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 9

A Topological Characterization of Graph Neural Networks via Stochastic Block Model Embeddings on the n-Sphere

arXiv:2606. 07598v1 Announce Type: cross Abstract: We propose a topological framework for comparing trained Graph Neural Networks (GNNs) by mapping the Stochastic Block Models (SBMs) induced on the graphon-signal space of a Message Passing Neural Network (MPNN) onto the unit $n$-sphere $\sphere^{n-1}\subset\R^n$.

By Gopal Anantharaman

llms rag

arXiv AI

Jun 9

Page image classifier fine-tuned on century-spanning archives of scanned documents for further content-specific processing

arXiv:2606. 07558v1 Announce Type: cross Abstract: Purpose: Digitization projects in the humanities produce vast, heterogeneous archives of historical documents, making manual sorting impractical at scale.

By Kateryna Lutsai, Pavel Stra\v{n}\'ak, David Nov\'ak, Dana K\v{r}iv\'ankov\'a

llms computer-vision fine-tuning multimodal

arXiv Machine Learning

Jun 9

Can LLMs extract scientific consensus? A case study in high-temperature superconductivity

arXiv:2606. 07570v1 Announce Type: cross Abstract: Scientific knowledge is increasingly dispersed across vast and heterogeneous scientific literature, where important claims are often implicit, evolving, and internally debated.

By Mouyang Cheng, Wenhao He, Zhuotao Jin, Bowen Yu, Ju Li, Boris Kozinsky, Yao Wang, Pavel Volkov, Liangzi Deng, Ching-Wu Chu, Xiao-Gang Wen, Mingda Li

llms nlp benchmarks

arXiv AI

Jun 9

Closing the Sim-to-Real Gap: An Evaluation Framework for Autonomous Cyber Defense Configuration of Commercial EDR

arXiv:2606. 08168v1 Announce Type: cross Abstract: Leading commercial endpoint detection and response (EDR) products have shifted from operator-configured rule sets to multi-component systems where autonomous AI components operate alongside, and increasingly in place of, operator-deployed policies.

By Kerri Prinos, Lilianne Brush

llms agents robotics benchmarks

arXiv AI

Jun 9

From Detection to Recovery: Operational Analysis on LLM Pre-training with 504 GPUs

arXiv:2605. 09370v3 Announce Type: replace-cross Abstract: Large-scale AI training is now fundamentally a distributed systems problem, and hardware failures have become routine operating conditions rather than rare exceptions.

By Daemyung Kang, Eunjin Hwang, Hanjeong Lee, HyeokJin Kim, Hyunhoi Koo, Jeongkyu Shin, Jeongseok Kang, Jihyun Kang, Joongi Kim, Junbum Lee, Jungseung Yang, Kyujin Cho, Youngsook Song

llms

arXiv Machine Learning

Jun 9

AutoMegaKernel: A Statically-Checked Agent Harness for Self-Retargeting Megakernel Synthesis

arXiv:2606. 09682v1 Announce Type: new Abstract: AutoMegaKernel (AMK) compiles a HuggingFace Llama-family model into a single persistent cooperative CUDA kernel that runs the whole forward pass in one launch, with no per-model hand-written CUDA.

By Jaber Jaber, Osama Jaber

llms agents safety

arXiv Machine Learning

Jun 9

Claw-R1: A Step-Level Data Middleware System for Agentic Reinforcement Learning

arXiv:2606. 09138v1 Announce Type: new Abstract: Agentic reinforcement learning (RL) has become an important post-training paradigm for turning LLMs from static chatbots into interactive agents, giving rise to representative applications such as OpenClaw.

By Daoyu Wang, Mingyue Cheng, Qingchuan Li, Shuo Yu, Jie Ouyang, Qi Liu

llms agents reinforcement-learning

arXiv Machine Learning

Jun 9

QueryWeaver: Reliable Multi-Tool Query Execution Planning via LLM-Based Graph Generation

arXiv:2606. 08300v1 Announce Type: new Abstract: Many real-world queries over personal data span multiple applications and require structured planning, as individual tools expose only partial information.

By Aishwarya Chakravarthy, Vidhi Kulkarni, Duen Horng Chau

llms agents

arXiv Machine Learning

Jun 9

Constraint-Aware Optimization for Robust Protein Stability Prediction

arXiv:2606. 08100v1 Announce Type: new Abstract: Multimodal $\Delta\Delta G$ predictors integrating protein language models with inverse-folding representations achieve strong in-distribution accuracy on the Megascale dataset but exhibit limited robustness on out-of-distribution (OOD) proteins, persistent forward-reverse bias on paired-mutation benchmarks, and under-representation of rare stabilizing mutations.

By A Shivram, Aneesh S. Chivukula, Manik Gupta, Sourav Chowdhury

llms multimodal benchmarks safety

arXiv Machine Learning

Jun 9

A Unifying View of Attention Sinks: Two Algorithms, Two Solutions

arXiv:2606. 08105v1 Announce Type: new Abstract: When attention concentrates on a single token, a sink, what is the model actually computing?

