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 23

D3VL: Understanding Driving Scenes from 3D Time Series Data and Video with Language Models

arXiv:2607. 19528v1 Announce Type: cross Abstract: Recent advances in Multimodal Large Language Models (MLLMs) have triggered the development of end-to-end MLLMs for autonomous driving.

By Heesang Han, A. Lynn Abbott, Abhijit Sarkar

llms agents robotics efficiency multimodal safety

arXiv AI

Jul 23

Hybrid LLM-Guided Search for Quantum Reservoir Architecture Design

arXiv:2607. 19506v1 Announce Type: cross Abstract: Quantum reservoir computing (QRC) uses fixed quantum dynamics as a high-dimensional temporal feature map and trains only a lightweight classical readout.

By Krishna Bhatia, Gautami Sanjay Naik

llms agents benchmarks

arXiv AI

Jul 23

Economic Evaluations of Language Models

arXiv:2607. 19375v1 Announce Type: cross Abstract: Language models perform economically valuable work, yet they are not currently assessed for how well they perform every economically valuable task.

By Alexander Wan, Stephane Hatgis-Kessell, Tom\'as Aguirre, Percy Liang, Rishi Bommasani

llms benchmarks safety

arXiv Machine Learning

Jul 23

Multi-Mask Diffusion Language Models for Few-Step Generation

arXiv:2607. 19686v1 Announce Type: cross Abstract: Masked diffusion models (MDMs) are a promising family of language generators, but achieving high-quality few-step generation remains challenging.

By Sijin Chen, Yinuo Ren, Heyang Zhao, Ziheng Cheng, Quanquan Gu, Lexing Ying

llms diffusion efficiency

arXiv AI

Jul 23

From Trajectories to Prefixes: Reusing Teacher Trajectories via Replayed Prefixes and Online Continuation

arXiv:2607. 19395v1 Announce Type: cross Abstract: Small language models are attractive backbones for interactive agents, but direct distillation from strong teacher trajectories often turns rich multi-turn behavior into one-shot imitation targets.

By Yihan Wang, Zhong Guan, Haoran Sun, Jiale Huang, Likang Wu, Hongke Zhao

llms agents reinforcement-learning fine-tuning efficiency

arXiv AI

Jul 23

OpenEvoShield: Dual Non-Stationary Continual Defense for Open-World Multi-Agent System Attacks

arXiv:2607. 19351v1 Announce Type: new Abstract: LLM-based multi-agent systems (LLM-MAS) are increasingly deployed in safety-critical applications, where adversaries inject malicious instructions through inter-agent communication to propagate harmful behaviors.

By Litian Zhang, Chaozhuo Li, Yuting Zhang, Zejian Chen, Bingyu Yan, Qiwei Ye

llms agents benchmarks safety

arXiv AI

Jul 23

Reading and Steering Representations of Materials-Science Mechanisms in an Open-Weight Language Model

arXiv:2607. 20058v1 Announce Type: new Abstract: Large language models can answer scientific questions, yet a correct output does not reveal whether the model represents or uses the governing physics.

By Markus J. Buehler

llms benchmarks

arXiv Machine Learning

Jul 23

Multi-modal transformer for signal classification in nanopore blockade experiments

arXiv:2607. 20323v1 Announce Type: new Abstract: Nanopore devices have emerged as powerful tools for single-molecule sensing, with potential for rapid, portable diagnostics.

By Sandro Kuppel, Julian Ho{\ss}bach, Samuel Tovey, Christian Holm

llms multimodal benchmarks

arXiv Machine Learning

Jul 23

Evaluating and Mitigating Gender Bias in Pre-trained Embeddings for ML-based Recruitment

arXiv:2607. 20073v1 Announce Type: new Abstract: AI-based recruitment systems that rely on machine learning models trained on historical CV data, risk perpetuating and amplifying social biases.

By Farnaz Faramarzi Lighvan, Lynn Houthuys

llms rag safety

arXiv AI

Jul 23

Causal dictionary learning reveals and validates transcription-factor binding features in genomic language models

arXiv:2607. 19618v1 Announce Type: cross Abstract: Genomic language models achieve strong performance across regulatory-genomics tasks, yet what these models internally represent remains opaque, and the field lacks a principled procedure for verifying that an apparent ``concept'' inside a model is real rather than an artifact of sequence composition.

