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 10

V-REX: Benchmarking Exploratory Visual Reasoning via Chain-of-Questions

arXiv:2512. 11995v2 Announce Type: replace-cross Abstract: While many vision-language models (VLMs) are developed to answer well-defined, straightforward questions with highly specified targets, as in most benchmarks, they often struggle in practice with complex open-ended tasks, which usually require multiple rounds of exploration and reasoning in the visual space.

By Chenrui Fan, Yijun Liang, Shweta Bhardwaj, Kwesi Cobbina, Ming Li, Tianyi Zhou

llms multimodal benchmarks

arXiv Machine Learning

Jun 10

Streaming Knowledge Compilation: Proactive Materiality-Scored Pinning for Time-Evolving LLM Wikis

arXiv:2606. 09877v1 Announce Type: new Abstract: LLM wiki systems compile knowledge into pre-filled KV caches for efficient inference, but assume a static corpus -- an assumption that fails whenever the underlying information landscape evolves.

By Juan M. Huerta

llms benchmarks

arXiv AI

Jun 10

Quantifying Uncertainty in AI Visibility: A Statistical Framework for Generative Search Measurement

arXiv:2603. 08924v2 Announce Type: replace-cross Abstract: AI-powered answer engines are inherently non-deterministic: identical queries submitted at different times can produce different responses and cite different sources.

By Ronald Sielinski

llms

arXiv Machine Learning

Jun 10

DUET -- Dual User Embedding Transformers for Offsite Conversion Prediction

arXiv:2606. 10243v1 Announce Type: new Abstract: Offsite conversion rate (OCVR) prediction is an important ranking problem in computational recommendation systems.

By Reazul Hasan Russel, Mingwei Tang, Rostam Shirani, Xinlong Liu, Navid Madani, Leo Ding, Yawen He, Xiangyu Wang, Mustafa Acar, Ashish Katiyar, Yuhai Li, Alan Yang, Metarya Ruparel, Derek Qiang Xu, Rupert Wu, Rui Yang, Liang Tao, Xinyi Zhao, Larry Zhang, Sri Reddy, Rob Malkin

llms rag

arXiv AI

Jun 10

CANVAS: Captioning Art with Narrative Visual-Audio AI Systems

arXiv:2606. 09846v1 Announce Type: cross Abstract: Visual art remains largely inaccessible to blind and low-vision (BLV) audiences due to brief or absent alt-text, which rarely conveys the sensory, spatial, or emotional qualities of an artwork.

By Vignesh Nagarajan

llms multimodal

arXiv AI

Jun 10

Transformer Based Model for Spatiotemporal Feature Learning in EEG Emotion Recognition

arXiv:2606. 10718v1 Announce Type: cross Abstract: Electroencephalography (EEG) is a widely adopted technique for monitoring brain activity, offering valuable insights into neurological states due to its high temporal resolution and cost-effectiveness.

By Xinglong Cui, Dian Gu

llms benchmarks

arXiv AI

Jun 10

Unifying Data, Memory, and Compute Efficiency in LLM training: A Survey

arXiv:2606. 10706v1 Announce Type: cross Abstract: Resource constraints increasingly determine what can be trained, fine-tuned, and deployed in large language models (LLMs), yet efficiency is often studied through isolated techniques rather than as an interacting system of limits.

By Vanessa Schmidt, Huy Hoang Nguyen, C\'edric Jung, Shirin Salehi, Anke Schmeink

llms fine-tuning efficiency

arXiv AI

Jun 10

PreAct-Bench: Benchmarking Predictive Monitoring in LLMs

arXiv:2606. 09890v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as autonomous agents capable of executing multi-step action trajectories toward a given objective.

By Hainiu Xu, Italo Luis da Silva, Jiangnan Ye, Yuhao Wang, Wei Liu, Linyi Yang, Jonathan Richard Schwarz, Nicola Paoletti, Yulan He, Hanqi Yan

llms agents benchmarks safety

arXiv AI

Jun 10

Causal Ensemble Agent: Hierarchical Causal Discovery with LLM-guided Expert Reweighting

arXiv:2606. 10607v1 Announce Type: cross Abstract: Causal discovery aims to uncover causal structures from observational data, which is crucial for real-world decision-making.

By Xinyu Li, Yuanyuan Wang, Haoxuan Li, Chuan Zhou, Erdun Gao, Bo Han, Tongliang Liu, Kun Zhang, Howard Bondell, Mingming Gong

llms agents safety

arXiv AI

Jun 10

Supervised Fine-tuning with Synthetic Rationale Data Hurts Real-World Disease Prediction

arXiv:2606. 10279v1 Announce Type: new Abstract: Supervised fine-tuning with synthetic rationale data is widely assumed to improve language model performance on clinical prediction tasks by teaching models not just what to predict but why.

