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

Structure-Preserving Learning Improves Geometry Generalization in Neural PDEs

arXiv:2602. 02788v2 Announce Type: replace-cross Abstract: We aim to develop physics foundation models for science and engineering that provide real-time solutions to Partial Differential Equations (PDEs) which preserve structure and accuracy under adaptation to unseen geometries.

By Benjamin D. Shaffer, Shawn Koohy, Brooks Kinch, M. Ani Hsieh, Nathaniel Trask

llms benchmarks safety

arXiv AI

Jun 10

Earth-OneVision: Extending Remote Sensing Multimodal Large Language Models to More Sensor Modalities and Tasks

arXiv:2606. 10819v1 Announce Type: cross Abstract: RS-MLLMs enable natural-language understanding and spatial reasoning over earth observation imagery.

By Miaoxin Cai, Guanqun Wang, Wei Zhang, Guangyao Zhou, Yin Zhuang, Tong Zhang, Hao Wang, He Chen, Jun Li

llms multimodal benchmarks safety

arXiv AI

Jun 10

Beyond APIs: Probing the Limits of MLLMs in Physical Tool Use

arXiv:2606. 10803v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) excel at utilizing digital APIs and increasingly serve as the "brain" of embodied AI, instructing robots to interact with the physical world.

By Zhixin Ma, Yutong Zhou, Yongqi Li, Chong-Wah Ngo, Wenjie Li

llms agents robotics multimodal benchmarks

arXiv AI

Jun 10

Reasoning or Memorization? Direction-Aware Diversity Exploration in LLM Reinforcement Learning

arXiv:2606. 10346v1 Announce Type: new Abstract: Reinforcement learning has become a key paradigm for eliciting reasoning abilities in large language models, where exploration is crucial for discovering effective solution trajectories.

By Jiangnan Xia, Yucheng Shi, Yu Yang, Kishan Panaganti, Zhenwen Liang, Ninghao Liu

llms reinforcement-learning benchmarks

arXiv AI

Jun 10

Improving Topic Modeling by Distilling Soft Labels from Language Models

arXiv:2602. 17907v3 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

arXiv Machine Learning

Jun 10

Pre-AF 13: An Interpretable Atrial Fibrillation Risk Score Mined from Discharge Reports

arXiv:2606. 10725v1 Announce Type: new Abstract: Background.

By Olga Shakhmatova, Dmitrii Kriukov, Daniil Larionov, Nikita Khromov, Iaroslav Bespalov, Alexander Zolotarev, Kirill Grishchenkov, Ekaterina Ivanova, Miron Kuznetsov, Ilya Sochenkov, Elizaveta Panchenko, Artem Shelmanov, Dmitry V. Dylov

llms nlp

arXiv AI

Jun 10

Evaluating Research-Level Math Proofs via Strict Step-Level Verification

arXiv:2606. 10799v1 Announce Type: new Abstract: Large Language Models (LLMs) struggle to rigorously verify complex mathematical proofs.

By Yifeng Sun

llms agents benchmarks safety

arXiv Machine Learning

Jun 10

Domain Adapted Large Language Models for Additive Manufacturing

arXiv:2603. 22017v2 Announce Type: replace Abstract: This work presents a collection of multi-modal domain adapted large language models built upon the instruction tuned variants of open weight models (Gemma 3, Qwen 3, Gemma 4) using a relatively small dataset of around 50 million tokens.

By Peter Pak, Amir Barati Farimani

llms fine-tuning multimodal benchmarks

arXiv AI

Jun 10

RAG over Thinking Traces Can Improve Reasoning Tasks

arXiv:2605. 03344v2 Announce Type: replace-cross Abstract: Retrieval-augmented generation (RAG) has proven effective for knowledge-intensive tasks, but is widely believed to offer limited benefit for reasoning-intensive problems such as math and code generation.

By Negar Arabzadeh, Wenjie Ma, Sewon Min, Matei Zaharia

llms rag benchmarks

arXiv AI

Jun 10

Baseline-Free Policy Optimization for Neural Combinatorial Optimization

arXiv:2606. 10321v1 Announce Type: cross Abstract: Neural combinatorial optimization (NCO) trains autoregressive policies to solve routing problems.

By Carlos S. Sep\'ulveda, Gonzalo A. Ruz

llms reinforcement-learning benchmarks safety

arXiv AI

Jun 10

Advancing the State-of-the-Art in Empirical Privacy Auditing

arXiv:2606. 10481v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning of large language models (LLMs) can exhibit problematic memorization of individual training examples.