By Sarwan Ali

llms nlp safety

arXiv AI

Jul 23

Measuring LLM Trust Allocation Across Conflicting Software Artifacts

arXiv:2604. 03447v2 Announce Type: replace-cross Abstract: LLM-based software engineering assistants often reason over multiple artifacts, including code, documentation, signatures, and tests, even when those artifacts are incomplete or mutually inconsistent.

By Noshin Ulfat, Ahsanul Ameen Sabit, Soneya Binta Hossain

llms

arXiv AI

Jul 23

CrackedPDFs: A Controlled Benchmark for Hidden Prompt Injection in PDFs

arXiv:2607. 19396v1 Announce Type: new Abstract: Document-based LLM systems often flatten a PDF before guardrails inspect it.

By Pukaphol Thienpreecha ("Volk")

llms benchmarks safety

arXiv Machine Learning

Jul 23

The Blessing of Dimensionality: How Near-Orthogonality in High-Dimensional Spaces Explains Temporal Portability

arXiv:2607. 20301v1 Announce Type: new Abstract: Fine-tuning has been widely used to adapt large language models (LLMs) for domain-specific tasks.

By Abigail Woodring, Adrian Chan, Rana Muhammad Shahroz Khan, Sukwon Yun, Chau-Wai Wong, Tianlong Chen

llms fine-tuning

arXiv Machine Learning

Jul 23

Adaptive Multi-Expert Graph Transformer for Interpretable EEG-Based Diagnostics

arXiv:2607. 19429v1 Announce Type: new Abstract: Electroencephalographic (EEG) abnormalities arise from dynamic changes in neural synchrony across spatial and temporal scales, yet many computational approaches reduce these dynamics to static features.

By Maryam Rahimimovassagh, Md Elias Hossain, Ivan Garibay, Niloofar Yousefi

llms

arXiv Machine Learning

Jul 23

Scaling Laws for Hypernetwork-Based Knowledge Injection in Large Language Models

arXiv:2607. 19604v1 Announce Type: cross Abstract: Injecting factual knowledge into large language models (LLMs) reliably and at scale remains an open challenge.

By Nischay Dhankhar, Dos Baha, Abulhair Saparov

llms fine-tuning

arXiv Machine Learning

Jul 23

On the Computational Complexity of Structural Generalization

arXiv:2607. 19573v1 Announce Type: cross Abstract: Structural generalization has been measured repeatedly by several benchmarks, yet it has never been formally defined.

By Zichao Wei

llms benchmarks

arXiv Machine Learning

Jul 23

Reward-Aware Population Scaling of Evolutionary Strategies in LLM Fine-Tuning

arXiv:2607. 19408v1 Announce Type: new Abstract: Using Evolutionary Strategies (ES) for fine-tuning large language models is attractive because it is memory-efficient, parallel, and compatible with black-box or discrete rewards.

By Sung Cho, Gyubin Han

llms fine-tuning

arXiv AI

Jul 23

Internal Knowledge Without External Expression: Probing the Generalization Boundary of a Classical Chinese Language Model

arXiv:2604. 14180v2 Announce Type: replace-cross Abstract: We train a 318M-parameter Transformer language model from scratch on a curated corpus of 1.

By Jiuting Chen, Yuan Lian, Hao Wu, Tianqi Huang, Hiroshi Sasaki, Makoto Kouno, Jongil Choi

llms reinforcement-learning

arXiv AI

Jul 23

ArenaRL: Scaling RL for Open-Ended Agents via Tournament-based Relative Ranking

arXiv:2601. 06487v3 Announce Type: replace-cross Abstract: Reinforcement learning has substantially improved the performance of LLM agents on tasks with verifiable outcomes, but it still struggles on open-ended agent tasks with vast solution spaces (e.

By Qiang Zhang, Boli Chen, Fanrui Zhang, Ruixue Ding, Shihang Wang, Qiuchen Wang, Yinfeng Huang, Haonan Zhang, Rongxiang Zhu, Pengyong Wang, Ailin Ren, Xin Li, Pengjun Xie, Jiawei Liu, Ning Guo, Jingren Zhou, Zheng-Jun Zha

llms agents reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jul 23

HyGRL: Adaptive Hybrid Graph Reasoning for Multi-Entity Questions

arXiv:2607. 19398v1 Announce Type: new Abstract: Multi-entity compositional questions pose significant challenges to existing retrieval-augmented language models.

By Junyi Wang

llms rag reinforcement-learning efficiency benchmarks

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