By Buxin Su, Bingxuan Li, Cheng Qian, Yiwei Wang, Jin Jin, Bingxin Zhao

llms fine-tuning

arXiv AI

Jun 10

A Unified Multi-Modal Framework for Intelligent Financial Systems: Integrating Reinforcement Learning, High-Frequency Trading, and Game-Theoretic Approaches with Cross-Modal Sentiment Analysis

arXiv:2606. 10412v1 Announce Type: new Abstract: The rapid evolution of financial technology demands sophisticated artificial intelligence systems capable of handling diverse challenges across multiple domains simultaneously.

By Fanrong Liu, Zhang Yuwei, Mingni Luo

llms rag nlp reinforcement-learning multimodal benchmarks

arXiv AI

Jun 10

Structure from Reasoning, Numbers from Search: On-Premise Open LLMs as Structural Priors for Coupled MIMO Controller Tuning

arXiv:2606. 11015v1 Announce Type: new Abstract: Tuning controllers for strongly coupled multi-input multi-output (MIMO) industrial processes is hard: decentralized classical auto-tuning ignores loop interaction, and local numerical optimization from natural initializations stalls in the resulting non-convex cost landscape.

By Jiaxuan Chen, Haonan Li, Yang Shu

llms benchmarks safety

arXiv AI

Jun 10

Trainable Smooth-Rotation Transforms with Learned Channel Scales for LLM Quantization

arXiv:2606. 09927v1 Announce Type: cross Abstract: Post-training quantization (PTQ) is one of the most practical ways to reduce the serving cost of Large Language Models (LLMs), but activation quantization remains difficult because outlier-dominated channels lead to large quantization errors.

By Patrik Czak\'o, G\'abor Kert\'esz, S\'andor Sz\'en\'asi

llms efficiency

arXiv AI

Jun 10

CIAware-Bench: Benchmarking Control Intervention Awareness Across Frontier LLMs

arXiv:2606. 11063v1 Announce Type: new Abstract: AI control protocols oversee untrusted models by monitoring their actions and modifying potentially unsafe steps, often using a trusted model.

By Joachim Schaeffer, Thomas Jiralerspong, Alexander Panfilov, Guillaume Lajoie, Jonas Geiping, Yoshua Bengio, Roland S. Zimmermann

llms benchmarks

arXiv AI

Jun 10

LLM-Guided Neural Architecture Search for Robust Co-Design of Physical Neural Networks

arXiv:2606. 10294v1 Announce Type: cross Abstract: Deploying neural networks on unconventional hardware demands architectures that co-optimize task accuracy and platform-specific constraints such as energy cost, physical non-idealities, and numerical precision.

By Tyler King, Timothee Leleu

llms

arXiv AI

Jun 10

What Fits (Into Few Tokens) Doesn't Overfit: Compression and Generalization in ML Research Agents

arXiv:2606. 11045v1 Announce Type: new Abstract: Reusing a held-out benchmark adaptively should, in principle, invite overfitting.

By Martin Andres Bertran, Aaron Roth, Zhiwei Steven Wu

llms agents diffusion reinforcement-learning benchmarks

arXiv AI

Jun 10

Test-time Adversarial Takeover: A Real-time Hijacking Interface against Robotic Diffusion Policies

arXiv:2606. 10371v1 Announce Type: cross Abstract: Diffusion-based action generation has become a foundational component of embodied AI, but its reliance on visual conditioning leaves deployed visuomotor policies vulnerable to adversarial manipulation.

By Zi Yin, Peilin Chai, Siyuan Huang, Zhanhao Hu

llms diffusion robotics safety

arXiv AI

Jun 10

Local Is Not a Sufficient Privacy Boundary: Governing OS-Integrated On-Device AI

arXiv:2606. 10173v1 Announce Type: cross Abstract: As AI systems move into operating systems, privacy no longer turns only on whether a model runs locally.

By Jonghyun Chung, Sanket Badhe

llms rag safety

arXiv AI

Jun 10

Effective Reinforcement Learning for Agentic Search by Recycling Zero-Variance Queries During Training

arXiv:2606. 10709v1 Announce Type: cross Abstract: The use of GRPO-style algorithms has become the standard strategy for training LLM search agents under outcome-only rewards.

By Jo\~ao Coelho, Jo\~ao Magalh\~aes, Bruno Martins, Chenyan Xiong

llms agents reinforcement-learning benchmarks

arXiv AI

Jun 10

Beyond Static Evaluation: Co-Evolutionary Mechanisms for LLM-Driven Strategy Evolution in Adversarial Games

arXiv:2606. 10389v1 Announce Type: new Abstract: Recent advances in LLM-driven code evolution have enabled automated discovery by iteratively generating and improving programs.

By Haoran Li, Zengle Ge, Ziyang Zhang, Xiaomin Yuan, Yui Lo, Qianhui Liu, Bocheng An, Dongke Rong, Jiaqun Liu, Annan Li, Jianmin Wu, Dawei Yin, Dou Shen

llms agents safety

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