By Lukas Fesser, Mozes Jacobs, Thomas Fel, Andy Keller, Sham Kakade

llms

arXiv Machine Learning

Jun 9

The Easy, the Hard, and the Learnable: Confidence and Difficulty-Adaptive Policy Optimization for LLM Reasoning

arXiv:2606. 07950v1 Announce Type: new Abstract: RL with verifiable rewards can substantially improve LLM reasoning, yet standard GRPO-style training often treats easy, hard, and learnable questions alike through uniform sampling and weighting, leading to inefficient compute allocation.

By Zhanke Zhou, Xiangyu Lu, Chentao Cao, Brando Miranda, Tongliang Liu, Bo Han, Sanmi Koyejo

llms reinforcement-learning benchmarks

arXiv Machine Learning

Jun 9

Decoding Naturalistic Emotion Dynamics from the Brain: An LLM-Enhanced Regression Framework

arXiv:2606. 07707v1 Announce Type: new Abstract: Decoding emotional states from neural signals has been typically framed as a discrete, single-label classification task based on emotionally stable stimuli, a formulation that oversimplifies the continuous, fluid, and co-occurring nature of human affect.

By Lemei Zhang, Peng Liu, Hans Dahle Kvadsheim, August S{\ae}tre Aasv{\ae}r, Shuer Ye, Reza Bonyadi, Maryam Ziaei, Jon Atle Gulla

llms

arXiv Machine Learning

Jun 9

SpectrumKV: Per-Token Mixed-Precision KV Cache Transfer for Prefill-Decode Disaggregated LLM Serving

arXiv:2606. 08635v1 Announce Type: new Abstract: Prefill-decode (PD) disaggregation decouples prompt processing from token generation, but it also turns the key-value (KV) cache into a network payload.

By Yang Pengju

llms efficiency

arXiv AI

Jun 9

CrossVLA: Cross-Paradigm Post-Training and Inference Optimization for Vision-Language-Action Models

arXiv:2605. 21854v2 Announce Type: replace-cross Abstract: Vision-Language-Action (VLA) models have rapidly converged on a small set of architectural patterns: discrete-token autoregression (e.

By Zhi Liu

llms reinforcement-learning fine-tuning multimodal benchmarks safety

arXiv AI

Jun 9

Improving the Performance and Learning Stability of Parallelizable RNNs Designed for Ultra-Low Power Applications

arXiv:2605. 11855v2 Announce Type: replace-cross Abstract: Sequence learning is dominated by Transformers and parallelizable recurrent neural networks (RNNs) such as state-space models, yet learning long-term dependencies remains challenging, and state-of-the-art designs trade power consumption for performance.

By Julien Brandoit, Arthur Fyon, Damien Ernst, Guillaume Drion

llms efficiency benchmarks

arXiv AI

Jun 9

AgroOmni: A Large-Scale Multi-view Agricultural Dataset for Cross-Scale Multimodal Reasoning

arXiv:2603. 14342v2 Announce Type: replace-cross Abstract: Modern agricultural data is sourced from diverse platforms and spans multiple spatial scales, ranging from ground-level close-up photography to Unmanned Aerial Vehicle (UAV) aerial observation and satellite remote sensing imagery.

By Jiarui Zhang, Junqi Hu, Zurong Mai, Yang Liu, Yuhang Chen, Shuohong Lou, Henglian Huang, Hong Cheng, Lingyuan Zhao, Jianxi Huang, Yutong Lu, Haohuan Fu, Juepeng Zheng

llms nlp fine-tuning multimodal benchmarks safety

arXiv AI

Jun 9

Revisiting Training Scale: An Empirical Study of Token Count, Power Consumption, and Parameter Efficiency

arXiv:2601. 06649v2 Announce Type: replace-cross Abstract: Research in machine learning has questioned whether increases in training token counts reliably produce proportional performance gains in large language models.

By Joe Dwyer

llms

arXiv AI

Jun 9

Efficient Onboard Vision-Language Inference in UAV-Enabled Low-Altitude Economy Networks via LLM-Enhanced Optimization

arXiv:2510. 10028v2 Announce Type: replace-cross Abstract: The rapid advancement of Low-Altitude Economy Networks (LAENets) has enabled a variety of applications, including aerial surveillance, environmental sensing, and semantic data collection.

By Yang Li, Ruichen Zhang, Yinqiu Liu, Guangyuan Liu, Abbas Jamalipour, Xianbin Wang, Dong In Kim

llms nlp reinforcement-learning multimodal

arXiv AI

Jun 9

Large Language Models for Imbalanced Classification: Diversity makes the difference

arXiv:2510. 09783v2 Announce Type: replace-cross Abstract: Oversampling is one of the most widely used approaches for addressing imbalanced classification.

By Dang Nguyen, Sunil Gupta, Kien Do, Thin Nguyen, Taylor Braund, Alexis Whitton, Svetha Venkatesh

llms fine-tuning benchmarks

arXiv AI

Jun 9

TAO: Tolerance-Aware Optimistic Verification for Floating-Point Neural Networks

arXiv:2510. 16028v4 Announce Type: replace-cross Abstract: Neural networks increasingly run on hardware outside the user's control (cloud GPUs, inference marketplaces).

By Jianzhu Yao, Hongxu Su, Taobo Liao, Zerui Cheng, Huan Zhang, Xuechao Wang, Pramod Viswanath

llms rag diffusion efficiency safety

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