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 Machine Learning

Jun 4

Fixed Aggregation Features Can Rival GNNs

arXiv:2601. 19449v2 Announce Type: replace Abstract: Graph neural networks (GNNs) are widely believed to excel at node representation learning through trainable neighborhood aggregations.

By Celia Rubio-Madrigal, Rebekka Burkholz

llms benchmarks safety

arXiv Machine Learning

Jun 4

Vision Transformer Finetuning Benefits from Non-Smooth Components

arXiv:2602. 06883v3 Announce Type: replace Abstract: The smoothness of the transformer architecture has been extensively studied in the context of generalization, training stability, and adversarial robustness.

By Ambroise Odonnat, Laetitia Chapel, Romain Tavenard, Ievgen Redko

llms computer-vision fine-tuning safety

arXiv Machine Learning

Jun 4

Global Sketch-Based Watermarking for Diffusion Language Models

arXiv:2606. 04486v1 Announce Type: cross Abstract: Watermarking methods for language models have been studied extensively in the autoregressive setting, where tokens are generated sequentially.

By Daniel Zhao

llms diffusion safety

arXiv AI

Jun 4

A Systematic Analysis of Linguistic Features in AI-Generated Text Detection Across Domains and Models

arXiv:2606. 04177v1 Announce Type: cross Abstract: Interpretable linguistic features offer a promising approach for explaining why a given text appears machine-generated, particularly for non-expert users.

By Yassir El Attar, Esra D\"onmez, Maximilian Maurer, Agnieszka Falenska

llms

arXiv AI

Jun 4

The Variance Brain Foundation Models Forgot: Third-Order Statistics Predict Cognition Where Billion-Parameter Models Fail

arXiv:2606. 04010v1 Announce Type: cross Abstract: Brain foundation models (BFMs) are self-supervised Transformers pretrained on fMRI data.

By Giovanni Marraffini, Gabriel Mahuas, Trinidad Borrell, Victoria Shevchenko, Demian Wassermann

llms fine-tuning benchmarks

arXiv AI

Jun 4

Bilevel Autoresearch: Meta-Autoresearching Itself

arXiv:2603. 23420v2 Announce Type: replace Abstract: If autoresearch is itself a form of research, then autoresearch can be applied to research itself.

By Yaonan Qu, Meng Lu

llms agents benchmarks

arXiv AI

Jun 4

Arithmetic Pedagogy for Language Models

arXiv:2606. 05106v1 Announce Type: cross Abstract: We investigate whether methods of human mathematics pedagogy can guide the training of language models toward arithmetic reasoning.

By Andhika Bernard Lumbantobing, Hokky Situngkir

llms nlp reinforcement-learning

arXiv AI

Jun 4

Dynamic Infilling Anchors for Format-Constrained Generation in Diffusion Large Language Models

arXiv:2606. 04535v1 Announce Type: cross Abstract: Diffusion large language models (dLLMs) offer bidirectional attention and parallel generation, enabling them to exploit global context and naturally support format-constrained tasks like parseable JSON or reasoning templates.

By Boyan Han, Yiwei Wang, Yi Song, Yujun Cai, Chi Zhang

llms diffusion benchmarks

arXiv AI

Jun 4

RowNet: A Memory Transformer for Tabular Regression

arXiv:2606. 04445v1 Announce Type: cross Abstract: Real estate valuation is a structured regression problem in which prices are governed by heterogeneous feature types, sparse regional effects, nonlinear interactions, and the practical logic of comparable properties.

By Askat Rakhymbekov, Gulshat Muhametjanova

llms

arXiv AI

Jun 4

Revisiting Vul-RAG: Reproducibility and Replicability of RAG-based Vulnerability Detection with Open-Weight Models

arXiv:2606. 04739v1 Announce Type: cross Abstract: Large language models (LLMs) have shown strong potential for automated software vulnerability detection, particularly in retrieval-augmented generation (RAG) settings.

By Sabrina Kaniewski, Fabian Schmidt, Tobias Heer

llms rag

arXiv Machine Learning

Jun 4

SFMP: Fine-Grained, Hardware-Friendly and Search-Free Mixed-Precision Quantization for Large Language Models

arXiv:2602. 01027v2 Announce Type: replace Abstract: Mixed-precision quantization is a promising approach for compressing large language models under tight memory budgets.

