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

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

RobustVLA: On Robustness of Vision-Language-Action Model against Multi-Modal Perturbations

arXiv:2510. 00037v5 Announce Type: replace-cross Abstract: In Vision-Language-Actionf(VLA) models, robustness to real-world perturbations is critical for deployment.

By Jianing Guo, Zhenhong Wu, Chang Tu, Yiyao Ma, Xiangqi Kong, Zhiqian Liu, Jiaming Ji, Shuning Zhang, Yuanpei Chen, Kai Chen, Qi Dou, Yaodong Yang, Xianglong Liu, Huijie Zhao, Weifeng Lv, Simin Li

llms diffusion reinforcement-learning robotics multimodal safety

arXiv AI

Jul 21

Spatiotemporal Knowledge Graphs as Persistent Scene Memory for Embodied Question Answering

arXiv:2510. 01483v3 Announce Type: replace-cross Abstract: Vision-language models (VLMs) demonstrate strong image-level scene understanding, but reasoning over long egocentric video remains costly: because VLMs maintain no persistent memory or explicit spatial representation, all sampled frames must be re-processed for every new query.

By Mohamad Al Mdfaa, Svetlana Lukina, Timur Akhtyamov, Arthur Nigmatzyanov, Dmitrii Nalberskii, Sergey Zagoruyko, Gonzalo Ferrer

llms agents nlp robotics multimodal benchmarks

arXiv AI

Jul 21

AI for Auto-Research: Roadmap & User Guide

arXiv:2605. 18661v2 Announce Type: replace Abstract: AI-assisted research is crossing a threshold: fully automated systems can now generate research papers for as little as $15, while long-horizon agents can execute experiments, draft manuscripts, and simulate critique with minimal human input.

By Lingdong Kong, Xian Sun, Wei Chow, Linfeng Li, Kevin Qinghong Lin, Xuan Billy Zhang, Song Wang, Rong Li, Qing Wu, Wei Gao, Yingshuo Wang, Shaoyuan Xie, Jiachen Liu, Leigang Qu, Shijie Li, Lai Xing Ng, Benoit R. Cottereau, Ziwei Liu, Tat-Seng Chua, Wei Tsang Ooi

llms agents benchmarks

arXiv AI

Jul 21

Lil: Less is Less When Applying Post-Training Sparse-Attention Algorithms in Long-Decode Stage

arXiv:2601. 03043v4 Announce Type: replace-cross Abstract: Large language models (LLMs) demonstrate strong capabilities across a wide range of complex tasks and are increasingly deployed at scale, placing significant demands on inference efficiency.

By Junhao Hu, Fangze Li, Mingtao Xu, Feifan Meng, Shiju Zhao, Tiancheng Hu, Ting Peng, Anmin Liu, Wenrui Huang, Chenxu Liu, Ziyue Hua, Tao Xie

llms benchmarks

arXiv AI

Jul 21

CarbonNet: How Computer Vision Plays a Role in Climate Change? Application: Learning Geomechanics from Subsurface Geometry of CCS to Mitigate Global Warming

arXiv:2403. 06025v4 Announce Type: replace-cross Abstract: We introduce a new approach using computer vision to predict the land surface displacement from subsurface geometry images for Carbon Capture and Sequestration (CCS).

By Wei Chen, Yunan Li, Yuan Tian

llms computer-vision

arXiv AI

Jul 21

Attentions Under the Microscope: A Comparative Study of Resource Utilization for Variants of Self-Attention

arXiv:2507. 07247v2 Announce Type: replace-cross Abstract: As large language models (LLMs) and visual language models (VLMs) grow in scale and application, attention mechanisms have become a central computational bottleneck due to their high memory and time complexity.

By Zhengyu Tian, Anantha Padmanaban Krishna Kumar, Hemant Krishnakumar, Reza Rawassizadeh

llms efficiency benchmarks

arXiv AI

Jul 21

mHC-GNN: Manifold-Constrained Hyper-Connections for Graph Neural Networks

arXiv:2601. 02451v2 Announce Type: replace-cross Abstract: Graph Neural Networks (GNNs) suffer from over-smoothing in deep architectures and expressiveness bounded by the 1-Weisfeiler-Leman (1-WL) test.

By Subhankar Mishra

llms benchmarks

arXiv AI

Jul 21

GRIP: In-Parameter Graph Reasoning through Fine-Tuning Large Language Models

arXiv:2511. 07457v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities in modeling sequential textual data and generalizing across diverse tasks.

By Jiarui Feng, Donghong Cai, Yixin Chen, Muhan Zhang

llms fine-tuning safety

arXiv AI

Jul 21

When AI Takes the Couch: Psychometric Jailbreaks Reveal Internal Conflict in Frontier Models

arXiv:2512. 04124v4 Announce Type: replace-cross Abstract: Frontier language models increasingly participate in conversations about distress and mental health, yet the mechanisms that generate anthropomorphic self narratives remain unclear.

