-Oriented Conversation

arXiv:2604. 03924v2 Announce Type: replace-cross Abstract: Goal-oriented conversational systems require making sequential decisions under uncertainty about the user's intent, where the algorithm must balance information acquisition and target commitment over multiple turns.

By Xinyi Ling, Ye Liu, Reza Averly, Xia Ning

llms benchmarks

arXiv AI

Aug 13

Learning to Persuade Exposes How Easily LLMs Abandon Correct Beliefs

arXiv:2608. 11624v1 Announce Type: cross Abstract: Persuasion is a core dynamic of natural language communication, shaping how large language models (LLMs) update beliefs, resolve disagreements, and reach decisions.

By Nimet Beyza Bozdag, Emre Can Acikgoz, Gokhan Tur, Dilek Hakkani-T\"ur

llms agents reinforcement-learning safety

arXiv Machine Learning

Aug 13

Robustness of AI-Art Detectors under Generator Shift

arXiv:2608. 11643v1 Announce Type: cross Abstract: Text-to-image generative models have advanced rapidly, with modern Diffusion Transformer architectures producing images that are increasingly difficult to distinguish from human-created artwork.

By Shivank Singh Thakur, Meien Li, Mark Stamp

llms diffusion

arXiv AI

Aug 13

Social Chain of Thought: A Multi-Agent Architecture Grounded in Medical Differential Diagnosis Methodology

arXiv:2608. 11420v1 Announce Type: new Abstract: Medical diagnostic reasoning is a high-impact use case for LLMs that carries significant implications for the health and wellbeing of users.

By Del Coburn, Scott Sanner, Dan Silver

llms agents

arXiv AI

Aug 13

Domain-Aware Lightweight Spectral-Grouped Convolutions for Hyperspectral Fish Freshness Classification

arXiv:2608. 12227v1 Announce Type: cross Abstract: Hyperspectral imaging (HSI) offers nondestructive assessment of fish freshness by detecting biochemical alterations across spectral bands.

By Kazi Nabiul Alam, Pooneh Bagheri Zadeh, Akbar Sheikh-Akbari

llms benchmarks

arXiv Machine Learning

Aug 13

Long-Horizon Forecasting of Complete Financial Statements with Forma

arXiv:2608. 11327v1 Announce Type: new Abstract: Specialist training beats generalist scale when forecasting financial statements.

By Travis L. Johnson, Jiannan Jiang, Soumyabrata Chaudhuri, Yihao Chen, Lauren Falvey, Donal O'Cofaigh

llms benchmarks

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