By Xin Nie, Haicheng Zhang, Liang Dong, Beining Feng, Jinhong Weng, Guiling Sun

llms efficiency benchmarks

arXiv Machine Learning

Jun 4

Towards A Generative Protein Evolution Machine with DPLM-Evo

arXiv:2605. 00182v3 Announce Type: replace Abstract: Proteins are shaped by gradual evolution under biophysical and functional constraints.

By Xinyou Wang, Liang Hong, Jiasheng Ye, Zaixiang Zheng, Yu Li, Shujian Huang, Quanquan Gu

llms diffusion benchmarks safety

arXiv AI

Jun 4

Platonic Transformers: A Solid Choice For Equivariance

arXiv:2510. 03511v3 Announce Type: replace-cross Abstract: While widespread, Transformers lack inductive biases for geometric symmetries common in science and computer vision.

By Mohammad Mohaiminul Islam, Rishabh Anand, David R. Wessels, Friso de Kruiff, Thijs P. Kuipers, Rex Ying, Clara I. S\'anchez, Sharvaree Vadgama, Georg B\"okman, Erik J. Bekkers

llms computer-vision benchmarks

arXiv AI

Jun 4

UniCAD: A Unified Benchmark and Universal Model for Multi-Modal Multi-Task CAD

arXiv:2606. 05058v1 Announce Type: cross Abstract: Computer-Aided Design (CAD) underpins modern engineering and manufacturing by enabling the creation of precise, editable 3D models.

By Jingyuan Chen, Sheng Jin, Haopeng Sun, Wentao Liu, Chen Qian

llms nlp multimodal benchmarks

arXiv AI

Jun 4

Provably Auditable and Safe LLM Agents from Human-Authored Ontologies

arXiv:2606. 04903v1 Announce Type: cross Abstract: We introduce the LLM agent architecture Agentic Redux, intended for use with nontrivial problem domains that require linear auditability.

By Aaron Sterling

llms agents

arXiv Machine Learning

Jun 4

Data Attribution in Large Language Models via Bidirectional Gradient Optimization

arXiv:2606. 04928v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed across diverse applications, raising critical questions for governance, accountability, and data provenance.

By Fr\'ed\'eric Berdoz, Luca A. Lanzend\"orfer, Kaan Bayraktar, Roger Wattenhofer

llms safety

arXiv Machine Learning

Jun 4

Stateful Visual Encoders for Vision-Language Models

arXiv:2606. 04433v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly used in multi-image, multi-turn agentic settings where decisions depend on visual changes.

By Zirui Wang, Junwei Yu, Adam Yala, David M. Chan, Joseph E. Gonzalez, Trevor Darrell

llms agents fine-tuning multimodal

arXiv AI

Jun 4

EvoPrompt: Guided Prompt Evolution for Vision-Language Models Adaptation

arXiv:2603. 09493v2 Announce Type: replace-cross Abstract: The adaptation of large-scale vision-language models (VLMs) to downstream tasks with limited labeled data remains a significant challenge.

By Enming Zhang, Jiayang Li, Yanlong Wang, Yanru Wu, Zhenyu Liu, Yang Li

llms rag fine-tuning multimodal benchmarks

arXiv AI

Jun 4

Toward Autonomous O-RAN: A Multi-Scale Agentic AI Framework for Real-Time Network Control and Management

arXiv:2602. 14117v2 Announce Type: replace-cross Abstract: Open Radio Access Networks (O-RAN) promise flexible 6G network access through disaggregated, software-driven components and open interfaces, but this programmability also increases operational complexity.

By Hojjat Navidan, Mohammad Cheraghinia, Jaron Fontaine, Mohamed Seif, Eli De Poorter, H. Vincent Poor, Ingrid Moerman, Adnan Shahid

llms agents

arXiv AI

Jun 4

DSL-Topic: Improving Topic Modeling by Distilling Soft Labelsfrom Language Models

arXiv:2602. 17907v2 Announce Type: replace-cross Abstract: Traditional neural topic models are typically optimized by reconstructing the document's Bag-of-Words (BoW) representations, overlooking contextual information and struggling with data sparsity.

By Raymond Li, Amirhossein Abaskohi, Chuyuan Li, Gabriel Murray, Giuseppe Carenini

llms efficiency

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