By Afshin Khadangi, Hanna Marxen, Amir Sartipi, Igor Tchappi, Gilbert Fridgen

llms reinforcement-learning safety

arXiv Machine Learning

Jul 21

FlowSonic: Stable Zero-Shot Music Editing via High-Order Trajectory Integration

arXiv:2607. 17526v1 Announce Type: cross Abstract: Zero-shot text-guided editing of real-world music recordings requires balancing semantic modification with faithful preservation of the original musical structure.

By Ali Boudaghi, Hadi Zare

llms diffusion safety

arXiv AI

Jul 21

Autonomous VR-Based Risk Detection for Situational Awareness in Dangerous Settings

arXiv:2607. 16582v1 Announce Type: cross Abstract: In high-risk environments such as disaster response, situational awareness depends not only on detecting hazards but also on communicating them clearly to human operators.

By Mohammad Eskandari, Murali Krishna Varma Indukuri, Stephanie M. Lukin, Cynthia Matuszek

llms agents robotics multimodal safety

arXiv Machine Learning

Jul 21

Planning with Transformers: Chain of Computation and Structured Context Windows

arXiv:2607. 17710v1 Announce Type: new Abstract: Large Language Models (LLMs) have had a remarkable impact across many areas of machine learning.

By Ehsan Futuhi, Nathan R. Sturtevant

llms

arXiv AI

Jul 21

LenGuard-GPC: Length Guarding with Guided-Prompt Consistency for Spatial Reasoning Reinforce Learning

arXiv:2607. 17243v1 Announce Type: new Abstract: Multi-view spatial reasoning requires vision-language models to compare visual evidence across images, align object correspondences, and infer spatial relations over long visual contexts, a setting where chain-of-thought reasoning tends to grow verbose without becoming more accurate.

By Xingjian Tao, Yiwei Wang, Yujun Cai, Jing Tang

llms reinforcement-learning multimodal benchmarks

arXiv AI

Jul 21

Think, Plan, Paint: Layout-Aware Reasoning for Controllable Image Generation in Unified Models

arXiv:2607. 16409v1 Announce Type: cross Abstract: Unified Multimodal Large Language Models (MLLMs) offer a promising paradigm for unifying visual understanding and generation, yet they still struggle to follow complex spatial instructions and logical constraints in controllable image generation.

By Junhao Liu, Jian-Wei Zhang, Tao Huang, Miles Yang, Zhao Zhong, Liefeng Bo

llms diffusion multimodal benchmarks safety

arXiv AI

Jul 21

The Autonomous Agency Scale: A Behavioral Framework for Measuring Self-Directed Behavior in AI Systems

arXiv:2607. 17947v1 Announce Type: new Abstract: Existing AI measurement frameworks quantify cognitive capability, task automation, or catastrophic risk, but none measure autonomous agency: the extent to which a system behaves in a self-directed way.

By Samuel Presgraves

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arXiv AI

Jul 21

Breaking the Factorization Barrier in Diffusion Language Models

arXiv:2603. 00045v3 Announce Type: replace-cross Abstract: Diffusion language models theoretically allow for efficient parallel generation but are practically hindered by the ``factorization barrier'': the assumption that simultaneously predicted tokens are independent.

By Ian Li, Zilei Shao, Benjie Wang, Rose Yu, Guy Van den Broeck, Anji Liu

llms diffusion reinforcement-learning

arXiv AI

Jul 21

GradAlign: Gradient-Aligned Data Selection for LLM Reinforcement Learning

arXiv:2602. 21492v2 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has become a central post-training paradigm for large language models (LLMs), but its performance is highly sensitive to the quality of training problems.

By Ningyuan Yang, Weihua Du, Weiwei Sun, Sean Welleck, Yiming Yang

llms reinforcement-learning fine-tuning

arXiv AI

Jul 21

Natural Language Access to Domain-Specific Metadata: A Reusable Framework for LLM Query Generation

arXiv:2607. 18029v1 Announce Type: cross Abstract: Researchers need to answer ad-hoc questions about the contents of domain-specific archives but often lack the expertise to write structured queries on the metadata.

By Blake G. Fitch, Cato Elia Kurtz

llms agents fine-tuning benchmarks safety data-engineering

arXiv Machine Learning

Jul 21

GeneSpeak-FP: Target and Compound Retrieval from Observed Cell-Level Perturbation Signatures

arXiv:2607. 17671v1 Announce Type: new Abstract: Large-scale single-cell perturbation atlases make it possible to ask an inverse question: given an observed transcriptional response, which annotated targets and compounds in a fixed library are most consistent with that response?

By Kseniia Vaniushkina, Jeongmin Lim, Jinyong Park

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