By Nicole Mitchell, Galen Andrew, Arun Ganesh, Brendan McMahan, Peter Kairouz

llms fine-tuning benchmarks safety

arXiv AI

Jun 10

ERAlign: Energy-based Representation Alignment of GNNs and LLMs on Text-attributed Graphs

arXiv:2606. 10461v1 Announce Type: cross Abstract: Text-attributed Graphs (TAGs) incorporate textual node attributes with graph structures to describe rich relational semantics.

By Xianlin Zeng, Fan Xia, Xiangyu Chen

llms rag diffusion benchmarks safety

arXiv Machine Learning

Jun 10

Geometry-Aware Reinforcement Learning for 2D Irregular Nesting

arXiv:2606. 10611v1 Announce Type: new Abstract: Traditional heuristic solvers for the 2D irregular nesting problem share a fundamental limitation: they are blind to polygon geometry, relying on guided brute-force to navigate the continuous placement space with minimal geometrical guidance.

By Auguste Lehuger, Guillaume Henon-Just

llms agents reinforcement-learning benchmarks

arXiv Machine Learning

Jun 10

N-GRPO: Embedding-Level Neighbor Mixing for Enhanced Policy Optimization

arXiv:2606. 10768v1 Announce Type: new Abstract: The success of Large Language Models in mathematical reasoning relies heavily on the generation of diverse and valid solution paths during the rollout phase.

By Xukun Zhu, Hang Yu, Peng Di, Linchao Zhu

llms rag benchmarks

arXiv AI

Jun 10

GCA Framework: A GCC Countries-Grounded Dataset and Agentic Pipeline for Climate Decision Support

arXiv:2604. 12306v3 Announce Type: replace-cross Abstract: Climate decision-making in the GCC states increasingly demands systems that can translate heterogeneous scientific and policy evidence into actionable guidance, yet general-purpose large language models (LLMs) remain weak both in region-specific climate knowledge and grounded interaction with geospatial and forecasting tools.

By Muhammad Umer Sheikh, Khawar Shehzad, Salman Khan, Fahad Shahbaz Khan, Muhammad Haris Khan

llms agents fine-tuning multimodal benchmarks

arXiv AI

Jun 10

Conditional Vendi Score: Prompt-Aware Diversity Evaluation for Generative AI Models and LLMs

arXiv:2411. 02817v2 Announce Type: replace-cross Abstract: Generative models guided by text prompts are widely evaluated for fidelity and prompt alignment, yet their ability to produce outputs remains underexplored.

By Mohammad Jalali, Azim Ospanov, Amin Gohari, Farzan Farnia

llms diffusion safety

arXiv AI

Jun 10

Routing-Aware Expert Calibration for Machine Unlearning in Mixture-of-Experts Language Models

arXiv:2606. 10338v1 Announce Type: cross Abstract: Machine unlearning is increasingly important for large language models, yet unlearning in Mixture-of-Experts (MoE) architectures remains underexplored.

By Jingyi Xie, Yijun Lin, Yinjiang Xiong, Zhikun Zhang, Sai Li

llms

arXiv AI

Jun 10

Spatial-Omni: Spatial Audio Understanding Integration in Multimodal LLMs via FOA Encoding

arXiv:2606. 10738v1 Announce Type: cross Abstract: Recent multimodal large language models mainly process audio as monaural signals, thereby discarding the spatial cues contained in spatial audio for sound localization, spatial relation reasoning, and spatial scene understanding.

By Zhiyuan Zhu, Yixuan Chen, Yiwen Shao, Wenxiang Guo, Changhao Pan, Yu Zhang, Yuxiang Wang, Wei Liu, Houhua Zhang, Chengkuan Zeng, Wenbo Cheng, Yunxi Liu, Rui Yang, Steve Yves, Liefeng Bo, Zhou Zhao

llms nlp multimodal

arXiv Machine Learning

Jun 10

Parallel Causal Associative Fields: Gated Sparse Memory for Long-Context Language Modeling

arXiv:2606. 10435v1 Announce Type: new Abstract: Transformers achieve strong language modeling performance by providing direct token-to-token communication paths, but causal self-attention scales quadratically with context length.

By Muhammad Ahmed

llms

arXiv AI

Jun 10

Attention Expansion: Enhancing Keyphrase Extraction from Long Documents with Attention-Augmented Contextualized Embeddings

arXiv:2606. 10716v1 Announce Type: cross Abstract: Pre-trained language models (PLMs) have achieved strong performance in keyphrase extraction (KPE), largely due to their ability to generate rich contextualized representations.

By Roberto Mart\'inez-Cruz, Alvaro J. L\'opez-L\'opez, Jos\'e Portela

llms rag efficiency benchmarks

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